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• W2893380225 abstract "The automated segmentation of liver and tumor from CT images is of great importance in medical diagnoses and clinical treatment. However, accurate and automatic segmentation of liver and tumor is generally complicated due to the complex anatomical structures and low contrast. This paper proposes a registration-based organ positioning (ROP) and joint segmentation method for liver and tumor segmentation from CT images. First, a ROP method is developed to obtain liver’s bounding box accurately and efficiently. Second, a joint segmentation method based on fuzzy c-means (FCM) and extreme learning machine (ELM) is designed to perform coarse liver segmentation. Third, the coarse segmentation is regarded as the initial contour of active contour model (ACM) to refine liver boundary by considering the topological information. Finally, tumor segmentation is performed using another ELM. Experiments on two datasets demonstrate the performance advantages of our proposed method compared with other related works." @default.
• W2893380225 created "2018-10-05" @default.
• W2893380225 creator A5012443982 @default.
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• W2893380225 date "2018-09-24" @default.
• W2893380225 modified "2023-10-15" @default.
• W2893380225 title "Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation" @default.
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• W2893380225 doi "https://doi.org/10.1155/2018/8536854" @default.
• W2893380225 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6174803" @default.
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