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Barrett's esophagus (BE) and associated adenocarcinoma have emerged as a major health care problem over the last two decades. Because of the widespread use of endoscopy, BE is being recognized increasingly in all Western countries. In clinical trials of endoscopic optical coherence tomography (EOCT), we defined certain image features that appear to be characteristic of precancerous (dysplastic) mucosa: decreased scattering and disorganization in the microscopic morphology. The objective of the present work is to develop computer-aided diagnosis (CAD) algorithms that aid the detection of dysplasia in BE. The image dataset used in the present study was derived from a total of 405 EOCT images (13 patients) that were paired with highly correlated histologic sections of corresponding biopsies. Of these, 106 images were included in the study. The CAD algorithm used was based on a standard texture analysis method (center-symmetric auto-correlation). Using histology as the reference standard, this CAD algorithm had a sensitivity of 82%, specificity of 74%, and accuracy of 83%. CAD has the potential to quantify and standardize the diagnosis of dysplasia and allows high throughput image evaluation for EOCT screening applications. With further refinements, CAD could also improve the accuracy of EOCT identification of dysplasia in BE.

Automated image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of breast cancer. Automated segmentation of the cells comprising imaged tissue microarrays (TMA) is a prerequisite for any subsequent quantitative analysis. Unfortunately, crowding and overlapping of cells present significant challenges for most traditional segmentation algorithms. In this paper, we propose a novel algorithm which can reliably separate touching cells in hematoxylin stained breast TMA specimens which have been acquired using a standard RGB camera. The algorithm is composed of two steps. It begins with a fast, reliable object center localization approach which utilizes single-path voting followed by mean-shift clustering. Next, the contour of each cell is obtained using a level set algorithm based on an interactive model. We compared the experimental results with those reported in the most current literature. Finally, performance was evaluated by comparing the pixel-wise accuracy provided by human experts with that produced by the new automated segmentation algorithm. The method was systematically tested on 234 image patches exhibiting dense overlap and containing more than 2200 cells. It was also tested on whole slide images including blood smears and tissue microarrays containing thousands of cells. Since the voting step of the seed detection algorithm is well suited for parallelization, a parallel version of the algorithm was implemented using graphic processing units (GPU) which resulted in significant speed-up over the C/C++ implementation.

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