Dr. Matthew T. Freedman Profile

Dr. Matthew T. Freedman

Adjunct Associate Professor

SPIE Involvement:

Author

Publications (114)

Proceedings Article | 19 March 2013 Paper

Image reconstruction of arc cone-beam CT with reprojection: a preliminary study

Shih-Chung Lo, Matthew Freedman

Proceedings Volume 8668, 866831 (2013) https://doi.org/10.1117/12.2008156

KEYWORDS: X-rays, Computed tomography, Head, X-ray computed tomography, Glasses, X-ray imaging, Image restoration, CT reconstruction, Reconstruction algorithms, 3D image reconstruction

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Proceedings Article | 23 February 2012 Paper

Changes of nodule detection after radiologists read bone opacity suppressed chest radiography

Shih-Chung Lo, Matthew Freedman

Proceedings Volume 8315, 83152S (2012) https://doi.org/10.1117/12.911651

KEYWORDS: Bone, Opacity, Cancer, Lung cancer, Chest imaging, Lung, Chest, Radiography, Colorectal cancer, Ovarian cancer

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Proceedings Article | 9 March 2011 Paper

BI-RADS guided mammographic mass retrieval

Yimo Tao, Shih-Chung Lo, Lubomir Hadjiski, Heang-Ping Chan, Matthew Freedman

Proceedings Volume 7963, 79632H (2011) https://doi.org/10.1117/12.878274

KEYWORDS: Image retrieval, Feature extraction, Mammography, Visualization, Computer aided diagnosis and therapy, Computing systems, Image segmentation, Content based image retrieval, Biological research, Biopsy

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Proceedings Article | 9 March 2010 Paper

Joint segmentation and spiculation detection for ill-defined and spiculated mammographic masses

Yimo Tao, Shih-Chung Lo, Matthew Freedman, Jianhua Xuan

Proceedings Volume 7624, 762407 (2010) https://doi.org/10.1117/12.844045

KEYWORDS: Image segmentation, Phase modulation, Image processing algorithms and systems, Tissues, Detection and tracking algorithms, Image enhancement, Ridge detection, Expectation maximization algorithms, Breast, Binary data

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Proceedings Article | 21 March 2009 Paper

PE-CMOS-based C-mode ultrasound: signal acquisition and time gating

Shih-Chung B. Lo, Chu-Chuan Liu, Matthew Freedman, Seong-Ki Mun, John Kula, Marvin Lasser, Bob Lasser, Yue Joseph Wang

Proceedings Volume 7265, 726508 (2009) https://doi.org/10.1117/12.811800

KEYWORDS: Ultrasonography, Bone, Picosecond phenomena, Sensors, Prototyping, Skin, Image sensors, Signal detection, Systems modeling, Image acquisition

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Proceedings Article | 17 March 2008 Paper

Automatic categorization of mammographic masses using BI-RADS as a guidance

Yimo Tao, Shih-Chung Lo, Matthew Freedman, Erini Makariou, Jianhua Xuan

Proceedings Volume 6915, 691526 (2008) https://doi.org/10.1117/12.772808

KEYWORDS: Image retrieval, Feature extraction, Image filtering, Gaussian filters, Mammography, Breast, Associative arrays, Electronic filtering, Cancer, Diagnostics

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Proceedings Article | 10 March 2008 Paper

Projection-reflection ultrasound images using PE-CMOS sensor: a preliminary bone fracture study

Shih-Chung B. Lo, Chu-Chuan Liu, Matthew Freedman, Seong-Ki Mun, John Kula, Marvin Lasser, Bob Lasser, Yue Joseph Wang

Proceedings Volume 6920, 692005 (2008) https://doi.org/10.1117/12.772847

KEYWORDS: Bone, Ultrasonography, Prototyping, Sensors, Speckle, Skin, Tissues, Transducers, Signal detection, Acoustics

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Proceedings Article | 31 March 2007 Paper

A preliminary study of content-based mammographic masses retrieval

Yimo Tao, Shih-Chung Lo, Matthew Freedman, Jianhua Xuan

Proceedings Volume 6514, 65141Z (2007) https://doi.org/10.1117/12.711528

KEYWORDS: Feature extraction, Databases, Data modeling, Image retrieval, Computer aided diagnosis and therapy, Computing systems, Mammography, Visualization, Medical imaging, Fourier transforms

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Proceedings Article | 12 March 2007 Paper

Quantitative ultrasound images generated by a PE-CMOS sensor array: scatter modeling and image restoration

Chu-Chuan Liu, Shih-Chung Ben Lo, Matthew Freedman, Marvin Lasser, Bob Lasser, John Kula, Yue Joseph Wang

Proceedings Volume 6513, 65130Q (2007) https://doi.org/10.1117/12.712224

KEYWORDS: Ultrasonography, Scattering, Sensors, Laser scattering, Collimation, Image processing, Prototyping, Image restoration, Signal attenuation, Acoustics

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Proceedings Article | 5 March 2007 Paper

Characterization of pulmonary nodules on computer tomography (CT) scans: the effect of additive white noise on features selection and classification performance

Teresa Osicka, Matthew Freedman, Farid Ahmed

Proceedings Volume 6512, 651245 (2007) https://doi.org/10.1117/12.709576

KEYWORDS: Wavelets, Lung, Image classification, Computed tomography, Discrete wavelet transforms, Image segmentation, Feature selection, Lung cancer, Feature extraction, Cancer

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Proceedings Article | 17 March 2006 Paper

Mammographic CADx system using an image library with an intelligent agent: a pattern matching approach

Shih-Chung Lo, Matthew Freedman, Lisa Kinnard, Erini Makariou

Proceedings Volume 6144, 61445O (2006) https://doi.org/10.1117/12.654667

KEYWORDS: Image segmentation, Computer aided diagnosis and therapy, Mammography, Breast cancer, Image retrieval, Diagnostics, Breast, Fuzzy logic, Medical imaging, Computing systems

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Proceedings Article | 17 March 2006 Paper

Characterization of pulmonary nodules features on computer tomography (CT) scans using wavelet coefficients and heat maps

Teresa Osicka, Matthew Freedman, Farid Ahmed

Proceedings Volume 6144, 614463 (2006) https://doi.org/10.1117/12.653198

KEYWORDS: Discrete wavelet transforms, Wavelets, Feature extraction, Computed tomography, Lung, Visualization, Feature selection, Cancer, Lung cancer, Visual analytics

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Proceedings Article | 16 March 2006 Paper

Investigation of foreign objects in soft-tissue using a PE-CMOS ultrasound system: a preliminary comparative study

Chu-Chuan Liu, Shih-Chung Ben Lo, Matthew Freedman, Marvin Lasser, Bob Lasser, John Kula, Yue Joseph Wang, Tsung-I Hsieh, Anita Sarcone

Proceedings Volume 6147, 61470K (2006) https://doi.org/10.1117/12.654105

KEYWORDS: Ultrasonography, X-ray imaging, X-rays, Imaging systems, Aluminum, Tissues, Chromium, Glasses, Computing systems, Sensors

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SPIE Journal Paper | 1 September 2005 Open Access

Reflectance confocal microscopy for characterization of mammary ductal structures and development of neoplasia in genetically engineered mouse models of breast cancer

Angela Parrish, Ewa Halama, Maddalena Tilli, Matthew Freedman, Priscilla Furth

JBO, Vol. 10, Issue 05, 051602, (September 2005) https://doi.org/10.1117/12.10.1117/1.2065827

KEYWORDS: Confocal microscopy, Mammary gland, Reflectivity, Tissues, Tissue optics, Microscopy, Cancer, Visualization, Mouse models, Breast cancer

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Proceedings Article | 29 April 2005 Paper

Mass segmentation of dense breasts on digitized mammograms: analysis of a probability-based function

Lisa Kinnard, Shih-Chung Lo, Eva Duckett, Erini Makariou, Teresa Osicka, Matthew Freedman, Mohamed Chouikha

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.594842

KEYWORDS: Image segmentation, Breast, Mammography, Tissues, Radiology, Statistical analysis, Cancer, Diagnostics, Computer aided diagnosis and therapy, Visualization

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Proceedings Article | 12 April 2005 Paper

Transmission and reflective ultrasound images using PE-CMOS sensor array

Shih-Chung Lo, Chu Chuan Liu, Matthew Freedman, John Kula, Bob Lasser, Marvin Lasser, Yue Wang

Proceedings Volume 5750, (2005) https://doi.org/10.1117/12.595615

KEYWORDS: Ultrasonography, Reflectivity, Sensors, Signal attenuation, Transducers, Tissues, Bone, Modulation transfer functions, Signal detection, Imaging systems

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Proceedings Article | 6 April 2005 Paper

Computer aided detection of lung cancer in the absence of the cancer on chest radiographs: effect of the computer-aided detection on radiologists’ performance on cancer-free cases

Teresa Osicka, Matthew Freedman, Shih-Chung Lo, Fleming Lure, Xin-Wei Xu, Jesse Lin, Ron Zhang, Hui Zhao

Proceedings Volume 5749, (2005) https://doi.org/10.1117/12.594568

KEYWORDS: Computer aided diagnosis and therapy, Cancer, Lung cancer, Chest imaging, Clinical trials, CAD systems, Switches, Statistical analysis, Radiography

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Using data from a clinical trial of a commercial CAD system for lung cancer detection we separately analyzed the location, if any, selected on each film by 15 radiologists as they interpreted chest radiographs, 160 of which did not contain cancers. On the cancer-free cases, the radiologists showed statistically significant difference in decisions while using the CAD (p-value 0.002). Average specificity without computer assistance was 78%, and with computer assistance 73%. In a clinical trial with CAD for lung cancer detection there are multiple machine false positives. On chest radiographs of older current or former smokers, there are many scars that can appear like cancer to the interpreting radiologists. We are reporting on the radiologists' false positives and on the effect of machine false positive detections on observer performance on cancer-free cases. The only difference between radiologists occurred when they changed their initial true negative decision to false positive (p-value less than 0.0001), average confidence level increased, on the scale from 0.0 to 100.0, from 16.9 (high confidence of non-cancer) to 53.5 (moderate confidence cancer was present). We are reporting on the consistency of misinterpretation by multiple radiologists when they interpret cancer-free radiographs of smokers in the absence of CAD prompts. When multiple radiologists selected the same false positive location, there was usually a definite abnormality that triggered this response. The CAD identifies areas that are of sufficient concern for cancer that the radiologists will switch from a correct decision of no cancer to mark a false positive, previously overlooked, but suspicious appearing cancer-free area; one that has often been marked by another radiologist without the use of the CAD prompt. This work has implications on what should be accepted as ground truth in ROC studies: One might ask, "What a false positive response means?" when the finding, clinically, looks like cancer-it just isn’t cancer, based on long-term follow-up or histology.

