Pathological liver segmentation using stochastic resonance and cellular automata |
| |
| Affiliation: | 1. Qatar Robotic Surgery Centre, Qatar Science & Technology Park, Qatar;2. Department of Urology, Hamad General Hospital, Qatar;1. Universidad Técnica Federico Santa María, Av. España 1680, CP 110-V Valparaíso, Chile;2. Department of Computer Science, TU Dortmund University, Germany;1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China;2. School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510006, China;3. SYSU-CMU Shunde International Joint Research Institute, Shunde, Guangdong, China;4. Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510006, China;1. Faculty of Arts and Science, Kyushu University, 819-0395, Japan;2. Faculty of Information Science and Electrical Engineering, Kyushu University, Japan;1. Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China;2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China;1. Information and Communications Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan;2. Institute of Computer Science and Technology, Peking University, Beijing, China;3. Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan |
| |
| Abstract: | Liver segmentation continues to remain a major challenge, largely due to its intensity complexity with surrounding anatomical structures (stomach, kidney, and heart), high noise level and lack of contrast in pathological computed tomography data. In this paper, we present an approach to reconstructing the liver surface in low contrast computed tomography. The main contributions are: (1) a stochastic resonance based methodology in discrete cosine transform domain is developed to enhance the contrast of pathological liver images, (2) a new formulation is proposed to prevent the object boundary, resulted by cellular automata method, from leaking into the surrounding areas of similar intensity, and (3) a level-set method is suggested to generate intermediate segmentation contours from two segmented slices distantly located in a subject sequence. We have tested the algorithm on real datasets obtained from two sources, Hamad General Hospital and MICCAI Grand Challenge workshop. Both qualitative and quantitative evaluation performed on liver data show promising segmentation accuracy when compared with ground truth data reflecting the potential of the proposed method. |
| |
| Keywords: | CT Image segmentation Dynamic cellular automata Stochastic resonance Graph cut Liver Contrast enhancement Discrete Gabor transform |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
