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The purpose of the study is to ascertain the key feature subsets of hepatitis b virus (HBV) reactivation and establish classification prognosis models of HBV reactivation for primary liver carcinoma (PLC) patients after precise radiotherapy (RT). Genetic Algorithm (GA) is proposed to extract the key feature subsets of HBV reactivation from the initial feature sets of primary liver carcinoma. Bayes...
A multi step liver segmentation method is proposed by combining improved level set based method with texture analysis technique for computed tomography (CT) images in this work. The aiming of proposed algorithm is to overcome the segmentation problem which is caused by similar intensities between liver region and its neighboring tissues, also robust to the variations of shape and size within liver...
In this paper, we propose an automated liver segmentation method to overcome the challenging issue of similar intensities shared by liver and its surrounding tissues in low-contrast CT images. Our approach takes advantage of PET data to initialize the CT liver region of interest (ROI), and then applies anisotropic diffusion on the CT liver ROI to suppress the intensity values of adjacent structures...
In this paper, we propose an automated liver segmentation method to overcome the challenging issues of high degree of variations in liver shape / size and similar density distribution shared by the liver and its surrounding structures. To improve the performance of conventional statistical shape model for liver segmentation, in our method, the signed distance function is utilized so that the landmarks...
Automated liver segmentation is problematic due to variations in liver shape / size and because the liver has a similar density distribution to surrounding structures. We propose a method that: 1) utilizes iteratively constructed probabilistic liver and rib cage atlases, 2) conducts the Gaussian distribution analysis to avoid incorrectly classifying the irrelevant surrounding tissues as `liver region'...
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