Prof. David R. Haynor Profile

Prof. David R. Haynor

Professor at Univ of Washington

SPIE Involvement:

Conference Program Committee | Author | Editor

Publications (50)

Proceedings Article | 15 February 2021 Presentation + Paper

Finding atrophy patterns of grey matter through orthonormal non-negative factorization

Wenbo Zhang, Kwun Chuen Gary Chan, Dean Shibata, David Haynor

Proceedings Volume 11596, 115960X (2021) https://doi.org/10.1117/12.2581860

KEYWORDS: Brain, Alzheimer's disease, Magnetic resonance imaging, Dimension reduction, Control systems, 3D image processing

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Proceedings Article | 15 March 2019 Paper

The utility of deep learning: evaluation of a convolutional neural network for detection of intracranial bleeds on non-contrast head computed tomography studies

P. Ojeda, M. Zawaideh, M. Mossa-Basha, D. Haynor

Proceedings Volume 10949, 109493J (2019) https://doi.org/10.1117/12.2513167

KEYWORDS: Computed tomography, Head, Data modeling, Radiology, Convolutional neural networks, Evolutionary algorithms, Performance modeling, Diagnostics, Artificial intelligence, Picture Archiving and Communication System

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Proceedings Article | 12 March 2018 Paper

Automated connectivity-based cortical mapping using registration-constrained classification

K. Eschenburg, D. Haynor, T. Grabowski

Proceedings Volume 10578, 105782T (2018) https://doi.org/10.1117/12.2293968

KEYWORDS: Brain mapping, Data modeling, Brain, Neural networks, Data acquisition, Matrices, Neuroimaging, Diffusion, Functional magnetic resonance imaging

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Proceedings Article | 27 February 2018 Presentation + Paper

Deep-learning derived features for lung nodule classification with limited datasets

P. Thammasorn, W. Wu, L. Pierce, S. Pipavath, P. D. Lampe, A. M. Houghton, D. Haynor, W. A. Chaovalitwongse, P. Kinahan

Proceedings Volume 10575, 105751F (2018) https://doi.org/10.1117/12.2293236

KEYWORDS: Computed tomography, Diagnostics, Image processing, Machine learning, Feature extraction, Lung, Lung cancer, Network architectures, Image classification, Convolutional neural networks

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Proceedings Article | 13 March 2013 Paper

A flexible toolkit for rapid GPU-based generation of DRRs for 2D-3D registration

Grant Marchelli, David Haynor, William Ledoux, Richard Tsai, Duane Storti

Proceedings Volume 8669, 86691C (2013) https://doi.org/10.1117/12.2007225

KEYWORDS: Bone, Image segmentation, Image intensifiers, Data storage, 3D image processing, Data centers, Gait analysis, Kinematics, Composites, Image registration

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Proceedings Article | 16 February 2012 Paper

Comparative analysis of semantic localization accuracies between adult and pediatric DICOM CT images

Duncan Robertson, Sayan Pathak, Antonio Criminisi, Steve White, David Haynor, Oliver Chen, Khan Siddiqui

Proceedings Volume 8319, 83190N (2012) https://doi.org/10.1117/12.912428

KEYWORDS: Computed tomography, Data modeling, Kidney, Liver, Databases, Abdomen, Heart, Feature extraction, Image registration, Detection and tracking algorithms

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Proceedings Article | 12 March 2011 Paper

Robust linear registration of CT images using random regression forests

Ender Konukoglu, Antonio Criminisi, Sayan Pathak, Duncan Robertson, Steve White, David Haynor, Khan Siddiqui

Proceedings Volume 7962, 79621X (2011) https://doi.org/10.1117/12.878085

KEYWORDS: Image registration, Image processing algorithms and systems, Computed tomography, Image segmentation, Optimization (mathematics), Databases, Stochastic processes, Medical imaging, Detection and tracking algorithms, 3D image processing

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Proceedings Article | 13 March 2009 Paper

Validation of CT-video registration for guiding a novel ultrathin bronchoscope to peripheral lung nodules using electromagnetic tracking

Timothy Soper, David Haynor, Robb Glenny, Eric Seibel

Proceedings Volume 7261, 72610C (2009) https://doi.org/10.1117/12.812329

KEYWORDS: Electromagnetism, Image registration, Lung, Medical imaging, Visualization, Visual process modeling, Current controlled current source, Actuators, Thermal weapon sites, Printed circuit board testing

