One of the leading causes of mortality worldwide is liver cancer.The earlier the detection of hepatic tumors,the lower the mortality rate.This paper introduces a computer-aided diagnosis system to extract hepatic tumo...One of the leading causes of mortality worldwide is liver cancer.The earlier the detection of hepatic tumors,the lower the mortality rate.This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors.Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range,intensity values overlap between the liver and neighboring organs,high noise from computed tomography scanner,and large variance in tumors shapes.The proposed method consists of three main stages;liver segmentation using Fast Generalized Fuzzy C-Means,tumor segmentation using dynamic thresholding,and the tumor’s classification into malignant/benign using support vector machines classifier.The performance of the proposed system was evaluated using three liver benchmark datasets,which are MICCAI-Sliver07,LiTS17,and 3Dircadb.The proposed computer adided diagnosis system achieved an average accuracy of 96.75%,sensetivity of 96.38%,specificity of 95.20%and Dice similarity coefficient of 95.13%.展开更多
文摘One of the leading causes of mortality worldwide is liver cancer.The earlier the detection of hepatic tumors,the lower the mortality rate.This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors.Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range,intensity values overlap between the liver and neighboring organs,high noise from computed tomography scanner,and large variance in tumors shapes.The proposed method consists of three main stages;liver segmentation using Fast Generalized Fuzzy C-Means,tumor segmentation using dynamic thresholding,and the tumor’s classification into malignant/benign using support vector machines classifier.The performance of the proposed system was evaluated using three liver benchmark datasets,which are MICCAI-Sliver07,LiTS17,and 3Dircadb.The proposed computer adided diagnosis system achieved an average accuracy of 96.75%,sensetivity of 96.38%,specificity of 95.20%and Dice similarity coefficient of 95.13%.