Proceedings Article | 12 May 2004 Paper

Enhancement of lung nodule detection in temporal thoracic CT

Shih-Chung Lo, J. Lin, Li-Yueh Hsu, Yuan Ming Lure, Matthew Freedman, Ruiping Li, Hui Zhao

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.537621

KEYWORDS: Computed tomography, Lung, Image registration, 3D image processing, Image segmentation, Lung cancer, Cancer, 3D vision, Chest, Signal to noise ratio

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Proceedings Article | 4 May 2004 Paper

Computer-aided detection of lung cancer on chest radiographs: effect of machine CAD true positive/false negative detections on radiologists' confidence level

Matthew Freedman, Teresa Osicka, Shih-Chung Benedict Lo, Fleming Lure, Xin-Wei Xu, Jesse Lin, Hui Zhao, Ron Zhang

Proceedings Volume 5372, (2004) https://doi.org/10.1117/12.536157

KEYWORDS: Computer aided diagnosis and therapy, Cancer, Lung cancer, Chest imaging, Medical imaging, Clinical trials, CAD systems

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Proceedings Article | 28 April 2004 Paper

Projection ultrasound and ultrasound CT using a PE-CMOS sensor: a preliminary study

Chu Chuan Liu, Shih-Chung Lo, Matthew Freedman, David Rich, John Kula, Bob Lasser, Marvin Lasser, JianChao Zeng, Doug Ro

Proceedings Volume 5373, (2004) https://doi.org/10.1117/12.537672

KEYWORDS: Ultrasonography, Signal attenuation, Computed tomography, Sensors, Computing systems, Breast, Scattering, 3D image processing, Acoustics, Tissues

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Proceedings Article | 5 June 2003 Paper

Phase contrast digital mammography using molybdenum x-ray: clinical implications in detectability improvement

Matthew Freedman, Shih-Chung Lo, Chika Honda, Erini Makariou, Gale Sisney, Edward Pien, Hiromu Ohara, Akira Ishisaka, Fumio Shimada

Proceedings Volume 5030, (2003) https://doi.org/10.1117/12.480366

KEYWORDS: Phase imaging, X-rays, Mammography, Phase contrast, Digital mammography, X-ray imaging, Printing, Spatial resolution, Chromium, Imaging systems

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Proceedings Article | 22 May 2003 Paper

Computer-aided detection of lung cancer on chest radiographs: differences in the interpretation time of radiologists showing versus not showing improvement with CAD

Teresa Osicka, Matthew Freedman, Shih-Chung Lo, Fleming Lure, Xin-Wei Xu, Jesse Lin, Hui Zhao, Ron Zhang

Proceedings Volume 5034, (2003) https://doi.org/10.1117/12.480347

KEYWORDS: Cancer, Computer aided diagnosis and therapy, Lung cancer, Chest imaging, CAD systems, Clinical trials, Medical imaging, Diagnostics, Classification systems

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Proceedings Article | 15 May 2003 Paper

Classification of lung nodules in diagnostic CT: an approach based on 3D vascular features, nodule density distribution, and shape features

Shih-Chung Lo, Li-Yueh Hsu, Matthew Freedman, Yuan Ming Lure, Hui Zhao

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.481878

KEYWORDS: Lung, Computed tomography, Image segmentation, Diagnostics, Lung cancer, Image processing, Neural networks, 3D modeling, Chest, Tumors

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Proceedings Article | 15 May 2003 Paper

Separation of malignant and benign masses using image and segmentation features

Lisa Kinnard, Shih-Chung Lo, Paul Wang, Matthew Freedman, Mohamed Chouikha

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.481889

KEYWORDS: Image segmentation, Neural networks, Feature extraction, Image classification, Tumors, Image processing, Mammography, Diagnostics, Databases, Radiology

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Proceedings Article | 15 May 2003 Paper

Finite-element deformable sheet-curve models for registration of breast MR images

Jianhua Xuan, Matthew Freedman, Yue Wang

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.480859

KEYWORDS: Breast, Magnetic resonance imaging, Image registration, Tissues, Motion models, Systems modeling, Chemical elements, Breast cancer, Finite element methods, Dynamical systems

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Proceedings Article | 9 May 2002 Paper

Separation of malignant and benign masses using maximum-likelihood modeling and neural networks

Lisa Kinnard, Shih-Chung Lo, Paul Wang, Matthew Freedman, Mohammed Chouikha

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467216

KEYWORDS: Tumors, Image segmentation, Neural networks, Breast cancer, Tumor growth modeling, Radiology, Convolution, Mammography, Diagnostics, Image filtering

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Proceedings Article | 9 May 2002 Paper

Enhanced lung cancer detection in temporal subtraction chest radiography using directional edge filtering techniques

Hui Zhao, Shih-Chung Lo, Matthew Freedman, Yue Wang

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467212

KEYWORDS: Image filtering, Lung, Chest, Image enhancement, Lung cancer, Image processing, Radiography, Chest imaging, Image registration, CAD systems

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Proceedings Article | 9 May 2002 Paper

Mixture of principal axes registration: a neural computation approach

Rujirutana Srikanchana, Jianhua Xuan, Kun Huang, Matthew Freedman, Yue Wang

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467045

KEYWORDS: Image registration, Expectation maximization algorithms, Image segmentation, Image enhancement, Tissues, Neural networks, Breast, Image restoration, Magnetic resonance imaging, Medical imaging

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Proceedings Article | 9 May 2002 Paper

Computer-aided detection of lung cancer on chest radiographs: effect of machine CAD false-positive locations on radiologists' behavior

Matthew Freedman, Shih-Chung Lo, Teresa Osicka, Fleming Lure, Xin-Wei Xu, Jesse Lin, Hui Zhao, Ron Zhang

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467092

KEYWORDS: Computer aided diagnosis and therapy, Lung cancer, Chest imaging, Cancer, CAD systems, Clinical trials, Computing systems, Biopsy, Medical imaging

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Proceedings Article | 9 May 2002 Paper

MR-image-based tissue analysis and its clinical applications

Kun Huang, Jianhua Xuan, Jozsef Varga, Matthew Freedman, Zsolt Szabo, Dan Hayes, Vered Stearns, Yue Wang

Proceedings Volume 4684, (2002) https://doi.org/10.1117/12.467184

KEYWORDS: Tissues, Image segmentation, Magnetic resonance imaging, Positron emission tomography, Image analysis, Breast, Expectation maximization algorithms, Brain, Neuroimaging, 3D modeling

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Proceedings Article | 3 May 2002 Paper

Tactile imaging of palpable breast cancer

Rujirutana Srikanchana, Yue Wang, Matthew Freedman, Charles Nguyen

Proceedings Volume 4682, (2002) https://doi.org/10.1117/12.465630

KEYWORDS: Breast, Sensors, Cameras, Breast cancer, Prototyping, 3D image processing, Stereoscopic cameras, Mammography, Imaging systems, Tissues

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Proceedings Article | 12 April 2002 Paper

Independent versus sequential reading in ROC studies of computer-assist modalities

Sergey Beiden, Robert Wagner, Kunio Doi, Robert Nishikawa, Matthew Freedman, Shih-Chung Lo, Xin-Wei Xu

Proceedings Volume 4686, (2002) https://doi.org/10.1117/12.462678

KEYWORDS: Statistical analysis, Computing systems, Chest imaging, Receivers, Computer aided diagnosis and therapy, Diagnostics, Computer aided design, Medical imaging, Data modeling

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Proceedings Article | 3 July 2001 Paper

Automatic temporal subtraction of chest radiographs and its enhancement for lung cancers

Hui Zhao, Shih-Chung Lo, Matthew Freedman, Seong Mun

Proceedings Volume 4322, (2001) https://doi.org/10.1117/12.431078

KEYWORDS: Signal to noise ratio, Cancer, Lung cancer, Image registration, Chest imaging, Chest, Image segmentation, Lung, Radiography, Edge detection

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Proceedings Article | 3 July 2001 Paper

Integer wavelet compression guided by a computer-aided detection system in mammography

Shih-Chung Lo, Erini Makariou, Andrzej Delegacz, Heang-Ping Chan, Donald Dorfman, Matthew Freedman, Kevin Berbaum

