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 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.
