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The automatic analysis of retinal blood vessels plays an important role in the computer-aided diagnosis. In this paper, we introduce a probabilistic tracking-based method for automatic vessel segmentation in retinal images. We take into account vessel edge detection on the whole retinal image and handle different vessel structures. During the tracking(More)
A Bayesian method with spatial constraint is proposed for vessel segmentation in retinal images. The proposed model makes the assumption that the posterior probability of each pixel is dependent on posterior probabilities of their neighboring pixels. An energy function is defined for the proposed model. By applying the modified level set approach to(More)
Various approaches have been proposed in the literature for texture characterization of images. Some of them are based on statistical properties, others on fractal measures and some more on multi-resolution analysis. Basically, these approaches have been applied on mono-band images. However, most of them have been extended by including the additional(More)
In this paper, we propose an automatic tracking method to extract blood vessels in retinal images. Seed points are firstly picked out on a retinal image for initialization. Our algorithm detects vessel edge points iteratively based on a statistical sampling model using a Bayesian method. At a given step, local vessel's sectional intensity profile is(More)
Linear structures such as blood vessels in medical images are important features for computer-aided diagnosis and follow-up of many diseases. In this letter a new tracking-based segmentation method is proposed to detect blood vessels in retinal images. Bayesian segmentation with the Maximum <i>a posteriori</i> (MAP) Probability criterion is used for that(More)
This paper deals with segmentation of breast anatomical regions, pectoral muscle, fatty and fibroglandular regions, using a Bayesian approach. This work is a part of a computer aided diagnosis project aiming at evaluating breast cancer risk and its association with characteristics (density, texture, etc.) of regions of interest on digitized mammograms.(More)
BACKGROUND The foveal avascular zone (FAZ) is known to enlarge in diabetic retinopathy. In a preliminary study, the authors applied a region growing algorithm to fluorescein angiograms to detect the FAZ in a semi-automated fashion. METHODS The FAZ in 44 fluorescein angiograms of 44 eyes of 41 patients with diabetic retinopathy underwent manual outlining,(More)