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Automatic Tracing and segmentation of Rat Mammary Fat Pads in MRI Image Sequences Based on Cartoon-Texture Model

Transactions of Tianjin University 2009年第3期15卷 229-235页

作者：涂圣贤张素陈亚珠 Freedman Matthew T WANG Bin XUAN Jason WANG Yue Department of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Oncology Lombardi Comprehensive Cancer Center Georgetown University Washington DC 20057 USA Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington VA 22203 USA

The growth patterns of mammary fat pads and glandular tissues inside the fat pads may be related with the risk factors of breast *** measurements of this relationship are available after segmentation of mammary pads and glandular *** fat pads may lose continuity along image sequences or adjoin similar intensity areas like epidermis and subcutaneous regions.A new approach for automatic tracing and segmentation of fat pads in magnetic resonance imaging(MRI) image sequences is presented,which does not require that the number of pads be constant or the spatial location of pads be adjacent among image ***,each image is decomposed into cartoon image and texture image based on cartoon-texture *** will be used as smooth image and feature image for segmentation and for targeting pad seeds,***,two-phase direct energy segmentation based on Chan-Vese active contour model is applied to partitioning the cartoon image into a set of regions,from which the pad boundary is traced iteratively from the pad seed.A tracing algorithm based on scanning order is proposed to accurately trace the pad boundary,which effectively removes the epidermis attached to the pad without any post processing as well as solves the problem of over-segmentation of some small holes inside the *** experimental results demonstrate the utility of this approach in accurate delineation of various numbers of mammary pads from several sets of MRI images.

关键词： active contours cartoon-texture model tracing boundary sequential images segmentation

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