F-score Feature Selection Method May Improve Texture-based Liver Segmentation Strategies
A fast computer-aided liver segmentation plays a vital role in computer aided surgery (CAS), especially when using texture-based methods. Large amount of features yielded in supervised segmentation methods may result in slow segment processes. In order to reduce the time required in the segment stage, we applied principal component analysis (PCA), forward orthogonal search by maximizing the overall dependency (FOSMOD),and F-score to our supervised method proposed recently.Our results showed that the F-score may help in accelerating segment stage by approximately 42％ whilst the PCA-based feature selection method failed to extract the liver contour correctly.Though FOSMOD can obtain a good segmentation result of liver, it is time consuming comparing with the other two methods. Thus, F-score method may provide an effective solution for accelerating liver segmentation.
XU Yang LIU Jia HU Qingmao CHEN Zhijun DU Xiaohua HENG Pheng Ann
Center for Human-Computer Interaction Research,Shenzhen Institute of Advanced Integration Technology,Chinese Academy of Sciences/The Chinese University of Hong Kong,Shenzhen 518067 China
国际会议
厦门
英文
697-702
2008-12-12（万方平台首次上网日期，不代表论文的发表时间）