Proceedings Volume 4322, (2001) https://doi.org/10.1117/12.431138

KEYWORDS: Mammography, Image compression, Image quality, Wavelets, Computing systems, Digital mammography, Computer aided diagnosis and therapy, Breast, Image enhancement

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Proceedings Article | 26 June 2001 Paper

Methods for identifying changes in radiologists' behavioral operating point of sensitivity-specificity trade-offs within an ROC study of the use of computer-aided detection of lung cancer

Matthew Freedman, Teresa Osicka, Shih-Chung Lo, Fleming Lure, Xin-Wei Xu, Jesse Lin, Ron Zhang

Proceedings Volume 4324, (2001) https://doi.org/10.1117/12.431187

KEYWORDS: Cancer, Computer aided diagnosis and therapy, Lung cancer, Mammography, Digital mammography, Statistical analysis, Biopsy, Diagnostics, Lead

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Proceedings Article | 30 May 2001 Paper

C-scan transmission ultrasound based on a hybrid microelectronic sensor array and its physical performance

Shih-Chung Lo, David Rich, Marvin Lasser, John Kula, Hui Zhao, Bob Lasser, Matthew Freedman

Proceedings Volume 4325, (2001) https://doi.org/10.1117/12.428185

KEYWORDS: Ultrasonography, Optical spheres, Breast, Tumors, Biopsy, Sensors, Acoustics, Tissues, Microelectronics, X-rays

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Proceedings Article | 28 May 2001 Paper

Feature extraction, analysis, and 3D visualization of local lung regions in volumetric CT images

Andrzej Delegacz, Shih-Chung Lo, Matthew Freedman, Seong Mun

Proceedings Volume 4319, (2001) https://doi.org/10.1117/12.428121

KEYWORDS: Lung, Feature extraction, Computed tomography, Visualization, Lung cancer, 3D displays, Image segmentation, Computing systems, 3D visualizations, Volume rendering

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Proceedings Article | 28 May 2001 Paper

Computer-aided detection of lung cancer on chest radiographs: algorithm performance vs. radiologists' performance by size of cancer

Matthew Freedman, Shih-Chung Lo, Fleming Lure, Xin-Wei Xu, Jesse Lin, Hui Zhao, Teresa Osicka, Ron Zhang

Proceedings Volume 4319, (2001) https://doi.org/10.1117/12.428048

KEYWORDS: Cancer, Computer aided diagnosis and therapy, Lung cancer, Chest imaging, Remote sensing, Computing systems, Detection and tracking algorithms, System integration, CAD systems

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Proceedings Article | 21 May 2001 Paper

Ultrasound images of implanted tumors in nude mice using Sono-CT correlated with MRI appearance

Matthew Freedman, Anita Sarcone, Kathleen Pirollo, Chin-Shoou Lin, Esther Chang

Proceedings Volume 4321, (2001) https://doi.org/10.1117/12.428133

KEYWORDS: Tumors, Ultrasonography, Magnetic resonance imaging, Doppler effect, In vivo imaging, Cancer, Tissues, Computed tomography, Diffusion, Blood

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Proceedings Article | 6 June 2000 Paper

Computer-based decision support system: visual mapping of featured database in computer-aided diagnosis

Yue Wang, Zuyi Wang, Lan Luo, Shih-Chung Lo, Matthew Freedman

Proceedings Volume 3979, (2000) https://doi.org/10.1117/12.387618

KEYWORDS: Data modeling, Visualization, Expectation maximization algorithms, Databases, Visual process modeling, Computer aided diagnosis and therapy, Principal component analysis, Feature extraction, Mammography, CAD systems

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Proceedings Article | 6 June 2000 Paper

Patient site model supported change detection

Kelvin Woods, Maxine McClain, Yue Wang, Matthew Freedman

Proceedings Volume 3979, (2000) https://doi.org/10.1117/12.387614

KEYWORDS: Image registration, Mammography, Image processing, Breast, Detection and tracking algorithms, Image segmentation, Tissues, Expectation maximization algorithms, Image processing algorithms and systems, Transform theory

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Proceedings Article | 5 May 2000 Paper

I-SPINE: a software package for advances in image-guided and minimally invasive spine procedures

Jae Jeong Choi, Kevin Cleary, Jianchao Zeng, Kevin Gary, Matthew Freedman, Vance Watson, David Lindisch, Seong Mun

Proceedings Volume 3905, (2000) https://doi.org/10.1117/12.384879

KEYWORDS: Visualization, Spine, Image visualization, Volume rendering, Computed tomography, 3D visualizations, Cements, Image segmentation, Bone, Medical imaging

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Proceedings Article | 18 April 2000 Paper

Statistical volumetric model for characterization and visualization of prostate cancer

Jianping Lu, Rujirutana Srikanchana, Maxine McClain, Yue Wang, Jian Hua Xuan, Isabell Sesterhenn, Matthew Freedman, Seong Mun

Proceedings Volume 3976, (2000) https://doi.org/10.1117/12.383036

KEYWORDS: 3D modeling, Tumors, Expectation maximization algorithms, Biopsy, Prostate, Prostate cancer, Statistical modeling, Tumor growth modeling, Cancer, Visualization

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Proceedings Article | 18 April 2000 Paper

Three-dimensional visualization system as an aid for lung cancer detection

Andrzej Delegacz, Shih-Chung Lo, Huchen Xie, Matthew Freedman, Jae Jeong Choi

Proceedings Volume 3976, (2000) https://doi.org/10.1117/12.383066

KEYWORDS: Lung, Image segmentation, Visualization, Computed tomography, 3D image processing, Volume rendering, Lung cancer, Image filtering, 3D visualizations, Opacity

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The purpose of this work was to create a 3D visualization system to aid physicians in observing abnormalities of the human lungs. A series of 20-30 helical CT lung slice images obtained from the lung cancer screening protocol as well as a series of 100-150 diagnostic helical CT lung slice images were used as an input. We designed a segmentation filter to enhance the lung boundaries and filter out small and medium bronchi from the original images. The pairs of original and filtered images were further processed with the contour extraction method to segment out only the lung field for further study. In the next step the segmented lung images containing the small bronchi and lung textures were used to generate the volumetric dataset input for the 3D visualization system. Additional processing for the extracted contour was used to smooth the 3D lung contour in the lung boundaries. The computer program developed allows, among others, viewing of the 3D lung object from various angles, zooming in and out as well as selecting the regions of interest for further viewing. The density and gradient opacity tables are defined and used to manipulate the displayed contents of 3D rendered images. Thus, an effective 'see-through' technique is applied to the 3D lung object for better visual access to the internal lung structures like bronchi and possible cancer masses. These and other features of the resulting 3D lung visualization system give the user a powerful tool to observe and investigate the patient's lungs. The filter designed for this study is a completely new solution that greatly facilitates the boundary detection. The developed 3D visualization system dedicated from chest CT provides the user a new way to explore effective diagnosis of potential lung abnormalities and cancer. In the authors' opinion, the developed system can be successfully used to view and analyze patient's lung CT images in a new powerful approach in both diagnosis and surgery-planning applications. Additionally, we see the possibility of using the system for teaching anatomy as well as pathology of the human lung.

Proceedings Article | 26 May 1999 Paper

Hierarchical minimax entropy modeling and probabilistic principal component visualization for data exploration

Yue Wang, Lan Luo, Haifeng Li, Matthew Freedman

Proceedings Volume 3658, (1999) https://doi.org/10.1117/12.349421

KEYWORDS: Data modeling, Visualization, Databases, Visual process modeling, Expectation maximization algorithms, Mammography, Neural networks, CAD systems, Principal component analysis, Associative arrays

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As a step toward understanding the complex information from data and relationships, structural and discriminative knowledge reveals insight that may prove useful in data interpretation and exploration. This paper reports the development of an automated and intelligent procedure for generating the hierarchy of minimize entropy models and principal component visualization spaces for improved data explanation. The proposed hierarchical mimimax entropy modeling and probabilistic principal component projection are both statistically principles and visually effective at revealing all of the interesting aspects of the data set. The methods involve multiple use of standard finite normal mixture models and probabilistic principal component projections. The strategy is that the top-level model and projection should explain the entire data set, best revealing the presence of clusters and relationships, while lower-level models and projections should display internal structure within individual clusters, such as the presence of subclusters and attribute trends, which might not be apparent in the higher-level models and projections. With may complementary mixture models and visualization projections, each level will be relatively simple while the complete hierarchy maintains overall flexibility yet still conveys considerable structural information. In particular, a model identification procedure is developed to select the optimal number and kernel shapes of local clusters from a class of data, resulting in a standard finite normal mixtures with minimum conditional bias and variance, and a probabilistic principal component neural network is advanced to generate optimal projections, leading to a hierarchical visualization algorithm allowing the complete data set to be analyzed at the top level, with best separated subclusters of data points analyzed at deeper levels. Hierarchial probabilistic principal component visualization involves (1) evaluation of posterior probabilities for mixture data set, (2) estimation of multiple principal component axes from probabilistic data set, and (3) generation of a compete hierarchy of visual projections. With a soft clustering of the data set t_i via the EM algorithm, data points will effectively belong to more than one cluster at any given level with posterior probabilities denoted by z_ik. Thus, the effective input values are z_ikt_i for an independent visualization space k in the hierarchy. Further projections can again be performed using the effective input values z_ikz_j\kt_i for the visualization subspace j. The complete visual explanation hierarchy is generated by performing principal projection and model identification in two iterative steps using information theoretic criteria, EM algorithm, and probabilistic principal component analysis.

