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Abstract: Robust and effective optic disc detection is a necessary processing component in automatic retinal screening systems. In this paper, optic disc localization is achieved by a novel illumination correction operation, and contour segmentation is completed by a supervised gradient vector flow snake (SGVF snake) model. Conventional GVF snake is not sufficient to segment contour due to vessel occlusion and fuzzy disc boundaries. In view of this reason, the SGVF snake is extended in each time of deformation iteration, so that the contour points can be classified and updated according to their corresponding feature information. The classification relies on the feature vector extraction and the statistical information generated from training images. This approach is evaluated by means of two publicly available databases, Digital Retinal Images for Vessel Extraction (DRIVE) database and Structured Analysis of the Retina (STARE) database, of color retinal images. The experimental results show that the overall performance is with 95% correct optic disc localization from the two databases and 91% disc boundaries are correctly segmented by the SGVF snake algorithm. [Copyright &y& Elsevier]

Abstract: One of the issues when interpreting a mammogram is that the density of a pectoral muscle region is similar to the tumor cells. The appearance of pectoral muscle on medio-lateral oblique (MLO) views of mammograms will increase the false positives in computer aided detection (CAD) of breast cancer. For this reason, pectoral muscle has to be identified and segmented from the breast region in a mammogram before further analysis. The main goal of this paper is to propose an accurate and efficient algorithm of pectoral muscle extraction on MLO mammograms. The proposed algorithm is based on the positional characteristic of pectoral muscle in a breast region to combine the iterative Otsu thresholding scheme and the mathematic morphological processing to find a rough border of the pectoral muscle. The multiple regression analysis (MRA) is then employed on this rough border to obtain an accurate segmentation of the pectoral muscle. The presented algorithm is tested on the digital mammograms from the Mammogram Image Analysis Society (MIAS) database. The experimental results show that the pectoral muscle extracted by the presented algorithm approximately follows that extracted by an expert radiologist. [Copyright &y& Elsevier]