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Proceedings Article | 28 February 2009 Paper

Gene expression based mouse brain parcellation using Markov random field regularized non-negative matrix factorization

Sayan Pathak, David Haynor, Carol Thompson, Ed Lein, Michael Hawrylycz

Proceedings Volume 7262, 726212 (2009) https://doi.org/10.1117/12.813437

KEYWORDS: Brain, Thalamus, Magnetorheological finishing, Computer simulations, Neuroimaging, Genetic algorithms, Somatosensory cortex, Algorithm development, 3D image processing, Spatial resolution

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Proceedings Article | 17 March 2008 Paper

CT and MR image fusion for CSF leak diagnosis

Yangqiu Hu, David Haynor, Kenneth Maravilla

Proceedings Volume 6918, 69181P (2008) https://doi.org/10.1117/12.771057

KEYWORDS: Image fusion, Computed tomography, Magnetic resonance imaging, Image registration, Bone, Visualization, Medical imaging, Tissues, Picture Archiving and Communication System, Image processing

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Proceedings Article | 29 March 2007 Paper

A model of respiratory airway motion for real-time tracking of an ultrathin bronchoscope

Timothy Soper, David Haynor, Robb Glenny, Eric Seibel

Proceedings Volume 6511, 65110M (2007) https://doi.org/10.1117/12.710150

KEYWORDS: Lung, Image registration, Image segmentation, Motion models, Computed tomography, Chest, Data modeling, Image resolution, Mathematical modeling, Bronchoscopy

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Proceedings Article | 8 March 2007 Paper

WHIPPET: a collaborative software environment for medical image processing and analysis

Yangqiu Hu, David Haynor, Kenneth Maravilla

Proceedings Volume 6512, 65123B (2007) https://doi.org/10.1117/12.709721

KEYWORDS: Image processing, Diffusion tensor imaging, Medical imaging, Functional magnetic resonance imaging, Image analysis, Brain, Magnetic resonance imaging, 3D image processing, Operating systems, Image segmentation

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Proceedings Article | 10 March 2006 Paper

Image segmentation and registration for the analysis of joint motion from 3D MRI

Yangqiu Hu, David Haynor, Michael Fassbind, Eric Rohr, William Ledoux

Proceedings Volume 6141, 61410H (2006) https://doi.org/10.1117/12.654338

KEYWORDS: Bone, Image segmentation, Image registration, Magnetic resonance imaging, Motion analysis, Polonium, Visualization, 3D magnetic resonance imaging, Kinematics, 3D image processing

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Proceedings Article | 29 April 2005 Paper

Prostate ultrasound image segmentation using level set-based region flow with shape guidance

Lixin Gong, Lydia Ng, Sayan Pathak, Ismail Tutar, Paul Cho, David Haynor, Yongmin Kim

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.594403

KEYWORDS: Image segmentation, Prostate, Ultrasonography, Signal to noise ratio, Data modeling, Binary data, Statistical modeling, Image processing, Image processing algorithms and systems, Medical imaging

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Proceedings Article | 29 April 2005 Paper

Gene expression detection and expression visualization in in situ hybridized cross-sectional images of the mouse brain

Sayan Pathak, Ed Lein, Simon Smith, David Haynor, Michael Hawrylycz

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.594722

KEYWORDS: Brain, Tissues, Neuroimaging, Genetic algorithms, RGB color model, Image segmentation, Detection and tracking algorithms, Visualization, Algorithm development, Signal to noise ratio

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Proceedings Article | 12 April 2005 Paper

MR and CT image fusion of the cervical spine: a noninvasive alternative to CT-myelography

Yangqiu Hu, Sohail Mirza, Jeffrey Jarvik, Patrick Heagerty, David Haynor

Proceedings Volume 5744, (2005) https://doi.org/10.1117/12.594990

KEYWORDS: Image fusion, Magnetic resonance imaging, Computed tomography, Image segmentation, Surgery, Image registration, Spine, Bone, Tissues, Pathology

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Proceedings Article | 12 May 2004 Paper

Multirigid registration of MR and CT images of the cervical spine

Yangqiu Hu, David Haynor

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.533949

KEYWORDS: Image segmentation, Computed tomography, Magnetic resonance imaging, Image fusion, Image registration, Bone, Spine, Tissues, Image processing, Surgery