Proceedings Article | 21 May 1999 Paper

Feature analysis of lung nodules using sector geometry and multiple circular path neural network

Shih-Chung Lo, Matthew Freedman, Jyh-Shyan Lin, Xin-Wei Xu, Andrzej Delegacz

Proceedings Volume 3661, (1999) https://doi.org/10.1117/12.348564

KEYWORDS: Neural networks, Lung, Chest imaging, Image filtering, Pattern recognition, Feature extraction, Lung cancer, Cancer, Computer aided diagnosis and therapy, Radiography

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In this study, each chest radiograph was processed by a three- dimensional Gaussian-like matched filter. Edge tracking and region growing techniques were applied to the filtered image to segment all possible nodules. The area and its boundary were then divided into 36 sectors (i.e., 10 degrees per sector) using 36 equi-angle dividers radiated from the center. For each suspicious area, we computed radius, average gradient within the sector, average gradient near the boundary, and contrast were computed features within each 10 degree sector. A total of 144 computed features for one suspicious area were used as input values for a newly designed three layer neural network to perform pattern recognition studies. The neural network system was constructed to emphasize the correlation information associated with the features. In this part of the research, several circular path neural network connections between the input and the first hidden layers were linked. These included (1) self correlation networking and (2) neighborhood correlation networking. The networks for self correlation and neighborhood correlation were designed to extract the common factors within the sector and between sectors, respectively. In this study, neighborhood correlation across sectors of 20 degrees, 30 degrees, 40 degrees, and 50 degrees were used. We have tested this approach on the JPST chest radiograph database consisting of 154 chest radiographs using the grouped jack-knife method. The performance in detecting medium-sized nodules was 75% in sensitivity at 5.9 false-positives per image. The performance remained the same for large nodules (with 75% sensitivity at 5.6 false-positive per image). This work presents a new and effective way in analyzing tumor objects. Instead of lumping global features for each object and analyzing them by a conventional classifier, the new method computes features in sectors and analyzes them using a fan-oriented neural network. We also found that the MCPNN technique performs slightly more effective in detecting larger nodules than smaller nodules.

SPIE Journal Paper | 1 January 1999

Lung contour detection in chest radiographs using 1-D convolution neural networks

Osamu Tsujii, Matthew Freedman, Seong Mun

JEI, Vol. 8, Issue 01, (January 1999) https://doi.org/10.1117/12.10.1117/1.482683

KEYWORDS: Neural networks, Lung, Image processing, Chest imaging, Convolution, Neurons, Image segmentation, Edge detection, Heart, Linear filtering

Proceedings Article | 26 June 1998 Paper

Optimizing prostate needle biopsy through 3D simulation

Jianchao Zeng, Charles Kaplan, Jian Hua Xuan, Isabell Sesterhenn, John Lynch, Matthew Freedman, Seong Mun

Proceedings Volume 3335, (1998) https://doi.org/10.1117/12.312525

KEYWORDS: Biopsy, Prostate, 3D modeling, Tumors, Computer simulations, Cancer, Statistical modeling, Ultrasonography, Prostate cancer, Visualization

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Prostate needle biopsy is used for the detection of prostate cancer. The protocol of needle biopsy that is currently routinely used in the clinical environment is the systematic sextant technique, which defines six symmetric locations on the prostate surface for needle insertion. However, this protocol has been developed based on the long-term observation and experience of urologists. Little quantitative or scientific evidence supports the use of this biopsy technique. In this research, we aim at developing a statistically optimized new prostate needle biopsy protocol to improve the quality of diagnosis of prostate cancer. This new protocol will be developed by using a three-dimensional (3-D) computer- based probability map of prostate cancer. For this purpose, we have developed a computer-based 3-D visualization and simulation system with prostate models constructed from the digitized prostate specimens, in which the process of prostate needle biopsy can be simulated automatically by the computer. In this paper, we first develop an interactive biopsy simulation mode in the system, and evaluate the performance of the automatic biopsy simulation with the sextant biopsy protocol by comparing the results by the urologist using the interactive simulation mode with respect to 53 prostate models. This is required to confirm that the automatic simulation is accurate and reliable enough for the simulation with respect to a large number of prostate models. Then we compare the performance of the existing protocols using the automatic biopsy simulation system with respect to 107 prostate models, which will statistically identify if one protocol is better than another. Since the estimation of tumor volume is extremely important in determining the significance of a tumor and in deciding appropriate treatment methods, we further investigate correlation between the tumor volume and the positive core volume with 89 prostate models. This is done in order to develop a method to estimate the tumor volume from the corresponding positive core volumes. Finally, we propose an algorithm for developing a statistically optimized prostate needle biopsy protocol. Preliminary experimental results are also presented.

Proceedings Article | 26 June 1998 Paper

Multimodality medical image fusion: probabilistic quantification, segmentation, and registration

Yue Wang, Matthew Freedman, Jian Hua Xuan, Qinfen Zheng, Seong Mun

Proceedings Volume 3335, (1998) https://doi.org/10.1117/12.312497

KEYWORDS: Image segmentation, Image fusion, Magnetic resonance imaging, Image registration, Positron emission tomography, Tissues, Expectation maximization algorithms, Medical imaging, Binary data, Brain

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Multimodality medical image fusion is becoming increasingly important in clinical applications, which involves information processing, registration and visualization of interventional and/or diagnostic images obtained from different modalities. This work is to develop a multimodality medical image fusion technique through probabilistic quantification, segmentation, and registration, based on statistical data mapping, multiple feature correlation, and probabilistic mean ergodic theorems. The goal of image fusion is to geometrically align two or more image areas/volumes so that pixels/voxels representing the same underlying anatomical structure can be superimposed meaningfully. Three steps are involved. To accurately extract the regions of interest, we developed the model supported Bayesian relaxation labeling, and edge detection and region growing integrated algorithms to segment the images into objects. After identifying the shift-invariant features (i.e., edge and region information), we provided an accurate and robust registration technique which is based on matching multiple binary feature images through a site model based image re-projection. The image was initially segmented into specified number of regions. A rough contour can be obtained by delineating and merging some of the segmented regions. We applied region growing and morphological filtering to extract the contour and get rid of some disconnected residual pixels after segmentation. The matching algorithm is implemented as follows: (1) the centroids of PET/CT and MR images are computed and then translated to the center of both images. (2) preliminary registration is performed first to determine an initial range of scaling factors and rotations, and the MR image is then resampled according to the specified parameters. (3) the total binary difference of the corresponding binary maps in both images is calculated for the selected registration parameters, and the final registration is achieved when the minimum number of mismatch pixels gives the optimal scaling factor and rotation angle. Cross-modality quantification is then performed by incorporating the probabilistic pixel memberships from one modality (e.g., MRI) and the functional activities from another modality (e.g., PET or CT) within any given region of interests. The consistent nature of the comparative clinical studies show that our matching technique result in a robust and accurate image fusion of MRI and PET/CT scans.

Proceedings Article | 24 June 1998 Paper

Detection of mammographic masses using sector features with a multiple-circular-path neural network

Shih-Chung Lo, Huai Li, Akira Hasegawa, Yue Wang, Matthew Freedman, Seong Mun

Proceedings Volume 3338, (1998) https://doi.org/10.1117/12.310848

KEYWORDS: Neural networks, Mammography, Breast, Breast cancer, Feature extraction, Convolution, Tissues, Pattern recognition, Signal detection, Evolutionary algorithms

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SPIE Journal Paper | 1 December 1997

Finger tracking for breast palpation quantification using color image features

Jianchao Zeng, Yue Wang, Matthew Freedman, Seong Mun

OE, Vol. 36, Issue 12, (December 1997) https://doi.org/10.1117/12.10.1117/1.601585

KEYWORDS: Breast, Feature extraction, Visualization, Prototyping, Cameras, Optical tracking, Breast cancer, Imaging systems, RGB color model, Tumors

Proceedings Article | 22 May 1997 Paper

Design of a multimedia PC-based telemedicine network for the monitoring of renal dialysis patients

Walid Tohme, James Winchester, Hailei Dai, Nassib Khanafer, Marion Meissner, Jeff Collmann, Kevin Schulman, Ayah Johnson, Matthew Freedman, Seong Mun

Proceedings Volume 3035, (1997) https://doi.org/10.1117/12.274619

KEYWORDS: Telemedicine, Video, Multimedia, Diagnostics, Video compression, Data storage, Medicine, Databases, Imaging systems, Ultrasonography

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Proceedings Article | 22 May 1997 Paper

Multimedia medical data archive and retrieval server on the Internet

Darmadi Komo, Betty Levine, Matthew Freedman, Seong Mun, Y. Tang, Ted Chiang

Proceedings Volume 3035, (1997) https://doi.org/10.1117/12.274554

KEYWORDS: Databases, Data archive systems, Java, Internet, Robotics, Multimedia, Robotic systems, Surgery, Telecommunications, Imaging systems

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The Multimedia Medical Data Archive and Retrieval Server has been installed at the imaging science and information systems (ISIS) center in Georgetown University Medical Center to provide medical data archive and retrieval support for medical researchers. The medical data includes text, images, sound, and video. All medical data is keyword indexed using a database management system and placed temporarily in a staging area and then transferred to a StorageTek one terabyte tape library system with a robotic arm for permanent archive. There are two methods of interaction with the system. The first method is to use a web browser with HTML functions to perform insert, query, update, and retrieve operations. These generate dynamic SQL calls to the database and produce StorageTek API calls to the tape library. The HTML functions consist of a database, StorageTek interface, HTTP server, common gateway interface, and Java programs. The second method is to issue a DICOM store command, which is translated by the system's DICOM server to SQL calls and then produce StorageTek API calls to the tape library. The system performs as both an Internet and a DICOM server using standard protocols such as HTTP, HTML, Java, and DICOM. Users with proper authentication can log on to the server from anywhere on the Internet using a standard web browser resulting in a user-friendly, open environment, and platform independent solution for archiving multimedia medical data. It represents a complex integration of different components including a robotic tape storage system, database, user-interface, WWW protocols, and TCP/IP networking. The user will only deal with the WWW and DICOM server components of the system, the database and robotic tape library system are transparent and the user will not know that the medical data is stored on magnetic tapes. The server provides the researchers a cost-effective tool for archiving and retrieving medical data across a TCP/IP network environment. It will serve as a medium to exchange information between researchers at Georgetown University and those from other institutions.

