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This paper presents a segmentation methodology of abdominal axial CT images. The aim of the study is to determine the location of mesenteric area from the axial images so the organs enclosed within can be localized precisely for diagnostic purposes. The challenge confronted here is that there is no a certain deterministic shape of abdominal organs. The methodology implemented here utilizes a curvelets...
This study presents an efficient saliency model mainly aiming at content-based applications such as salient object segmentation. The input colour image is first pre-segmented into a set of regions using the mean shift algorithm. A set of Gaussian models are estimated on the basis of segmented regions, and then for each pixel, a set of normalised colour likelihood measures to different Gaussian models...
A technique for map image segmentation is presented. By combining a thresholding method which is fast and easy to implement and fuzzy rules which can deal with uncertain or ambiguous data, the technique presented outperforms the commonly used adaptive thresholding method.<<ETX>>
A novel MR (magnetic resonance) image segmentation algorithm has been implanted for tissue classification. The original image is first split into two subimages to minimize a specified cost function. Each image is then split into two smaller subimages and this procedure is repeated for several levels. Finally subimages are merged into desired number of groups according to a measure of the distance...
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