An improved level set for liver segmentation and perfusion analysis in MRIs

Authors:
Gang Chen;Lixu Gu;Lijun Qian;Jianrong Xu
Affiliations:
Image Guided Surgery and Therapy Laboratory, Med-X Research Institute and Department of Computer Science, Shanghai Jiao Tong University, Shanghai, China;Image Guided Surgery and Therapy Laboratory, Med-X Research Institute and Department of Computer Science, Shanghai Jiao Tong University, Shanghai, China;Radiology Department, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China;Radiology Department, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
2009

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Abstract

Determining liver segmentation accurately from MRIs is the primary and crucial step for any automated liver perfusion analysis, which provides important information about the blood supply to the liver. Although implicit contour extraction methods, such as level set methods (LSMs) and active contours, are often used to segment livers, the results are not always satisfactory due to the presence of artifacts and low-gradient response on the liver boundary. In this paper, we propose a multiple-initialization, multiple-step LSM to overcome the leakage and over-segmentation problems. The multiple-initialization curves are first evolved separately using the fast marching methods and LSMs, which are then combined with a convex hull algorithm to obtain a rough liver contour. Finally, the contour is evolved again using global level set smoothing to determine a precise liver boundary. Experimental results on 12 abdominal MRI series showed that the proposed approach obtained better liver segmentation results, so that a refined liver perfusion curve without respiration affection can be obtained by using a modified chamfer matching algorithm and the perfusion curve is evaluated by radiologists.