Proceedings Article | 22 May 1997 Paper

Home teleradiology system

Darmadi Komo, Brian Garra, Matthew Freedman, Seong Mun

Proceedings Volume 3035, (1997) https://doi.org/10.1117/12.274560

KEYWORDS: Medical imaging, Computing systems, Teleradiology, Telecommunications, Computed tomography, Magnetic resonance imaging, Radiology, Imaging systems, Image storage, Medicine

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The Home Teleradiology Server system has been developed and installed at the Department of Radiology, Georgetown University Medical Center. The main purpose of the system is to provide a service for on-call physicians to view patients' medical images at home during off-hours. This service will reduce the overhead time required by on-call physicians to travel to the hospital, thereby increasing the efficiency of patient care and improving the total quality of the health care. Typically when a new case is conducted, the medical images generated from CT, US, and/or MRI modalities are transferred to a central server at the hospital via DICOM messages over an existing hospital network. The server has a DICOM network agent that listens to DICOM messages sent by CT, US, and MRI modalities and stores them into separate DICOM files for sending purposes. The server also has a general purpose, flexible scheduling software that can be configured to send image files to specific user(s) at certain times on any day(s) of the week. The server will then distribute the medical images to on- call physicians' homes via a high-speed modem. All file transmissions occur in the background without human interaction after the scheduling software is pre-configured accordingly. At the receiving end, the physicians' computers consist of high-end workstations that have high-speed modems to receive the medical images sent by the central server from the hospital, and DICOM compatible viewer software to view the transmitted medical images in DICOM format. A technician from the hospital, and DICOM compatible viewer software to view the transmitted medical images in DICOM format. A technician from the hospital will notify the physician(s) after all the image files have been completely sent. The physician(s) will then examine the medical images and decide if it is necessary to travel to the hospital for further examination on the patients. Overall, the Home Teleradiology system provides the on-call physicians with a cost-effective and convenient environment for viewing patients' medical images at home.

Proceedings Article | 7 May 1997 Paper

Stereo-vision system for finger tracking in breast self-examination

Jianchao Zeng, Yue Wang, Matthew Freedman, Seong Mun

Proceedings Volume 3031, (1997) https://doi.org/10.1117/12.273888

KEYWORDS: Breast, Cancer, Breast cancer, Tumors, Feature extraction, Mammography, Visualization, Cameras, Computing systems, Imaging systems

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Early detection of breast cancer, one of the leading causes of death by cancer for women in the US is key to any strategy designed to reduce breast cancer mortality. Breast self-examination (BSE) is considered as the most cost- effective approach available for early breast cancer detection because it is simple and non-invasive, and a large fraction of breast cancers are actually found by patients using this technique today. In BSE, the patient should use a proper search strategy to cover the whole breast region in order to detect al possible tumors. At present there is no objective approach or clinical data to evaluate the effectiveness of a particular BSE strategy. Even if a particular strategy is determined to be the most effective, training women to use it is still difficult because there is no objective way for them to know whether they are doing it correctly. We have developed a system using vision-based motion tracking technology to gather quantitative data about the breast palpation process for analysis of the BSE technique. By tracking position of the fingers, the system can provide the first objective quantitative data about the BSE process, and thus can improve our knowledge of the technique and help analyze its effectiveness. By visually displaying all the touched position information to the patient as the BSE is being conducted, the system can provide interactive feedback to the patient and create a prototype for a computer-based BSE training system. We propose to use color features, put them on the finger nails and track these features, because in breast palpation the background is the breast itself which is similar to the hand in color. This situation can hinder the ability/efficiency of other features if real time performance is required. To simplify feature extraction process, color transform is utilized instead of RGB values. Although the clinical environment will be well illuminated, normalization of color attributes is applied to compensate for minor changes in illumination. Neighbor search is employed to ensure real time performance, and a three-finger pattern topology is always checked for extracted features to avoid any possible false features. After detecting the features in the images, 3D position parameters of the colored fingers are calculated using the stereo vision principle. In the experiments, a 15 frames/second performance is obtained using an image size of 160 X 120 and an SGI Indy MIPS R4000 workstation. The system is robust and accurate, which confirms the performance and effectiveness of the proposed approach. The system is robust and accurate, which confirms the performance and effectiveness of the proposed approach. The system can be used to quantify search strategy of the palpation and its documentation. With real-time visual feedback, it can be used to train both patients and new physicians to improve their performance of palpation and thus visual feedback, it can be used to train both patients and new physicians to improve their performance of palpation and thus improve the rate of breast tumor detection.

Proceedings Article | 7 May 1997 Paper

Dyadic decomposition: a unified perspective on predictive, subband, and wavelet transforms

Shih-Chung Lo, Jian Hua Xuan, Huai Li, Yue Wang, Matthew Freedman, Seong Mun

Proceedings Volume 3031, (1997) https://doi.org/10.1117/12.273906

KEYWORDS: Wavelets, Wavelet transforms, Data compression, Lens design, Convolution, Image compression, Antimony, Data processing, Lithium, Digital filtering

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Proceedings Article | 7 May 1997 Paper

Surface reconstruction and visualization of the surgical prostate model

Jianhua Xuan, Isabell Sesterhenn, Wendelin Hayes, Yue Wang, Tulay Adali, Yukako Yagi, Matthew Freedman, Seong Mun

Proceedings Volume 3031, (1997) https://doi.org/10.1117/12.273939

KEYWORDS: 3D modeling, Prostate, Spine, Tumor growth modeling, Visualization, Biopsy, Visual process modeling, Prostate cancer, Tumors, Computer simulations

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Proceedings Article | 2 May 1997 Paper

Imaging performance of a low-dose screen-film mammography system

Hamid Jafroudi, Robert Jennings, Robert Gagne, Dorothy Artz, James Vucich, Matthew Freedman, Seong Mun

Proceedings Volume 3032, (1997) https://doi.org/10.1117/12.274003

KEYWORDS: Imaging systems, Mammography, Receptors, Chromium, X-rays, Computing systems, X-ray imaging, Aluminum, Tissues, Digital mammography

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Proceedings Article | 25 April 1997 Paper

Anatomic region-based dynamic range compression for chest radiographs using warping transformation of correlated distribution

Osamu Tsujii, Matthew Freedman, Seong Mun

Proceedings Volume 3034, (1997) https://doi.org/10.1117/12.274169

KEYWORDS: Lung, Chest imaging, Image processing, Visualization, Image segmentation, Image compression, Diagnostics, Chest, Algorithm development, Control systems

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Proceedings Article | 25 April 1997 Paper

Automated segmentation of anatomic regions in chest radiographs using an adaptive-sized hybrid neural network

Osamu Tsujii, Matthew Freedman, Seong Mun

Proceedings Volume 3034, (1997) https://doi.org/10.1117/12.274167

KEYWORDS: Lung, Neural networks, Image segmentation, Chest imaging, Image processing, Technetium, Feature extraction, Astatine, Neon, Silicon

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Proceedings Article | 25 April 1997 Paper

Classification of false-positive findings on computer-aided detection of breast microcalcifications

Matthew Freedman, Shih-Chung Lo, Dorothy Artz, Ivan Lau, Seong Mun

Proceedings Volume 3034, (1997) https://doi.org/10.1117/12.274173

KEYWORDS: Computer aided diagnosis and therapy, Mammography, Detection and tracking algorithms, Breast, Computing systems, Image analysis, Neural networks, Image quality, Software

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Proceedings Article | 25 April 1997 Paper

Image feature analysis for classification of microcalcifications in digital mammography: neural networks and genetic algorithms

Chris Wu, Osamu Tsujii, Matthew Freedman, Seong Mun

Proceedings Volume 3034, (1997) https://doi.org/10.1117/12.274136

KEYWORDS: Neural networks, Genetic algorithms, Mammography, Biopsy, Feature extraction, Image analysis, Image classification, Image processing, Breast cancer, Digital mammography

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We have developed an image feature-based algorithm to classify microcalcifications associated with benign and malignant processes in digital mammograms for the diagnosis of breast cancer. The feature-based algorithm is an alternative approach to image based method for classification of microcalcifications in digital mammograms. Microcalcifications can be characterized by a number of quantitative variables describing the underling key features of a suspicious region such as the size, shape, and number of microcalcifications in a cluster. These features are calculated by an automated extraction scheme for each of the selected regions. The features are then used as input to a backpropagation neural network to make a decision regarding the probability of malignancy of a selected region. The initial selection of image features set is a rough estimation that may include redundant and non-discriminant features. A genetic algorithm is employed to select an optimal image feature set from the initial feature set and select an optimized structure of the neural network for the optimal input features. The performance of neural network is compared with that of radiologists in classifying the clusters of microcalcifications. Two set of mammogram cases are used in this study. The first set is from the digital mammography database from the Mammographic Image Analysis Society (MIAS). The second set is from cases collected at Georgetown University Medical Center (GUMC). The diagnostic truth of the cases have been verified by biopsy. The performance of the neural network system is evaluated by ROC analysis. The system of neural network and genetic algorithms improves performance of our previous TRBF neural network. The neural network system was able to classify benign and malignant microcalcifications at a level favorably compared to experienced radiologists. The use of the neural network system can be used to help radiologists reducing the number biopsies in clinical applications. Genetic algorithms are an effective tool to select optimal input features and structure of a backpropagation neural network. The neural network, combined with genetic algorithms, is able to effectively classify benign and malignant microcalcifications. The results of the neural network system can be used to help reducing the number of benign biopsies.

