کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6863526 | 1439515 | 2018 | 55 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Structure convolutional extreme learning machine and case-based shape template for HCC nucleus segmentation
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Accurate segmentation of hepatocellular carcinoma (HCC) nuclei is of great importance in automatic pathologic diagnosis. This paper proposes structure convolutional extreme learning machine (SC-ELM) and case-based shape template (CBST) methods for HCC nucleus segmentation, which could tackle complex nucleus scenarios including adhesion or overlap. First, SC-ELM is developed for global segmentation of pathology images, which is used for coarse segmentation. Then, each connected region is considered as a nucleus clump and a probability model with three energy functions is proposed for contour refinement of nucleus clumps. Finally, for complex nucleus clumps, the CBST method combined with pixel-based classification is utilized for unclear or lost boundary inference. Experimentations with 127 liver pathology images demonstrate the performance advantages of our proposed method as compared with related work.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 312, 27 October 2018, Pages 9-26
Journal: Neurocomputing - Volume 312, 27 October 2018, Pages 9-26
نویسندگان
Siqi Li, Huiyan Jiang, Yu-dong Yao, Wenbo Pang, Qingjiao Sun, Li Kuang,