Breast cancer is an abnormal growth of cells in the breast, usually in the inner lining of the milk ducts or lobules. It is currently the most common type of cancer in women in developed and developing countries. The number of women affected by breast cancer is gradually increasing and remains as a significant health concern. Researchers are continuously working to develop novel techniques to detect early stages of breast cancer. This book covers breast cancer detection, diagnosis, and treatment using different imaging modalities such as mammography, magnetic resonance imaging, computed tomography, positron emission tomography, ultrasonography, infrared imaging, and other modalities. The information and methodologies presented will be useful to researchers, doctors, teachers, and students in biomedical sciences, medical imaging, and engineering.
This book presents the established and currently researched diagnostic and therapeutic imaging techniques used for breast cancer. Section I is a review of the principles, applications, and recent advances of breast imaging modalities. Section II focuses on breast pathologies and presents the use of breast cancer subgross morphology parameters. Section III covers mammography. Section IV focuses on CAD techniques used in breast cancer detection. Section V is dedicated to breast ultrasound, and Section VI discusses the role of magnetic resonance imaging in breast imaging. Thermal imaging in breast cancer is the theme of Section VII, and Section VIII concludes with chapters on breast cancer treatment.
Since 2005, our research team has been developing automated techniques for carotid artery (CA) wall segmentation and
intima-media thickness (IMT) measurement. We developed a snake-based technique (which we named CULEX1,2), a
method based on an integrated approach of feature extraction, fitting, and classification (which we named CALEX3), and
a watershed transform based algorithm4. Each of the previous methods substantially consisted in two distinct stages: Stage-I - Automatic carotid artery detection. In this step, intelligent procedures were adopted to automatically
locate the CA in the image frame. Stage-II - CA wall segmentation and IMT measurement. In this second step, the CA distal (or far) wall is segmented in order to trace the lumen-intima (LI) and media-adventitia (MA) boundaries. The distance between
the LI/MA borders is the IMT estimation.
The aim of this paper is the description of a novel and completely automated technique for carotid artery segmentation
and IMT measurement based on an innovative multi-resolution approach.
High-resolution ultrasonography (HRUS) has potentialities in differential diagnosis between malignant and benign
thyroid lesions, but interpretative pitfalls remain and accuracy is still poor.
We developed an image processing technique for characterizing the intra-nodular vascularization of thyroid lesions.
Twenty nodules (ten malignant) were analyzed by 3-D contrast-enhanced ultrasound imaging.
The 3-D volumes were preprocessed and skeletonized. Seven vascular parameters were computed on the skeletons:
number of vascular trees (NT); vascular density (VD); number of branching nodes (or branching points) (NB); mean
vessel radius (MR); 2-D (DM) and 3-D (SOAM) tortuosity; and inflection count metric (ICM). Results showed that the
malignant nodules had higher values of NT (83.1 vs. 18.1), VD (00.4 vs. 0.01), NB (1453 vs. 552), DM (51 vs. 18), ICM
(19.9 vs. 8.7), and SOAM (26 vs. 11).
Quantification of nodular vascularization based on 3-D contrast-enhanced ultrasound and skeletonization could help
differential diagnosis of thyroid lesions.
The carotid intima-media thickness (IMT) is the most used marker for the progression of atherosclerosis and onset of the
cardiovascular diseases. Computer-aided measurements improve accuracy, but usually require user interaction.
In this paper we characterized a new and completely automated technique for carotid segmentation and IMT
measurement based on the merits of two previously developed techniques. We used an integrated approach of intelligent
image feature extraction and line fitting for automatically locating the carotid artery in the image frame, followed by
wall interfaces extraction based on Gaussian edge operator. We called our system - CARES.
We validated the CARES on a multi-institutional database of 300 carotid ultrasound images. IMT measurement bias
was 0.032 ± 0.141 mm, better than other automated techniques and comparable to that of user-driven methodologies.
Our novel approach of CARES processed 96% of the images leading to the figure of merit to be 95.7%. CARES ensured
complete automation and high accuracy in IMT measurement; hence it could be a suitable clinical tool for processing of
large datasets in multicenter studies involving atherosclerosis.pre-
Most of the algorithms for the common carotid artery (CCA) segmentation require human interaction. The aim of this
study is to show a novel accurate algorithm for the computer-based automated tracing of CCA in longitudinal B-Mode
ultrasound images.
