کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6920697 1447928 2018 35 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Ground-glass nodule segmentation in chest CT images using asymmetric multi-phase deformable model and pulmonary vessel removal
ترجمه فارسی عنوان
تقسیم گره های خاکستری شیشه ای در تصاویر سی تی اسکن قفسه سینه با استفاده از مدل غیر قابل تغییر شکل چند فازی و حذف رگ ریوی
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
We propose a ground-glass nodule (GGN) segmentation method that can separate solid component and ground-glass opacity (GGO) using an asymmetric multi-phase deformable model in chest CT images. First, initial solid component and GGO were extracted using intensity-based segmentation with histogram modeling. Second, the initial extracted regions were refined using an asymmetric multi-phase deformable model with modified energy functional and intensity-constrained averaging function. Finally, vessel-like structures are removed based on multi-scale shape analysis. In experiments, the segmentation accuracy of the entire GGN was evaluated using datasets from SNUH and LIDC/IDRI. The average DSC values of Seoul National University Hospital (SNUH) and Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) were 0.85 ± 0.05 and 0.78 ± 0.07, respectively. The Pearson's correlation coefficient (r) between segmented volumes by the proposed method and manual segmentation was evaluated using SNUH dataset. The r of solid component, GGO, and entire GGN were 0.931, 0.875 and 0.907. Our experimental results show that the proposed method improves segmentation accuracy by applying the proposed asymmetric multiphase deformable model and pulmonary vessel removal.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Biology and Medicine - Volume 92, 1 January 2018, Pages 128-138
نویسندگان
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