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Proceedings Article | 30 May 2003 Paper

Prostate segmentation in ultrasound images with deformable shape priors

Lixin Gong, Sayan Pathak, David Haynor, Paul Cho, Yongmin Kim

Proceedings Volume 5029, (2003) https://doi.org/10.1117/12.480384

KEYWORDS: Prostate, Image segmentation, Ultrasonography, Image processing algorithms and systems, 3D modeling, Data modeling, Algorithm development, Edge detection, Detection and tracking algorithms, Speckle

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Proceedings Article | 17 May 2002 Paper

Registration of prostate brachytherapy seeds with prostate anatomy for improved patient dosimetry

Lixin Gong, Paul Cho, Ben Han, Sayan Pathak, David Haynor, Kent Wallner, Steve Sutlief, Yongmin Kim

Proceedings Volume 4681, (2002) https://doi.org/10.1117/12.466961

KEYWORDS: Prostate, Fluoroscopy, Ultrasonography, Image registration, 3D image processing, Computed tomography, Imaging systems, Prostate cancer, Tumors, Image segmentation

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Proceedings Article | 11 April 2002 Paper

Intensity-based image registration for 3D spatial compounding using a freehand 3D ultrasound system

Niko Pagoulatos, David Haynor, Yongmin Kim

Proceedings Volume 4687, (2002) https://doi.org/10.1117/12.462184

KEYWORDS: Image registration, 3D acquisition, 3D image processing, Data acquisition, 3D image reconstruction, Chromium, In vivo imaging, Ultrasonography, Sensors, Image quality

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3D spatial compounding involves the combination of two or more 3D ultrasound (US) data sets, acquired under different insonation angles and windows, to form a higher quality 3D US data set. An important requirement for this method to succeed is the accurate registration between the US images used to form the final compounded image. We have developed a new automatic method for rigid and deformable registration of 3D US data sets, acquired using a freehand 3D US system. Deformation is provided by using a 3D thin-plate spline (TPS). Our method is fundamentally different from the previous ones in that the acquired scattered US 2D slices are registered and compounded directly into the 3D US volume. Our approach has several benefits over the traditional registration and spatial compounding methods: (i) we only peform one 3D US reconstruction, for the first acquired data set, therefore we save the computation time required to reconstruct subsequent acquired scans, (ii) for our registration we use (except for the first scan) the acquired high-resolution 2D US images rather than the 3D US reconstruction data which are of lower quality due to the interpolation and potential subsampling associated with 3D reconstruction, and (iii) the scans performed after the first one are not required to follow the typical 3D US scanning protocol, where a large number of dense slices have to be acquired; slices can be acquired in any fashion in areas where compounding is desired. We show that by taking advantage of the similar information contained in adjacent acquired 2D US slices, we can reduce the computation time of linear and nonlinear registrations by a factor of more than 7:1, without compromising registration accuracy. Furthermore, we implemented an adaptive approximation to the 3D TPS with local bilinear transformations allowing additional reduction of the nonlinear registration computation time by a factor of approximately 3.5. Our results are based on a commercially available tissue-mimicking abdominal phantom and in-vivo muscle data.

Proceedings Article | 3 July 2001 Paper

Validation of brain segmentation and tissue classification algorithm for T1-weighted MR images

Vikram Chalana, Lydia Ng, Larry Rystrom, James Gee, David Haynor

Proceedings Volume 4322, (2001) https://doi.org/10.1117/12.431079

KEYWORDS: Brain, Image segmentation, Tissues, Neuroimaging, Image processing algorithms and systems, Magnetic resonance imaging, Algorithm development, Image registration, Image classification, Head

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Proceedings Article | 3 July 2001 Paper

Nonrigid multimodality image registration

David Mattes, David Haynor, Hubert Vesselle, Thomas Lewellyn, William Eubank

Proceedings Volume 4322, (2001) https://doi.org/10.1117/12.431046

KEYWORDS: Image registration, Computed tomography, Positron emission tomography, Lung, Error analysis, Image resolution, Abdomen, Visualization, Image processing, Chest

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Proceedings Article | 6 June 2000 Paper