One hundred ultrasound B-Mode longitudinal images of the CCA were processed to delineate the region of interest
containing the artery. The algorithm is based on geometric feature extraction, line fitting, and classification. Output of
the algorithm is the tracings of the near and far adventitia layers. Performance of the algorithm was validated against
human tracings (ground truth) and benchmarked with a previously developed automated technique.
Ninety-eight images were correctly processed, resulting in an overall system error (with respect to ground truth) equal to
0.18 ± 0.17 mm (near adventitia) and 0.17 ± 0.24 mm (far adventitia). In far adventitia detection, our novel technique
outperformed the current standard method, which showed overall system errors equal to 0.07 ± 0.07 mm and 0.49 ± 0.27
mm for near and far adventitia, respectively. We also showed that our new technique is quite insensitive to noise and has
performance independent on the subset of images used for training the classifiers.
Superior architecture of this methodology could constitute a general basis for the development of completely automatic
CCA segmentation strategies.
EM algorithm for the reconstruction of freehand B-Scan ultrasound image was developed by Joao M. Sanches
et al. The reconstruction has a parameter K which can be adjusted so that the results can be smoother or
sharper depending to the value of K. In order to make the image smoother inside the organs but sharper in their
boundaries simultaneously, we introduced a improved EM algorithm: EM algorithm with a diffusion filer or is
referred as EMD algorithm. There was a cubic average filter inside the loop of the iteration of the EM algorithm.
This average filter is replaced by a diffusion filter in the EMD algorithm. The diffusion filter offers an additional
parameter Kd which can be used to adjust the reconstructed image with better optimization in both smoothness
insider the human organ and sharpness in its boundary.
Two above mentioned reconstruction algorithms for the freehand B-scan ultrasound image are compared
through the simulation and the phantom measurements. In the simulation, strong noises are added to the
ultrasound frame data. The parameters of two algorithms are optimized to get smallest errors. The errors are
compared between two algorithms with optimized parameters.
For the measurement with phantom, the Eigen's tracker system is used to continuously measure the coordinates
of the ultrasound probe. The ultrasound B-scan frame is synchronously recorded with the probe
coordinates. Zonare ultrasound machine is used to acquire the 2D frame images. The segmentation of the
reconstruction results is done. The segmentation volumes of the prostate phantoms are compared.
The results shows that EMD algorithm is better at reducing the noises and keeping the image edge comparing
to EM algorithm. Eigen's tracker is cacaple to acquire freehand ultrasound data for a 3D image reconstruction
with high quality.
Digital Subtraction Angiography (DSA) is a well-established powerful modality for the visualization of stenosis and blood vessels in general. This paper presents two novel approaches which address image quality. In the first approach we combine anisotropic diffusion with nonlinear normalization. The second approach consists of an introduction of a regularization strategy followed by a classification procedure to improve the enhancement. The performances of two strategies are evaluated based on a database of 73 subjects using SNR, CNR and Tenengrad's metric. Compared with conventional DSA, Eigen's diffusion embedded nonlinear enhancement strategies can improve image quality 95.25% in terms of SNR. The regularization embedded linear enhancement strategy can also improve SNR 51.46% compared with conventional DSA. Similar results are obtained by CNR and Tenengrad's metric measurements. Our system runs on a PC-based workstation using C++ in Windows environment.
KEYWORDS: Biopsy, Prostate, Cancer, Image segmentation, 3D image processing, Ultrasonography, Image registration, 3D modeling, 3D acquisition, Prostate cancer
Prostate cancer is a multifocal disease and lesions are not distributed uniformly within the gland. Several biopsy
protocols concerning spatially specific targeting have been reported urology literature. Recently a statistical
cancer atlas of the prostate was constructed providing voxelwise probabilities of cancers in the prostate. Additionally
an optimized set of biopsy sites was computed with 94 - 96% detection accuracy was reported using only 6-7 needles. Here we discuss the warping of this atlas to prostate segmented side-fire ultrasound images of the patient. A shape model was used to speed up registration. The model was trained from over 38 expert segmented subjects off-line. This training yielded as few as 15-20 degrees of freedom that were optimized to warp the atlas surface to the patient's ultrasound image followed by elastic interpolation of the 3-D atlas. As a result the atlas is completely mapped to the patient's prostate anatomy along with optimal predetermined needle locations for biopsy. These do not preclude the use of additional biopsies if desired. A color overlay of the atlas is also displayed on the ultrasound image showing high cancer zones within the prostate. Finally current biopsy locations are saved in the atlas space and may be used to update the atlas based on the pathology report. In addition to the optimal atlas plan, previous biopsy locations and alternate plans can also be stored in the atlas space and warped to the patient with no additional time overhead.
