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
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Structure convolutional extreme learning machine and case-based shape template for HCC nucleus segmentation
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
نویسندگان
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