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.
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.