KEYWORDS: Prostate, Image segmentation, 3D image processing, Ultrasonography, Biopsy, 3D acquisition, Error analysis, Image processing algorithms and systems, 3D image reconstruction, Prostate cancer
Prostate volume is an indirect indicator for several prostate diseases. Volume estimation is a desired requirement during
prostate biopsy, therapy and clinical follow up. Image segmentation is thus necessary. Previously, discrete dynamic contour (DDC) was implemented in orthogonal unidirectional on the slice-by-slice basis
for prostate boundary estimation. This suffered from the glitch that it needed stopping criteria during the propagation of
segmentation procedure from slice-to-slice. To overcome this glitch, axial DDC was implemented and this suffered from the fact that central axis never remains fixed and wobbles during propagation of segmentation from slice-to-slice. The effect of this was a multi-fold reconstructed surface. This paper presents a bidirectional DDC approach, thereby removing the two glitches. Our bidirectional DDC protocol was tested on a clinical dataset on 28 3-D ultrasound image volumes acquired using side fire Philips transrectal ultrasound. We demonstrate the orthogonal bidirectional DDC strategy achieved the most accurate volume estimation compared with previously published orthogonal unidirectional DDC and axial DDC methods. Compared to the ground truth, we show that the mean volume estimation errors were: 18.48%, 9.21% and 7.82% for unidirectional, axial and bidirectional DDC methods, respectively. The segmentation architecture is implemented in Visual C++ in Windows environment.
Prostate cancer is the most commonly diagnosed cancer in males in the United States and the second leading
cause of cancer death. While the exact cause is still under investigation, researchers agree on certain risk factors
like age, family history, dietary habits, lifestyle and race. It is also widely accepted that cancer distribution
within the prostate is inhomogeneous, i.e. certain regions have a higher likelihood of developing cancer. In
this regard extensive work has been done to study the distribution of cancer in order to perform biopsy more
effectively. Recently a statistical cancer atlas of the prostate was demonstrated along with an optimal biopsy
scheme achieving a high detection rate.
In this paper we discuss the complete construction and application of such an atlas that can be used in a
clinical setting to effectively target high cancer zones during biopsy. The method consists of integrating intensity
statistics in the form of cancer probabilities at every voxel in the image with shape statistics of the prostate in
order to quickly warp the atlas onto a subject ultrasound image. While the atlas surface can be registered to a
pre-segmented subject prostate surface or instead used to perform segmentation of the capsule via optimization
of shape parameters to segment the subject image, the strength of our approach lies in the fast mapping of cancer
statistics onto the subject using shape statistics. The shape model was trained from over 38 expert segmented
prostate surfaces and the atlas registration accuracy was found to be high suggesting the use of this method to
perform biopsy in near real time situations with some optimization.
Prostate repeat biopsy has become one of the key requirements in today's prostate cancer detection. Urologists are
interested in knowing previous 3-D biopsy locations during the current visit of the patient. Eigen has developed a system
for performing 3-D Ultrasound image guided prostate biopsy. The repeat biopsy tool consists of three stages: (1)
segmentation of the prostate capsules from previous and current ultrasound volumes; (2) registration of segmented
surfaces using adaptive focus deformable model; (3) mapping of old biopsy sites onto new volume via thin-plate splines
(TPS). The system critically depends on accurate 3-D segmentation of capsule volumes. In this paper, we study the
effect of automated segmentation technique on the accuracy of 3-D ultrasound guided repeat biopsy. Our database
consists of 38 prostate volumes of different patients which are acquired using Philips sidefire transrectal ultrasound
(TRUS) probe. The prostate volumes were segmented in three ways: expert segmentation, semi-automated segmentation,
and fully automated segmentation. New biopsy sites were identified in the new volumes from different segmentation
methods, and we compared the mean squared distance between biopsy sites. It is demonstrated that the performance of
our fully automated segmentation tool is comparable to that of semi-automated segmentation method.