General-purpose software tool for serial segmentation of stacked images

Vikram Chalana, Michael Sannella, David Haynor

Proceedings Volume 3979, (2000) https://doi.org/10.1117/12.387675

KEYWORDS: Image segmentation, Image processing algorithms and systems, Detection and tracking algorithms, Algorithm development, Motion estimation, Brain, Image filtering, Medical imaging, Human-machine interfaces, 3D image processing

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Proceedings Article | 18 April 2000 Paper

Image-based registration of ultrasound and magnetic resonance images: a preliminary study

Niko Pagoulatos, David Haynor, Yongmin Kim

Proceedings Volume 3976, (2000) https://doi.org/10.1117/12.383037

KEYWORDS: Image registration, Magnetic resonance imaging, Surgery, Position sensors, Transmitters, Detection and tracking algorithms, Magnetism, Ultrasonography, Visualization, Medical imaging

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A number of surgical procedures are planned and executed based on medical images. Typically, x-ray computed tomography (CT) and magnetic resonance (MR) images are acquired preoperatively for diagnosis and surgical planning. In the operating room, execution of the surgical plan becomes feasible due to registration between preoperative images and surgical space where patient anatomy lies. In this paper, we present a new automatic algorithm where we use ultrasound (US) 2D B-mode images to register the preoperative MR image coordinate system with the surgical space which in our experiments is represented by the reference coordinate system of a DC magnetic position sensor. The position sensor is also used for tracking the position and orientation of the US images. Furthermore, we simulated patient anatomy by using custom-built phantoms. Our registration algorithm is a hybrid between fiducial- based and image-based registration algorithms. Initially, we perform a fiducial-based rigid-body registration between MR and position sensor space. Then, by changing various parameters of the rigid-body fiducial-based transformation, we produce an MR-sensor misregistration in order to simulate potential movements of the skin fiducials and/or the organs. The perturbed transformation serves as the initial estimate for the image-based registration algorithm, which uses normalized mutual information as a similarity measure, where one or more US images of the phantom are automatically matched with the MR image data set. By using the fiducial- based registration as the gold standard, we could compute the accuracy of the image-based registration algorithm in registering MR and sensor spaces. The registration error varied depending on the number of 2D US images used for registration. A good compromise between accuracy and computation time was the use of 3 US slices. In this case, the registration error had a mean value of 1.88 mm and standard deviation of 0.42 mm, whereas the required computation time was approximately 52 sec. Subsampling the US data by a factor of 4 X 4 and reducing the number of histogram bins to 128 reduced the computation time to approximately 6 sec. with a small increase in the registration error.

Proceedings Article | 26 May 1999 Paper

PC-based system for 3D registration of ultrasound and magnetic resonance images based on a magetic position sensor

Niko Pagoulatos, Warren Edwards, David Haynor, Yongmin Kim

Proceedings Volume 3658, (1999) https://doi.org/10.1117/12.349473

KEYWORDS: Image registration, Magnetic resonance imaging, Transmitters, Position sensors, Magnetism, Receivers, Transducers, Ultrasonography, Brain, 3D image processing

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Proceedings Article | 26 June 1998 Paper

Quantitative three-dimensional transrectal ultrasound (TRUS) for prostate imaging

Sayan Pathak, Rene Aarnink, Jean de la Rosette, Vikram Chalana, Hessel Wijkstra, David Haynor, Frans Debruyne, Yongmin Kim

Proceedings Volume 3335, (1998) https://doi.org/10.1117/12.312551

KEYWORDS: Prostate, Image segmentation, Ultrasonography, Image processing algorithms and systems, Image processing, Statistical analysis, 3D image processing, Speckle, Prostate cancer, Error analysis

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Proceedings Article | 26 June 1998 Paper

Calibration and validation of freehand 3D ultrasound systems based on DC magnetic tracking

Niko Pagoulatos, Warren Edwards, David Haynor, Yongmin Kim

Proceedings Volume 3335, (1998) https://doi.org/10.1117/12.312541

KEYWORDS: Calibration, Receivers, Ultrasonography, 3D image processing, Transmitters, Tin, Position sensors, Magnetic tracking, Sensors, Magnetism

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Proceedings Article | 24 June 1998 Paper

Unified data structures in a software environment for medical image segmentation

Vikram Chalana, Jennifer Hodgdon, David Haynor

Proceedings Volume 3338, (1998) https://doi.org/10.1117/12.310973

KEYWORDS: Image segmentation, Image processing algorithms and systems, Algorithm development, Medical imaging, Tissues, Computer architecture, Software development, Image classification, Databases, 3D modeling

