WANG Xiao-peng,ZHANG Wen,CUI Ying.Tumor segmentation in lung CT images based on support vector machine and improved level set[J].Optoelectronics Letters,2015,11(5):395-400 Tumor segmentation in lung CT images based on support vector machine and improved level set Author Name Affiliation WANG Xiao-peng  School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China  ZHANG Wen  School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China  CUI Ying  School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China  Abstract:       In lung CT images, the edge of a tumor is frequently fuzzy because of the complex relationship between tumors and tissues, especially in cases that the tumor adheres to the chest and lung in the pathology area. This makes the tumor segmentation more difficult. In order to segment tumors in lung CT images accurately, a method based on support vector machine (SVM) and improved level set model is proposed. Firstly, the image is divided into several block units; then the texture, gray and shape features of each block are extracted to construct eigenvector and then the SVM classifier is trained to detect suspicious lung lesion areas; finally, the suspicious edge is extracted as the initial contour after optimizing lesion areas, and the complete tumor segmentation can be obtained by level set model modified with morphological gradient. Experimental results show that this method can efficiently and fast segment the tumors from complex lung CT images with higher accuracy. Hits: 3833 Download times: 0 This work has been supported by the National Natural Science Foundation of China (No.61261029) and Jinchuan Company Research Foundation View Full Text    Download reader