Real-time knowledge of capsule volume of an organ provides a valuable clinical tool for 3D biopsy applications. It is
challenging to estimate this capsule volume in real-time due to the presence of speckles, shadow artifacts, partial volume
effect and patient motion during image scans, which are all inherent in medical ultrasound imaging.
The volumetric ultrasound prostate images are sliced in a rotational manner every three degrees. The automated
segmentation method employs a shape model, which is obtained from training data, to delineate the middle slices of
volumetric prostate images. Then a "DDC" algorithm is applied to the rest of the images with the initial contour
obtained. The volume of prostate is estimated with the segmentation results.
Our database consists of 36 prostate volumes which are acquired using a Philips ultrasound machine using a Side-fire
transrectal ultrasound (TRUS) probe. We compare our automated method with the semi-automated approach. The mean
volumes using the semi-automated and complete automated techniques were 35.16 cc and 34.86 cc, with the error of
7.3% and 7.6% compared to the volume obtained by the human estimated boundary (ideal boundary), respectively. The
overall system, which was developed using Microsoft Visual C++, is real-time and accurate.
DSA images suffer from challenges like system X-ray noise and artifacts due to patient movement. In this paper, we present a two-step strategy to improve DSA image quality. First, a hierarchical deformable registration algorithm is used to register the mask frame and the bolus frame before subtraction. Second, the resulted DSA image is further enhanced by background diffusion and nonlinear normalization for better visualization. Two major changes are made in the hierarchical deformable registration algorithm for DSA images: 1) B-Spline is used to represent the deformation field in order to produce the smooth deformation field; 2) two features are defined as the attribute vector for each point in the image, i.e., original image intensity and gradient. Also, for speeding up the 2D
image registration, the hierarchical motion compensation algorithm is implemented by a multi-resolution framework. The proposed method has been evaluated on a database of 73 subjects by quantitatively measuring signal-to-noise (SNR) ratio. DSA embedded with proposed strategies demonstrates an improvement of 74.1% over conventional DSA in terms of SNR. Our system runs on Eigen's DSA workstation using C++ in Windows environment.
Breast cancer is the most common type of cancer found in women worldwide; approximately 10% of women are confronted with breast cancer in their lives. Breast cancer can be most efficiently treated if detected at an early stage. This book focuses primarily on the application of computer vision for early lesion identification in mammograms and breast-imaging volumes through computer-aided diagnostics (CAD). Color illustrations are included in the text, and an accompanying CD-ROM contains other full-color images.
The book is divided into four parts. In Part I, the anatomic, histopathologic, and mammographic views of the breast are examined, and the physics for different breast-imaging modalities are presented. Part II presents techniques for lesion and mass detection, and computer-aided diagnosis. Part III discusses the applications of different computer-vision fields in breast-image registration, and performance evaluation for breast CAD techniques is discussed in Part IV.
KEYWORDS: Functional magnetic resonance imaging, Image registration, Statistical analysis, Image processing, Brain, Computer simulations, Monte Carlo methods, Neuroimaging, Magnetic resonance imaging, Signal attenuation
During functional magnetic resonance imaging (fMRI) brain examinations, the signal extraction from a large number of images is used to evaluate changes in blood oxygenation levels by applying statistical methodology. Image registration is essential as it assists in providing accurate fractional positioning accomplished by using interpolation between sequentially acquired fMRI images. Unfortunately, current subvoxel registration methods found in standard software may produce significant bias in the variance estimator when interpolating with fractional, spatial voxel shifts. It was found that interpolation schemes, as currently applied during the registration of functional brain images, could introduce statistical bias, but there is a possible correction scheme. This bias was shown to result from the "weighted-averaging" process employed by conventional implementation of interpolation schemes. The most severe consequence of inaccurate variance estimators is the undesirable violation of the fundamental 'stationary' assumption required for many statistical methods and Gaussian random field analysis. Thus, this bias violates assumptions of the general linear model (GLM) and/or t-tests commonly used in fMRI studies. Using simulated data as well as actual human data in this, it was demonstrated that this artifact can significantly alter the magnitude and location of the resulting activation patterns/results. Further, the work detailed here introduces a bias correction scheme and evaluates the improved accuracy of its sample variance calculation and influence on fMRI results through comparison with traditional fMRI image registered data.
