The growth patterns of mammary fat pads and glandular tissues inside the fat pads may be related with the risk factors of breast cancer.Quantitative measurements of this relationship are available after segmentation o...The growth patterns of mammary fat pads and glandular tissues inside the fat pads may be related with the risk factors of breast cancer.Quantitative measurements of this relationship are available after segmentation of mammary pads and glandular tissues.Rat 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 slices.First,each image is decomposed into cartoon image and texture image based on cartoon-texture model.They will be used as smooth image and feature image for segmentation and for targeting pad seeds,respectively.Then,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 pad.The experimental results demonstrate the utility of this approach in accurate delineation of various numbers of mammary pads from several sets of MRI images.展开更多
Currently, the processing speed of exist-ing autormtic liver segmentation for Magnetic Res-onance Imaging （MRI） images is rehtively slow. An automatic liver segmentation scheme for MRI irmges based on Cellular Neura...Currently, the processing speed of exist-ing autormtic liver segmentation for Magnetic Res-onance Imaging （MRI） images is rehtively slow. An automatic liver segmentation scheme for MRI irmges based on Cellular Neural Networks （CNN） is presented in this paper. It ensures the validity of this scheme and at the same time completes the im-age segmentation faster to accurately calculate the liver volume by using parallel computing in real time. In order to facilitate the CNN irmge process-hag, firstly, three-dimensional liver MRI images should be transformed into binary images; second- ly, an appropriate template parameter of the Global Connectivity Detection CNN （GCD CNN） shall be selected to probe the connectivity of the liver to extract the entire liver; and then the Hole-Filler CNN （HF CNN） are used to repair the entire extracting liver and improve the accuracy of fiver segmentation; final-ly, the liver volume is obtained. Results show that the scheme can ensure the accuracy of the automatic seg-mentation of the liver, and it can also improve the processing speed at the same time. The liver volume calculated is in line with the clinical diagnosis.展开更多
基金Supported by National Basic Research Program of China (No.2003CB716103)partially supported by the US Army Breast Cancer Research Program (DAMD17-03-1-0446)
文摘The growth patterns of mammary fat pads and glandular tissues inside the fat pads may be related with the risk factors of breast cancer.Quantitative measurements of this relationship are available after segmentation of mammary pads and glandular tissues.Rat 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 slices.First,each image is decomposed into cartoon image and texture image based on cartoon-texture model.They will be used as smooth image and feature image for segmentation and for targeting pad seeds,respectively.Then,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 pad.The experimental results demonstrate the utility of this approach in accurate delineation of various numbers of mammary pads from several sets of MRI images.
基金supported by the National Natural Science Foundation of China under Grant No. 61074192the Funds of the USTB under Grants No. YJ2010-019,No.06108104
文摘Currently, the processing speed of exist-ing autormtic liver segmentation for Magnetic Res-onance Imaging （MRI） images is rehtively slow. An automatic liver segmentation scheme for MRI irmges based on Cellular Neural Networks （CNN） is presented in this paper. It ensures the validity of this scheme and at the same time completes the im-age segmentation faster to accurately calculate the liver volume by using parallel computing in real time. In order to facilitate the CNN irmge process-hag, firstly, three-dimensional liver MRI images should be transformed into binary images; second- ly, an appropriate template parameter of the Global Connectivity Detection CNN （GCD CNN） shall be selected to probe the connectivity of the liver to extract the entire liver; and then the Hole-Filler CNN （HF CNN） are used to repair the entire extracting liver and improve the accuracy of fiver segmentation; final-ly, the liver volume is obtained. Results show that the scheme can ensure the accuracy of the automatic seg-mentation of the liver, and it can also improve the processing speed at the same time. The liver volume calculated is in line with the clinical diagnosis.