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Proceedings Article | 7 May 1997 Paper

MedPlus: a medical image analysis and browsing environment

Vikram Chalana, Mysore Jaisimha, David Haynor, Emmanuel Arbogast

Proceedings Volume 3031, (1997) https://doi.org/10.1117/12.273925

KEYWORDS: Databases, Medical imaging, Image analysis, Image processing, Statistical analysis, Visualization, Analytical research, Head, Image visualization, Algorithm development

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Proceedings Article | 25 April 1997 Paper

Parameter estimation in deformable models using Markov chain Monte Carlo

Vikram Chalana, David Haynor, Paul Sampson, Yongmin Kim

Proceedings Volume 3034, (1997) https://doi.org/10.1117/12.274096

KEYWORDS: Expectation maximization algorithms, Monte Carlo methods, Statistical analysis, Data modeling, Statistical modeling, Image segmentation, Medical imaging, Mathematical modeling, Stochastic processes, Data analysis

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Proceedings Article | 25 April 1997 Paper

Bayes and medical imaging: it's time to make priors a priority

David Haynor

Proceedings Volume 3034, (1997) https://doi.org/10.1117/12.274075

KEYWORDS: Medical imaging, Image filtering, Image analysis, Data modeling, Computed tomography, Data processing, RGB color model, Gaussian filters, Information technology, Radiography

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Proceedings Article | 16 April 1996 Paper

Rapid coarse-to-fine matching using scale-specific priors

James Gee, David Haynor

Proceedings Volume 2710, (1996) https://doi.org/10.1117/12.237944

KEYWORDS: Image segmentation, Magnetic resonance imaging, Head, Thalamus, Brain, Information technology, Chemical elements, Neuroimaging, Data modeling, Infrared imaging

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Proceedings Article | 12 May 1995 Paper

Bayesian approach to the brain image matching problem

James Gee, Lionel Le Briquer, Christian Barillot, David Haynor, Ruzena Bajcsy

Proceedings Volume 2434, (1995) https://doi.org/10.1117/12.208686

KEYWORDS: Brain mapping, Brain, Associative arrays, Neuroimaging, Error analysis, Tissues, Data modeling, Information technology, Image classification, Infrared imaging

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Proceedings Article | 8 May 1995 Paper

Quality assurance for CT and MRI scanners

Alan Rowberg, David Haynor, Michael Denny, Marv Kleven, David Goodenough

Proceedings Volume 2432, (1995) https://doi.org/10.1117/12.208375

KEYWORDS: Scanners, Signal to noise ratio, Magnetic resonance imaging, Computed tomography, X-ray computed tomography, Radiology, Calibration, Image resolution, Image analysis, Computing systems

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Proceedings Article | 9 September 1994 Paper

Matching structural images of the human brain using statistical and geometrical image features

James Gee, Christian Barillot, Lionel Le Briquer, David Haynor, Ruzena Bajcsy

Proceedings Volume 2359, (1994) https://doi.org/10.1117/12.185179

KEYWORDS: Brain, Neuroimaging, Chemical elements, Tissues, Image registration, Image classification, Image fusion, Image resolution, Information technology, Image filtering

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Proceedings Article | 11 May 1994 Paper

Finite element approach to warping of brain images

James Gee, David Haynor, Martin Reivich, Ruzena Bajcsy

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175067

KEYWORDS: Chemical elements, Brain, Neuroimaging, Probability theory, Image processing, Information technology, 3D image processing, Finite element methods, Brain mapping, Magnetic resonance imaging

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Proceedings Article | 11 May 1994 Paper

Left-ventricular boundary detection from short-axis echocardiograms: the use of active contour models

Vikram Chalana, David Haynor, Yongmin Kim

Proceedings Volume 2167, (1994) https://doi.org/10.1117/12.175117

KEYWORDS: 3D modeling, Detection and tracking algorithms, 3D image processing, Heart, Edge detection, Ultrasonography, Image processing, Image filtering, Image segmentation, Digital filtering

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Proceedings Article | 1 April 1994 Paper

Design of a receiver operating characteristic (ROC) study of 10:1 lossy image compression

Cary Collins, David Lane, Mark Frank, Michael Hardy, David Haynor, Donald Smith, James Parker, Gregory Bender, Yongmin Kim