It has been recently established that fusion of multi-modalities has led to better diagnostic capability and increased sensitivity and specificity. Fischer has been developing fused full-field digital mammography and ultrasound (FFDMUS) system. In FFDMUS, two sets of acquisitions are performed: 2-D X-ray and 3-D ultrasound. The segmentation of acquired lesions in phantom images is important: (1) to assess the image quality of X-ray and ultrasound images; (2) to register multi-modality images, and (3) to establish an automatic lesion detection methodology to assist the radiologist. In this paper, we studied the effect of PDE-based smoother on the gradient vector flow (GVF)-based active contour model for breast lesion detection. CIRS X-ray phantom images were acquired using FFDMUS, and region of interest (ROI) samples were extracted. PDE-based smoother was implemented to generate noise free images. The GVF-based strategy was then implemented on these noise free samples. Initial contours were set as default, and GVF snake then converged to extract lesion topology. The performance index was calculated by computing the difference between estimated lesion area and ideal lesion area. Our performance index with GVF (without PDE smoothing) yielded an average percentage error of 10.32%, while GVF with PDE yielded an average error of 9.61%, an improvement of 7%. We also optimized our PDE smoother for least GVF error estimation, and to our observation, we found the optimal number of iteration was 140. We also tested our program written in C++ on synthetic datasets.
We are involved in a comprehensive program to characterize atherosclerotic disease using multiple MR images having different contrast mechanisms (T1W, T2W, PDW, magnetization transfer, etc.) of human carotid and animal model arteries. We use specially designed intravascular and surface array coils that give high signal-to-noise but suffer from sensitivity inhomogeneity. With carotid surface coils, challenges include: (1) a steep bias field with an 80% change; (2) presence of nearby muscular structures lacking high frequency information to distinguish bias from anatomical features; (3) many confounding zero-valued voxels subject to fat suppression, blood flow cancellation, or air, which are not subject to coil sensitivity; and (4) substantial noise. Bias was corrected using a modification of the adaptive fuzzy c-mean method reported by Pham et al. (IEEE TMI, 18:738-752), whereby a bias field modeled as a mechanical membrane was iteratively improved until cluster means no longer changed. Because our images were noisy, we added a noise reduction filtering step between iterations and used about 5 classes. In a digital phantom having a bias field measured from our MR system, variations across an area comparable to a carotid artery were reduced from 50% to <5% with processing. Human carotid images were qualitatively improved and large regions of skeletal muscle were relatively flat. Other commonly applied techniques failed to segment the images or introduced strong edge artifacts. Current evaluations include comparisons to bias as measured by a body coil in human MR images.
This paper describes techniques to visualize aneurysms in three dimensions from Magnetic Resonance Angiographic data sets to aid surgeons and radiologists in surgical planning and treatment of cerebrovascular (brain) aneurysms. Maximum Intensity Projection using ray tracing is implemented to highlight the aneurysm zones in 2d. Segmentation Via Voxel Connectivity (SVVC) supports the recognition of the blood vessels and aneurysms and presents them in 3d.
KEYWORDS: Bone, Fractal analysis, Radiography, Binary data, Biostereometrics, Image processing, Technologies and applications, Simulation of CCA and DLA aggregates, X-rays, Databases
Atechnique is proposed which uses fractal analysis for the non- traumatic and non-invasive quantification of trabecular bone density in the mandible using standard dental radiographs. Binary images of trabecular bone patterns are derived from digitized radiographic images. Fractal analysis is then used to calculate the Hausdorif dimension (D) of the binary image patterns. Variations in D calculated with this method can be correlated with known cases of systemic osteoporosis to establish normal and abnormal ranges for the value of D.
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