کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
468272 698209 2015 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Supervised recursive segmentation of volumetric CT images for 3D reconstruction of lung and vessel tree
ترجمه فارسی عنوان
تقسیم بندی بازگشتی تحت کنترل از تصاویر CT حجمی برای بازسازی 3D درخت ریه و رگ
کلمات کلیدی
ریه؛ تقسیم بندی شبه سه بعدی تحت نظارت؛ تصاویر سیگنال حجمی؛ بازسازی سه بعدی؛ روش Isosurface
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
چکیده انگلیسی


• This manuscript proposes a novel recursive strategy based on geometric active contour model to extract lung tissues from the volumetric CT slices accurately.
• The proposed method could settle the challenging task of separating left and right lungs.
• Non-sheltered 3D models of lung and vessels tree are constructed based on the extracted datasets.
• The proposed method is validated by fifteen scans with good performance.

Three dimensional reconstruction of lung and vessel tree has great significance to 3D observation and quantitative analysis for lung diseases. This paper presents non-sheltered 3D models of lung and vessel tree based on a supervised semi-3D lung tissues segmentation method. A recursive strategy based on geometric active contour is proposed instead of the “coarse-to-fine” framework in existing literature to extract lung tissues from the volumetric CT slices. In this model, the segmentation of the current slice is supervised by the result of the previous one slice due to the slight changes between adjacent slice of lung tissues. Through this mechanism, lung tissues in all the slices are segmented fast and accurately. The serious problems of left and right lungs fusion, caused by partial volume effects, and segmentation of pleural nodules can be settled meanwhile during the semi-3D process. The proposed scheme is evaluated by fifteen scans, from eight healthy participants and seven participants suffering from early-stage lung tumors. The results validate the good performance of the proposed method compared with the “coarse-to-fine” framework. The segmented datasets are utilized to reconstruct the non-sheltered 3D models of lung and vessel tree.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Methods and Programs in Biomedicine - Volume 122, Issue 3, December 2015, Pages 316–329
نویسندگان
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