Proceedings Volume 2166, (1994) https://doi.org/10.1117/12.171736

KEYWORDS: Image compression, Pathology, Diagnostics, Digital imaging, Image quality, Bone, Receivers, Computing systems, Radiography, Medical imaging

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Proceedings Article | 8 September 1993 Paper

DDx: diagnostic assistance for the radiologist using PACS

David Haynor

Proceedings Volume 1899, (1993) https://doi.org/10.1117/12.152926

KEYWORDS: Diagnostics, Picture Archiving and Communication System, Databases, Information technology, Radiology, Brain, Image segmentation, Medical imaging, Information fusion, Statistical analysis

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Proceedings Article | 30 June 1993 Paper

X window image extension standard: what it means to medical imaging

James Fahy, Roland Masse, Yongmin Kim, David Haynor

Proceedings Volume 1897, (1993) https://doi.org/10.1117/12.146984

KEYWORDS: Image processing, Image enhancement, Picture Archiving and Communication System, Medical imaging, Surgery, Digital imaging, Convolution, Visualization, Image compression, Diagnostics

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Proceedings Article | 30 June 1993 Paper

Early evaluation of MDIS workstations at Madigan Army Medical Center

Robert Leckie, Fred Goeringer, Donald Smith, Gregory Bender, Hyung-Sik Choi, David Haynor, Yongmin Kim

Proceedings Volume 1897, (1993) https://doi.org/10.1117/12.146983

KEYWORDS: Picture Archiving and Communication System, Radiology, Image compression, Imaging systems, Chromium, Image quality, Databases, Diagnostics, Image retrieval, Nonimpact printing

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Proceedings Article | 1 July 1992 Paper

Layered approach to workstation design for medical image viewing

David Haynor, Gregory Zick, Marcus Heritage, Yongmin Kim

Proceedings Volume 1654, (1992) https://doi.org/10.1117/12.60298

KEYWORDS: Databases, Medical imaging, Human-machine interfaces, Image processing, Surgery, Picture Archiving and Communication System, Local area networks, Interfaces, Imaging systems, Magnetic resonance imaging

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Proceedings Article | 1 May 1992 Paper

Development of a prototypical PACS workstation based on the IBM RS6000 and the X window system

David Yee, David Haynor, Hyung-Sik Choi, Stuart Milton, Yongmin Kim

Proceedings Volume 1653, (1992) https://doi.org/10.1117/12.59513

KEYWORDS: Picture Archiving and Communication System, Image processing, Human-machine interfaces, Visualization, Software development, Image display, Convolution, Zoom lenses, Surgery, Medical imaging

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Proceedings Article | 1 May 1992 Paper

Three-dimensional distortion model for magnetic resonance images

Larry DeSoto, David Haynor, Yongmin Kim

Proceedings Volume 1653, (1992) https://doi.org/10.1117/12.59487

KEYWORDS: Magnetic resonance imaging, Distortion, 3D modeling, Magnetism, 3D image processing, 3D acquisition, Imaging systems, Databases, Surgery, Computed tomography

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SPIE Journal Paper | 1 October 1991

Lossy image compression for digital medical imaging systems

Paul Wilhelm, David Haynor, Yongmin Kim, Eve Riskin

OE, Vol. 30, Issue 10, (October 1991) https://doi.org/10.1117/12.10.1117/12.55970

KEYWORDS: Image compression, Fractal analysis, X-ray computed tomography, Picture Archiving and Communication System, X-rays, Medical imaging, Quantization, Head, X-ray imaging, Image analysis

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Proceedings Article | 1 August 1990 Paper

Clinical evaluation of PACS workstations: methodology and results

David Haynor, Suzanne Weghorst, Allan Saarinen, John Loop, Udo Schmiedl

Proceedings Volume 1234, (1990) https://doi.org/10.1117/12.19044

KEYWORDS: Picture Archiving and Communication System, Medical imaging, Databases, Diagnostics, Silicon, Human-machine interfaces, Imaging systems, Radiology, Magnetic resonance imaging, Image resolution

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Proceedings Article | 1 August 1990 Paper

Development of a prototype electronic alternator for DIN/PACS environment and its evaluation

