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The major limitations of precise evaluation of retinal structures in present clinical situations are the lack of standardization, the inherent subjectivity involved in the interpretation of retinal images, and intra- as well as interobserver variability. While evaluating optic disc deformation in glaucoma, these limitations could be overcome by using(More)
Automated segmentation and classification of diagnostic markers in medical imagery are challenging tasks. Numerous algorithms for segmentation and classification based on statistical approaches of varying complexity are found in the literature. However, the design of an efficient and automated algorithm for precise classification of desired diagnostic(More)
A modular, unsupervised neural network architecture that can be used for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns online in a stable and efficient manner. The system used a control structure similar to that found in the adaptive(More)
This report addresses the issues involved in developing a robust segmentation technique capable of finding the location and orientation of the cervical vertebrae in x-ray images. This technique should be invariant to rotation, scale, noise, occlusions and shape variability. A customized approach, based on the Generalized Hough transform (GHT), that captures(More)
This paper describes a system for surface recovery and visualization of the three-dimensional topography of the optic nerve head, as support of early diagnosis and follow up of glaucoma. In stereo vision, depth information is obtained from triangulation of corresponding points in a pair of stereo images. In this paper, the use of the cepstrum transformation(More)