Abstract:The accurate segmentation of liver in medical Computed Tomography (CT) sequence images is important prerequisite for computer-assisted liver surgery. However, the presence of tissue lesions, the blurred or missing boundary and the adhesion between different organs/tissues poses great challenges to liver segmentation. To address these problems, this paper presents a semi-automatic segmentation method based on the sequential constraints of image sequences, and introduces further a multi-view information fusion method to achieve the accurate segmentation of the liver. One advantage of this approach is that it does not need extensive data collection and complicated prior training. The validation and comparison results on the Sliver07 public data show that the proposed method shows competitive performance, especially when there is liver tumor, blurred or missing liver boundary.
彭佳林, 揭萍. 基于序列间先验约束和多视角信息融合的肝脏CT图像分割[J]. 电子与信息学报, 2018, 40(4): 971-978.
PENG Jialin, JIE Ping . Liver Segmentation from CT Image Based on Sequential Constraint and Multi-view Information Fusion. JEIT, 2018, 40(4): 971-978.
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