Proceedings Article | 25 April 1997 Paper

Quantification of MR brain image sequence by adaptive structure probabilistic self-organizing mixture

Yue Wang, Chi-Ming Lau, Tulay Adali, Matthew Freedman, Seong Mun

Proceedings Volume 3034, (1997) https://doi.org/10.1117/12.274104

KEYWORDS: Tissues, Brain, Neuroimaging, Expectation maximization algorithms, Data modeling, Magnetic resonance imaging, Image segmentation, Distance measurement, Statistical modeling, Stochastic processes

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Proceedings Article | 25 April 1997 Paper

Mammographic mass detection by stochastic modeling and a multimodular neural network

Huai Li, Shih-Chung Lo, Yue Wang, Matthew Freedman, Seong Mun

Proceedings Volume 3034, (1997) https://doi.org/10.1117/12.274134

KEYWORDS: Image segmentation, Mammography, Image enhancement, Feature extraction, Neural networks, Statistical modeling, Stochastic processes, Databases, Tissues, Biopsy

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Proceedings Article | 16 April 1997 Paper

ROC study of screen-film mammography and storage phosphor digital mammography: analysis of nonconcordant classifications and implications for the approval of digital mammography systems

Matthew Freedman, Dorothy Artz, Jacquelyn Hogge, Rebecca Zuurbier, Hamid Jafroudi, Shih-Chung Lo, Seong Mun

Proceedings Volume 3036, (1997) https://doi.org/10.1117/12.271303

KEYWORDS: Digital mammography, Mammography, Cancer, Biopsy, Gold, Breast, Image processing, Diagnostics, Classification systems, Visibility

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Proceedings Article | 16 April 1997 Paper

Single-image hard-copy display of the spine utilizing digital radiography

Dorothy Artz, Timothy Janchar, David Milzman, Matthew Freedman, Seong Mun

Proceedings Volume 3036, (1997) https://doi.org/10.1117/12.271304

KEYWORDS: Spine, Image processing, Radiography, Chromium, Visualization, Absorbance, Image quality, Image visualization, Image quality standards

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Regions of the entire spine contain a wide latitude of tissue densities within the imaged field of view presenting a problem for adequate radiological evaluation. With screen/film technology, the optimal technique for one area of the radiograph is sub-optimal for another area. Computed radiography (CR) with its inherent wide dynamic range, has been shown to be better than screen/film for lateral cervical spine imaging, but limitations are still present with standard image processing. By utilizing a dynamic range control (DRC) algorithm based on unsharp masking and signal transformation prior to gradation and frequency processing within the CR system, more vertebral bodies can be seen on a single hard copy display of the lateral cervical, thoracic, and thoracolumbar examinations. Examinations of the trauma cross-table lateral cervical spine, lateral thoracic spine, and lateral thoracolumbar spine were collected on live patient using photostimulable storage phosphor plates, the Fuji FCR 9000 reader, and the Fuji AC-3 computed radiography reader. Two images were produced from a single exposure; one with standard image processing and the second image with the standard process and the additional DRC algorithm. Both sets were printed from a Fuji LP 414 laser printer. Two different DRC algorithms were applied depending on which portion of the spine was not well visualized. One algorithm increased optical density and the second algorithm decreased optical density. The resultant image pairs were then reviewed by a panel of radiologists. Images produced with the additional DRC algorithm demonstrated improved visualization of previously 'under exposed' and 'over exposed' regions within the same image. Where lung field had previously obscured bony detail of the lateral thoracolumbar spine due to 'over exposure,' the image with the DRC applied to decrease the optical density allowed for easy visualization of the entire area of interest. For areas of the lateral cervical spine and lateral thoracic spine that typically have a low optical density value, the DRC algorithm used increased the optical density over that region improving visualization of C7-T2 and T11-L2 vertebral bodies; critical in trauma radiography. Emergency medicine physicians also reviewing the lateral cervical spine images were able to clear 37% of the DRC images compared to 30% of the non-DRC images for removal of the cervical collar. The DRC processed images reviewed by the physicians do not have a typical screen/film appearance; however, these different images were preferred for the three examinations in this study. This method of image processing after being tested and accepted, is in use clinically at Georgetown University Medical Center Department of Radiology for the following examinations: cervical spine, lateral thoracic spine, lateral thoracolumbar examinations, facial bones, shoulder, sternum, feet and portable chest. Computed radiography imaging of the spine is improved with the addition of histogram equalization known as dynamic range control (DRC). More anatomical structures are visualized on a single hard copy display.

Proceedings Article | 16 April 1997 Paper

Quality control in digital mammography: automatic detection of under- and over-exposed mammograms

Chris Wu, Matthew Freedman, Akira Hasegawa, Seong Mun

Proceedings Volume 3036, (1997) https://doi.org/10.1117/12.271289

KEYWORDS: Mammography, Breast, Image quality, Skin, Chromium, Image segmentation, Digital mammography, Control systems, Scanners, Computing systems

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We developed a quality control system (QCS) for digital mammography that can notify technologists in real time of mammograms of poor image quality due to under or over exposure. Mammograms are digitized by a Lumisys Scanner at 100 micron and 12 bits per pixel. An automatic image segmentation technique is employed to extract area inside the breast in mammogram. Histograms of the segmented areas are then calculated. By analyzing the composition of histograms, the computer program determines whether the original films have properly exposed. Traditional image segmentation techniques are based on histogram analysis of digitized mammograms. However, such methods often fail with mammograms of low contrast or that are under-exposed because the difference in brightness across the breast skin line is so small that it is difficult to define boundary by thresholding or region growing techniques. We proposed a novel method to detect breast skin line based on statistical changes of gradient. By analyzing the histogram composition of normal, under and over-exposed films, we defined an image feature that describes the image intensity content of underlying mammograms. The criterion for determining the category of a mammogram were established by studying a training database of normal, under, and over exposed films. We can then classify the mammograms using the image feature, based on the established criterion. Over 150 real mammograms of different exposure levels were analyzed. The images were classified by the computer system into groups of normal, slightly under-exposed, under-exposed, slightly over- exposed, and over-exposed. We compared the classification results by computer with a radiologist's evaluation. Our QCS system was able to correctly classify over 85% of the cases. Receiver operating curve (ROC) analysis will be employed to evaluate the performance of the QCS system in determining the image quality of digital mammograms. Our QCS program is able to automatically determine whether a mammogram is properly exposed and advise a technologist to re-take additional exposures. The QCS correctly identified 100% of over- and under-exposed mammograms and 92% of mammograms of normal exposure. The QCS can help reduce the cost of recalling patients and improve the overall quality of mammographic service.

Proceedings Article | 16 April 1996 Paper

Detection of clustered microcalcifications using fuzzy modeling and convolution neural network

Shih-Chung Lo, Huai Li, Jyh-Shyan Lin, Akira Hasegawa, Osamu Tsujii, Matthew Freedman, Seong Mun

Proceedings Volume 2710, (1996) https://doi.org/10.1117/12.237921

KEYWORDS: Fuzzy logic, Neural networks, Convolution, Mammography, Computing systems, Databases, Imaging systems, Image classification, Wavelets, Image filtering

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Proceedings Article | 16 April 1996 Paper

Region-based enhancement of chest and cervical spine radiographs

Jyh-Shyan Lin, Dorothy Steller Artz, Huai Li, Kevin Legendre, Matthew Freedman, Seong Mun

Proceedings Volume 2710, (1996) https://doi.org/10.1117/12.237983

KEYWORDS: Image filtering, Image enhancement, Chest, Spine, Image processing, Wavelets, Visualization, Chest imaging, Radiography, Image visualization

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Proceedings Article | 16 April 1996 Paper

Classification of microcalcifications in digital mammograms for the diagnosis of breast cancer

Osamu Tsujii, Akira Hasegawa, Chris Wu, Shih-Chung Lo, Matthew Freedman, Seong Mun

Proceedings Volume 2710, (1996) https://doi.org/10.1117/12.237985

KEYWORDS: Distance measurement, Neurons, Neural networks, Mammography, Databases, Feature selection, Breast cancer, Image restoration, Image processing, Einsteinium

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We are developing a computer program for automated classification of clustered microcalcifications associated with benign and malignant processes in mammograms. Accurate classification of microcalcifications into benign and malignant groups would help improve diagnosis sensitivity as well as reduce the number of unnecessary biopsies. In this study we investigate the effectiveness of several image features and the proposed neural network, the Trend-Oriented Radial Basis Function (TRBF). Our database is composed of 47 benign and 81 malignant region of interest (ROI) images, a total of 128 ROIs, which are selected from 50 micron by 50 micron digitized whole mammograms manually. Each 256 by 256 pixel ROI image contains clustered microcalcifications. First we extract 16 image features which are calculated from a binarized microcalcifications image and its two morphological dilation images. These features are based on 3 morphological criteria: (1) size and/or shape of the calcifications, (2) size or shape of the 'cluster,' and (3) number of microcalcifications. Secondly, we apply Karhunen-Loeve (K-L) expansion to 16 dimension feature space in order to reduce the dimension of the problem. Next we select two dimensional K-L features, which are outcomes of K-L expansion, through a calculation of the Euclidean distance measure. Finally we classify them based on two dimensional K-L features using the proposed neural network. The proposed TRBF neural network has three layers and a new learning algorithm. Its cost function for the learning process consists of a sum of squares error and a sum of inverse RBFs width. Through the learning process, the center and the width of each RBF and the weights between hidden and output layers are decided. The performances of the K-L features and the TRBF neural network are evaluated through the round-robin method in which one sample is tested after learning based on the rest of 127 samples. The two dimensional K-L features are more distinguishable than the raw two dimensional feature combination. The proposed TRBF network is able to define the trend of the distribution better than former RBF networks. According to the receiver operating characteristic (ROC) curve, this system indicates a better performance than one trained radiologist.