Hwan-Soo Choi, Hyun Wook Park, David Haynor, Yongmin Kim

Proceedings Volume 1234, (1990) https://doi.org/10.1117/12.18965

KEYWORDS: Image processing, Human-machine interfaces, Picture Archiving and Communication System, Databases, Image quality, Prototyping, Chromium, Medical imaging, Sun, Diagnostics

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Proceedings Article | 1 August 1990 Paper

Clinical determinants of PACS acceptance

Allan Saarinen, Gayle Youngs, David Haynor, John Loop

Proceedings Volume 1234, (1990) https://doi.org/10.1117/12.19009

KEYWORDS: Picture Archiving and Communication System, Radiology, Computing systems, Imaging systems, Diagnostics, Surgery, Reliability, Image processing, Digital imaging, Medical research

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One of the key determinants influencing how successfully a radiology department can convert from a conventional film-based environment to an exclusively digital imaging environment may be how well referring physician members of the hospital staff who are not radiologists endorse this new system. The benefits of Picture Archive and Communication Systems (PACS) to radiologists are becoming widely accepted and documented; however, physicians who interact with the radiology department represent an important user group whose views on PACS are less well understood. The acceptance of PACS by referring physicians (clinicians) may be critical to the overall utility ofPACS as well as a major drivingforce behind why a hospitalpurchases PACS. The degree to which referring physicians support PACS may be dependent upon many factors. This study identifies several aspects through the administration and analysis ofa survey which improve PACS acceptance by nonradiology physicians. It appears the more patients a referring physician sends to the radiology department, the more time a physician spends traveling to andfrom thefllmflle room retrievingfllms, and, the more interested a referring physician is about computers, the higher his interest is in PACS. If a referring physician believes that PACS will save him or her time, will reduce the incidence oflostfilms, or will cause performance of radiology exams or generation of reports to be more efficient, the referring physician appears more likely to support PACS and to make the initial time investment necessary to learn how PACS equipment operates. The factors which cause referring physicians to support PACS are principally: (1) the elimination oflost, misplaced, and checked outfllms, and (2) the elimination oftrips to and from thefile room. The major distractions ofthe technology are: (1) system reliability, and (2) reduced diagnostic capability. While the high cost ofPACS is also a distraction, it is not the predominant concern.

Proceedings Article | 1 August 1990 Paper

PACS economics and the referring physician

Allan Saarinen, M. Wilson, Scott Iverson, David Haynor, John Loop

Proceedings Volume 1234, (1990) https://doi.org/10.1117/12.19007

KEYWORDS: Picture Archiving and Communication System, Radiology, Imaging systems, Medical imaging, Medicine, Surgery, Telecommunications, Systems modeling, Medical research, Statistical analysis

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Proceedings Article | 1 July 1990 Paper

Lossy image compression for digital medical imaging systems

Paul Wilhelm, David Haynor, Yongmin Kim, Alan Nelson, Eve Riskin

Proceedings Volume 1232, (1990) https://doi.org/10.1117/12.18867

KEYWORDS: Image compression, Medical imaging, X-rays, Fractal analysis, X-ray computed tomography, X-ray imaging, Quantization, Picture Archiving and Communication System, Control systems, Head

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Image compression at rates of 10:1 or greater could make PACS much more responsive and economically attractive. This paper describes a protocol for subjective and objective evaluation of the fidelity of compressed/decompressed images to the originals and presents the results ofits application to four representative and promising compression methods. The methods examined are predictive pruned tree-structured vector quantization, fractal compression, the discrete cosine transform with equal weighting of block bit allocation, and the discrete cosine transform with human visual system weighting of block bit allocation. Vector quantization is theoretically capable of producing the best compressed images, but has proven to be difficult to effectively implement. It has the advantage that it can reconstruct images quickly through a simple lookup table. Disadvantages are that codebook training is required, the method is computationally intensive, and achieving the optimum performance would require prohibitively long vector dimensions. Fractal compression is a relatively new compression technique, but has produced satisfactory results while being computationally simple. It is fast at both image compression and image reconstruction. Discrete cosine iransform techniques reproduce images well, but have traditionally been hampered by the need for intensive computing to compress and decompress images. A protocol was developed for side-by-side observer comparison of reconstructed images with originals. Three 1024 X 1024 CR (Computed Radiography) images and two 512 X 512 X-ray CT images were viewed at six bit rates (0.2, 0.4, 0.6, 0.9, 1.2, and 1.5 bpp for CR, and 1.0, 1.3, 1.6, 1.9, 2.2, 2.5 bpp for X-ray CT) by nine radiologists at the University of Washington Medical Center. The CR images were viewed on a Pixar II Megascan (2560 X 2048) monitor and the CT images on a Sony (1280 X 1024) monitor. The radiologists' subjective evaluations of image fidelity were compared to calculations of mean square error (MSE), normalized mean square error (NMSE), percentage mean square error (PMSE), and fractal normalized mean square error (FMSE) for each compression method and bit rate.

