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This work concerns the development of a generalized framework for computer-aided diagnosis of medical images. The system is built to mimic human texture perception as texture has been shown to be an important feature for pathology discrimination in medical images. In particular, it was shown by Julesz that orientation, frequency and scale are important markers for texture discrimination. Consequently, this work focuses on the design of a feature extraction scheme which identifies these texture markers (in accordance to Juleszpsilas human texture perception model). To get a rich description of the space-localized texture elements, wavelet analysis is employed using a scale-invariant representation. A robust, multiscale texture analysis scheme is employed to quantify the texture characteristics of the image. Wavelet-domain graylevel cooccurrence matrices were implemented in a variety of directions in order to capture the orientation of such texture elements (which also offered semi-rotational invariance). To test the systempsilas performance, retinal, small bowel and mammogram images were used. 75 small bowel images were correctly classified at an average classification accuracy of 85%, 86 retinal images had an average classification accuracy of 82.2% and the mammogram lesions (54) were classified correctly 69% on average.