Proceedings Article | 16 April 1996 Paper

Digital mammography in the radio-dense and complex pattern breast

Matthew Freedman, Dorothy Steller Artz, Hamid Jafroudi, Jacquelyn Hogge, Rebecca Zuurbier, Jyh-Shyan Lin, Raj Katial, Seong Mun

Proceedings Volume 2710, (1996) https://doi.org/10.1117/12.237984

KEYWORDS: Image processing, Breast, Image enhancement, Digital image processing, Digital mammography, Mammography, Absorbance, Breast cancer, Skin, Image resolution

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Proceedings Article | 15 April 1996 Paper

Design-based approach to ethics in computer-aided diagnosis

Jeff Collmann, Jyh-Shyan Lin, Matthew Freedman, Chris Wu, Wendelin Hayes, Seong Mun

Proceedings Volume 2707, (1996) https://doi.org/10.1117/12.238492

KEYWORDS: Computer aided diagnosis and therapy, Databases, Computer aided design, Computing systems, Mammography, Breast cancer, Neural networks, Chest imaging, Lung cancer, Lung

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Proceedings Article | 15 April 1996 Paper

Diagnosis of breast cancer by MRI: a 3D computer visualization and analysis system

Chris Wu, Richard Patt, Matthew Freedman, Seong Mun

Proceedings Volume 2707, (1996) https://doi.org/10.1117/12.238500

KEYWORDS: 3D image processing, Breast, 3D image enhancement, Image enhancement, Image visualization, Image analysis, Magnetic resonance imaging, Neural networks, Breast cancer, Visualization

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Proceedings Article | 15 April 1996 Paper

Optimization of orthonormal wavelet decomposition: implication of data accuracy, feature preservation, and compression effects

Shih-Chung Lo, Huai Li, Yue Wang, Matthew Freedman, Seong Mun

Proceedings Volume 2707, (1996) https://doi.org/10.1117/12.238448

KEYWORDS: Wavelets, Neural networks, Quantization, Image compression, Convolution, Mammography, Linear filtering, Filtering (signal processing), Signal to noise ratio, Wavelet transforms

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Proceedings Article | 11 April 1996 Paper

Storage phosphor-based digital mammography using a low-dose x-ray system optimized for screen-film mammography

Robert Jennings, Hamid Jafroudi, Robert Gagne, Thomas Fewell, P. Quinn, Dorothy Steller Artz, James Vucich, Matthew Freedman, Seong Mun

Proceedings Volume 2708, (1996) https://doi.org/10.1117/12.237785

KEYWORDS: X-rays, Imaging systems, X-ray imaging, Mammography, Modulation transfer functions, Receptors, Signal to noise ratio, Aluminum, Spatial frequencies, Digital mammography

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Proceedings Article | 11 April 1996 Paper

Dual-energy in mammography: feasibility study

Hamid Jafroudi, Shih-Chung Lo, Huai Li, Dorothy Steller Artz, Matthew Freedman, Seong Mun

Proceedings Volume 2708, (1996) https://doi.org/10.1117/12.237789

KEYWORDS: Mammography, X-rays, Imaging systems, Signal to noise ratio, Computing systems, Radiography, Sensors, Breast, Tissues, Signal attenuation

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Proceedings Article | 11 April 1996 Paper

Digital mammography: an evaluation of the shape of microcalcifications

Matthew Freedman, Dorothy Steller Artz, Hamid Jafroudi, Jacquelyn Hogge, Rebecca Zuurbier, Raj Katial, Curtis Green, Seong Mun

Proceedings Volume 2708, (1996) https://doi.org/10.1117/12.237826

KEYWORDS: Digital mammography, Mammography, Cancer, Biopsy, Chromium, Radiography, Diagnostics, Breast cancer, Imaging systems, Radiology

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Proceedings Article | 21 November 1995 Paper

Multimedia medical archiving system

Arun Sood, George Atallah, Amar Rao, Kathleen Perez-Lopez, Matthew Freedman

Proceedings Volume 2606, (1995) https://doi.org/10.1117/12.227264

KEYWORDS: Image retrieval, Image storage, Surgery, Medical imaging, Multimedia, Databases, Image processing, Image acquisition, Imaging systems, Data modeling

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Proceedings Article | 12 May 1995 Paper

Artificial convolution neural network with wavelet kernels for disease pattern recognition

Shih-Chung Lo, Huai Li, Jyh-Shyan Lin, Akira Hasegawa, Chris Wu, Matthew Freedman, Seong Mun

Proceedings Volume 2434, (1995) https://doi.org/10.1117/12.208730

KEYWORDS: Wavelets, Convolution, Neural networks, Linear filtering, Pattern recognition, Wavelet transforms, Feature extraction, Mammography, Signal processing, Matrices

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Proceedings Article | 12 May 1995 Paper

Adaptive-sized neural-networks-based computer-aided diagnosis of microcalcifications

Akira Hasegawa, Chris Wu, Matthew Freedman, Seong Mun

Proceedings Volume 2434, (1995) https://doi.org/10.1117/12.208727

KEYWORDS: Neural networks, Computer aided diagnosis and therapy, Mammography, Solid modeling, Breast cancer, Image processing, Reliability, Diagnostics, Evolutionary algorithms, Computer networks

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Proceedings Article | 12 May 1995 Paper

Evaluating the role of gigabit speed wide-area networks in remote medical treatment

Walid Tohme, James Rodgers, Seong Mun, Matthew Freedman, Mark Hansen, Jay Cook, George Popescu, David Yun, Hong-Mei Garcia

Proceedings Volume 2435, (1995) https://doi.org/10.1117/12.208770

KEYWORDS: Radiology, Teleradiology, Radiotherapy, Satellites, Medicine, Data communications, Networks, Satellite communications, Diagnostics, Mammography

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Proceedings Article | 12 May 1995 Paper

Application of artificial neural networks for reducing false positives in lung nodule detection on digital chest radiiographs

Jyh-Shyan Lin, Shih-Chung Lo, Matthew Freedman, Seong Mun

Proceedings Volume 2434, (1995) https://doi.org/10.1117/12.208728

KEYWORDS: Neurons, Feature extraction, Computer aided diagnosis and therapy, Lung, Chest, Artificial neural networks, Chest imaging, Image processing, Algorithm development, Digital image processing

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Proceedings Article | 8 May 1995 Paper

Quality control on storage phosphor digital radiography system

Hamid Jafroudi, Dorothy Steller Artz, Matthew Freedman, Seong Mun

Proceedings Volume 2432, (1995) https://doi.org/10.1117/12.208378

KEYWORDS: Chromium, Image quality, Imaging systems, Image processing, Radiography, Image quality standards, Absorbance, Control systems, Standards development, Phase modulation

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Proceedings Article | 8 May 1995 Paper

Performance characteristics of high-resolution charge-coupled device film digitizers

Shih-Chung Lo, Philip Butson, Jyh-Shyan Lin, Huai Li, Matthew Freedman, Seong Mun

Proceedings Volume 2432, (1995) https://doi.org/10.1117/12.208367

KEYWORDS: Charge-coupled devices, Scanners, Absorbance, Modulation transfer functions, Mammography, Image resolution, Spatial resolution, Distortion, Systems modeling, Breast

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Proceedings Article | 8 May 1995 Paper

Multiparameter optimization of mammography with alternative x-ray sources

Hamid Jafroudi, Robert Jennings, E. Muntz, Matthew Freedman, Seong Mun

Proceedings Volume 2432, (1995) https://doi.org/10.1117/12.208329

KEYWORDS: Breast, Molybdenum, Rhodium, Mammography, X-ray sources, X-rays, Aluminum, Sensors, Systems modeling, Signal attenuation

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Proceedings Article | 8 May 1995 Paper

Digital mammography: tradeoffs between 50- and 100-micron pixel size

Matthew Freedman, Dorothy Steller Artz, Hamid Jafroudi, Shih-Chung Lo, Rebecca Zuurbier, Raj Katial, Wendelin Hayes, Chris Wu, Jyh-Shyan Lin, Richard Steinman, Walid Tohme, Seong Mun

Proceedings Volume 2432, (1995) https://doi.org/10.1117/12.208387

KEYWORDS: Digital mammography, Cancer, Breast, Sensors, Mammography, Breast cancer, Biopsy, Radiology, Data storage, Imaging systems

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Proceedings Article | 8 May 1995 Paper

Performance and maintenance of storage phosphor plates and cassettes in digital radiography

