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AUTOMATIC BRAIN TUMOR DETECTION IN MAGNETIC RESONANCE IMAGES is a difficult task due to variations in site, size, location and shape of tumors. In this paper, a multi-modality framework for automatic tumor detection is presented. The intensity, shape deformation, symmetry, and texture features were extracted from each image. The Ada Boost classifier was used to select the most discriminating features and to segment the tumor region.
Image retrieval based on color, texture and shape is an emerging and wide area of research scope. In this paper we present a novel framework for combining all the three i.e. color, texture and shape information, and achieve higher retrieval efficiency using dominant color feature. The image and its complement are partitioned into non-overlapping tiles of equal size. The features drawn from conditional co-occurrence histograms between the image tiles and corresponding complement tiles, in RGB color space, serve as local descriptors of color, shape and texture.