Learning Based Random Walks for Automatic Liver Segmentation in CT Image

摘要：

　　Liver segmentation from Computed Tomography (CT) image is important for the diagnosis and intervention of liver diseases.In this paper, we propose an automatic liver segmentation method based on probability image and random walks.First, pixel-level texture features are extracted and liver probability images are generated corresponding to the test images using a binary classification approach.Second, random walk algorithm with automatic seed points is developed to detect the liver region.The proposed method is validated on standard data with five evaluation criteria.Experimental results demonstrate the effectiveness and robustness of the proposed method for the liver segmentation in CT image.The proposed method can achieve an average volumetric overlap error of 8.76% and an average surface distance of 1.30 mm.

关键词： Automatic segmentation Liver Classification Random walks

作者: Pan Zhang Jian Yang Danni Ai Zhijie Xie Yue Liu

作者单位: Key Laboratory of Photoelectronic Imaging Technology and System,Ministry of Education of China, School of Optoelectronics,Beijing Institute of Technology, Beijing 100081, China

会议类型: 国际会议

会议名称: 10th Conference on Image and Graphics Technologies and Applications(第十届图像图形技术与应用学术会议)

会议地点: 北京

会议语种:英文

页码: 251-259

在线出版日期: 2015-06-19（万方平台首次上网日期，不代表论文的发表时间）