Dorothy Steller Artz, Hamid Jafroudi, Matthew Freedman, Seong Mun

Proceedings Volume 2432, (1995) https://doi.org/10.1117/12.208380

KEYWORDS: Radiography, Image quality, Chromium, Lead, Imaging systems, Surface plasmons, Mammography, Surgery, Image resolution, Telecommunications

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Proceedings Article | 8 May 1995 Paper

Digital mammography: the effects of decreased exposure

Matthew Freedman, Dorothy Steller Artz, Hamid Jafroudi, Shih-Chung Lo, Rebecca Zuurbier, Raj Katial, Wendelin Hayes, Chris Wu, Jyh-Shyan Lin, Seong Mun

Proceedings Volume 2432, (1995) https://doi.org/10.1117/12.208369

KEYWORDS: Digital mammography, Chlorine, Image processing, Radiography, Mammography, Breast, Digital image processing, Imaging systems, Absorption, Calcium

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Proceedings Article | 27 April 1995 Paper

Academic consortium for the evaluation of computer-aided diagnosis (CADx) in mammography

Seong Mun, Matthew Freedman, Chris Wu, Shih-Chung Lo, Carey Floyd, Joseph Lo, Heang-Ping Chan, Mark Helvie, Nicholas Petrick, Berkman Sahiner, Datong Wei, Dev Chakraborty, Laurence Clarke, Maria Kallergi, Bob Clark, Yongmin Kim

Proceedings Volume 2431, (1995) https://doi.org/10.1117/12.207638

KEYWORDS: Computer aided diagnosis and therapy, Mammography, Computing systems, Algorithm development, Breast cancer, Databases, Optimization (mathematics), Cancer, Digital mammography, Detection and tracking algorithms

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Proceedings Article | 27 April 1995 Paper

Full-frame compression of discrete wavelet and cosine transforms

Shih-Chung Lo, Huai Li, Brian Krasner, Matthew Freedman, Seong Mun

Proceedings Volume 2431, (1995) https://doi.org/10.1117/12.207613

KEYWORDS: Image compression, Transform theory, Mammography, Wavelets, Chest imaging, Quantization, Digital mammography, Computing systems, Medical imaging, Image resolution

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Proceedings Article | 17 April 1995 Paper

Single-image hard copy display of musculoskeletal digital radiographs

Kevin Legendre, Dorothy Steller Artz, Matthew Freedman, Seong Mun

Proceedings Volume 2436, (1995) https://doi.org/10.1117/12.206847

KEYWORDS: Image processing, Tissues, Absorbance, Radiography, Visualization, Image quality standards, Image quality, Image enhancement, Bone, Image compression

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Proceedings Article | 11 May 1994 Paper

Classification of microcalcifications in radiographs of pathological specimen for the diagnosis of breast cancer

Chris Wu, Shih-Chung Lo, Matthew Freedman, Akira Hasegawa, Rebecca Zuurbier, Seong Mun

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175099

KEYWORDS: Breast cancer, Neural networks, Radiography, Mammography, Image processing, Biopsy, Diagnostics, Breast, Databases, Convolution

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Proceedings Article | 11 May 1994 Paper

Image processing of storage phosphor musculoskeletal radiographs: a comparison of the AGFA and Fuji bone algorithms

Martha Nelson, Matthew Freedman, Einar Pe, Shih-Chung Lo, Donald Smith, Robert Leckie, Seong Mun

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175125

KEYWORDS: Image processing, Radon, Absorbance, Spatial frequencies, Radiography, Bone, Visibility, Image enhancement, Tissues, Imaging systems

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Proceedings Article | 11 May 1994 Paper

Comparison of conventional magnification mammography using film screen mammography and electronic magnification

Francine Fields, Matthew Freedman, Shih-Chung Lo, Rebecca Zuurbier, Seong Mun

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175104

KEYWORDS: Mammography, Image processing, Cancer, Breast, Breast cancer, Biopsy, Visibility, Image enhancement, Diagnostics, Visualization

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The detection of breast microcalcifications is an important finding that can indicate the presence of breast cancer. Once detected, the radiologist attempts to classify the microcalcifications into patterns that are associated with benign or malignant processes. If they cannot be so classified they are considered indeterminate calcifications. Those characteristics of the microcalcifications that are used in this important distinction are the shape and number of calcifications and the presence or absence of any change from a prior mammogram. (1-4) Geometric magnification views are often obtained to provide better visualization of the microcalcifications. Although conventional screen film mammography has sufficient high contrast resolution (20 line pairs per mm (lplmm)) to demonstrate these findings and the type of hand held magnifying lens often used by Radiologists has sufficient power to demonstrate more than 30 lp/mm, in practice, the region containing the microcalcifications is often in a low contrast region of the image and the hand held magnifying lens does not suffice. We produce electronic magnification views of the breast by taking an existing screen film mammogram and digitizing it with a small pixel size. (5) The image is then displayed on a monitor or printed on a laser printer that has a larger pixel size, thereby resulting in magnification. The visibility of the calcifications is enhanced by changing the window level and window width to increase optical density or to decrease luminance and to increase contrast. The effect of these two actions is to correct in part for the low contrast in the original image. Three potential benefits results from electronic magnification: 1. The current film on which calcifications are seen can be magnified and evaluated without the patient having to return for a geometric magnification view. 2. The prior mammogram (if of suboptimal quality) can be digitized and re-displayed to better determine whether the microcalcifications have changed. 3. The digitized image may allow the reclassification of calcifications from benign appearance to a pattern suggestive of cancer, thereby resulting in an earlier decision to biopsy.

Proceedings Article | 11 May 1994 Paper

Comparison of musculoskeletal images from the AGFA and Fuji digital imaging systems

Martha Nelson, Matthew Freedman, Einar Pe, Seong Mun

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175103

KEYWORDS: Image processing, Imaging systems, Radiography, Tissues, Visualization, Digital imaging, Diagnostics, Bone, Spine, Image resolution

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Proceedings Article | 11 May 1994 Paper

Convolution neural-network-based detection of lung structures

Akira Hasegawa, Shih-Chung Lo, Matthew Freedman, Seong Mun

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175101

KEYWORDS: Lung, Neural networks, Chest imaging, Image processing, Convolution, Computer aided diagnosis and therapy, Heart, Evolutionary algorithms, Chest, Diagnostics

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Proceedings Article | 1 May 1994 Paper

Evaluation of specific PACS equipment components: operational and maintenance experience

John Romlein, Robert Leckie, Suzy Smith, Edward Quillin, Matthew Freedman

Proceedings Volume 2164, (1994) https://doi.org/10.1117/12.174001

KEYWORDS: Chromium, Picture Archiving and Communication System, Imaging systems, Radiography, Radiology, Surgery, Image acquisition, Computing systems, Phase modulation, Medical diagnostics

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Proceedings Article | 1 May 1994 Paper

Radiological image compression: image characteristics and clinical considerations

Shih-Chung Lo, Man-Bae Kim, Huai Li, Brian Krasner, Matthew Freedman, Seong Mun

Proceedings Volume 2164, (1994) https://doi.org/10.1117/12.174009

KEYWORDS: Image compression, Image quality, Image processing, Quantization, Medical imaging, Modulation transfer functions, Mammography, Image resolution, Image storage, Radiography

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Proceedings Article | 1 May 1994 Paper

Tests of a new paper display for radiology images

Richard Patt, Matthew Freedman, Dorothy Steller Artz, Sunder Rajan, Jeff Collmann, Seong Mun

Proceedings Volume 2164, (1994) https://doi.org/10.1117/12.174003

KEYWORDS: Printing, Nonimpact printing, Magnetic resonance imaging, Image quality, Radiology, Imaging systems, Telecommunications, Cameras, Interfaces

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Proceedings Article | 1 May 1994 Paper

Image processing in digital mammography

Matthew Freedman, Einar Pe, Rebecca Zuurbier, Raj Katial, Hamid Jafroudi, Martha Nelson, Shih-Chung Lo, Seong Mun

Proceedings Volume 2164, (1994) https://doi.org/10.1117/12.174039

KEYWORDS: Image processing, Digital mammography, Mammography, Breast, Absorbance, Spatial frequencies, Digital image processing, Calcium, Optical spheres, Visibility

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Proceedings Article | 1 April 1994 Paper

Storage phosphor images in the neonatal intensive care unit: a multiobserver preference study

David Pearce, Matthew Freedman, Einar Pe, Dorothy Steller Artz, Clifton Leftridge, Martha Nelson, Shih-Chung Lo, Seong Mun

Proceedings Volume 2166, (1994) https://doi.org/10.1117/12.171732

KEYWORDS: Imaging systems, Absorbance, Image resolution, Radiography, Image quality, Collimation, Nonimpact printing, Chest imaging, Chest, Collimators

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Proceedings Article | 14 September 1993 Paper

Shape feature analysis using artificial neural networks for improvements of hybrid lung nodule detection system

Y. Chiou, Fleming Lure, Panos Ligomenides, Matthew Freedman, Steven Fritz

Proceedings Volume 1898, (1993) https://doi.org/10.1117/12.154549

KEYWORDS: Shape analysis, Artificial neural networks, Lung

Proceedings Article | 14 September 1993 Paper

Computer-assisted diagnosis of lung nodule detection using artificial convoultion neural network

Shih-Chung Lo, Jyh-Shyan Lin, Matthew Freedman, Seong Mun

Proceedings Volume 1898, (1993) https://doi.org/10.1117/12.154572

KEYWORDS: Neural networks, Convolution, Lung, Image processing, Pattern recognition, Image enhancement, Diagnostics, Electroluminescence, Artificial neural networks, Chest imaging

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