Showing 5 of 50 publications

Proceedings Volume Editor (4)

SPIE Conference Volume | 11 April 2013

Medical Imaging 2013: Image Processing

SPIE Conference Volume | 30 March 2012

Medical Imaging 2012: Image Processing

SPIE Conference Volume | 7 March 2011

Medical Imaging 2011: Image Processing

SPIE Conference Volume | 5 March 2010

Medical Imaging 2010: Image Processing

Conference Committee Involvement (45)

Image-Guided Procedures, Robotic Interventions, and Modeling

16 February 2025 | San Diego, California, United States

Image Processing

16 February 2025 | San Diego, California, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

19 February 2024 | San Diego, California, United States

Image Processing

19 February 2024 | San Diego, California, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

20 February 2023 | San Diego, California, United States

Image Processing

20 February 2023 | San Diego, California, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

22 February 2022 | San Diego, California, United States

Image Processing

20 February 2022 | San Diego, California, United States

Image Processing

15 February 2021 | Online Only, California, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

15 February 2021 | Online Only, California, United States

Image Processing

17 February 2020 | Houston, Texas, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

16 February 2020 | Houston, Texas, United States

Image Processing

19 February 2019 | San Diego, California, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

17 February 2019 | San Diego, California, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

12 February 2018 | Houston, Texas, United States

Image Processing

11 February 2018 | Houston, Texas, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

14 February 2017 | Orlando, Florida, United States

Image-Guided Procedures, Robotic Interventions, and Modeling Posters

14 February 2017 | Orlando, FL, United States

Image Processing

12 February 2017 | Orlando, Florida, United States

Image Processing Posters

12 February 2017 | Orlando, FL, United States

Image Processing

1 March 2016 | San Diego, California, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

28 February 2016 | San Diego, California, United States

Image Processing

24 February 2015 | Orlando, Florida, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

22 February 2015 | Orlando, Florida, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

18 February 2014 | San Diego, California, United States

Image Processing

16 February 2014 | San Diego, California, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

12 February 2013 | Lake Buena Vista (Orlando Area), Florida, United States

Image Processing

10 February 2013 | Lake Buena Vista (Orlando Area), Florida, United States

Image Processing

6 February 2012 | San Diego, California, United States

Image-Guided Procedures, Robotic Interventions, and Modeling

5 February 2012 | San Diego, California, United States

Image Processing

14 February 2011 | Lake Buena Vista (Orlando), Florida, United States

Visualization, Image-Guided Procedures, and Modeling

13 February 2011 | Lake Buena Vista (Orlando), Florida, United States

Image Processing

14 February 2010 | San Diego, California, United States

Visualization, Image-Guided Procedures, and Modeling

14 February 2010 | San Diego, California, United States

Image Processing

8 February 2009 | Lake Buena Vista (Orlando Area), Florida, United States

Visualization, Image-guided Procedures and Modeling

8 February 2009 | Lake Buena Vista (Orlando Area), Florida, United States

Visualization, Image-Guided Procedures, and Modeling

17 February 2008 | San Diego, California, United States

Image Processing

17 February 2008 | San Diego, California, United States

Image Processing

18 February 2007 | San Diego, CA, United States

Visualization and Image-Guided Procedures

18 February 2007 | San Diego, CA, United States

Image Processing

13 February 2006 | San Diego, California, United States

Visualization, Image-Guided Procedures, and Display

12 February 2006 | San Diego, California, United States

Visualization, Image-Guided Procedures, and Display

13 February 2005 | San Diego, California, United States

Visualization, Image-Guided Procedures, and Display

15 February 2004 | San Diego, California, United States

Visualization, Image-Guided Procedures and Display

16 February 2003 | San Diego, California, United States

Showing 5 of 45 Conference Committees

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