کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10127223 1645048 2019 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
MRI brain tumor segmentation based on texture features and kernel sparse coding
ترجمه فارسی عنوان
تقسیم تومور مغزی مغز بر اساس ویژگی های بافت و برنامه نویسی نهایی هسته
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی
An automatic brain tumor segmentation method based on texture feature and kernel sparse coding from FLAIR (fluid attenuated inversion recovery) contrast-enhanced MRIs (magnetic resonance imaging) is presented in this paper. First, the MRIs are pre-processed to reduce noise, enhance contrast and correct the intensity non-uniformity. Then sparse coding is performed on the first order and second order statistical eigenvector extracted from original MRIs which is a patch of 3 × 3 around the voxel. The kernel dictionary learning is used to extract the non-linear features to construct two adaptive dictionaries for healthy and pathologically tissues respectively. A kernel-clustering algorithm based on dictionary learning is developed to code the voxels, then the linear discrimination method is used to classify the target pixels. In the end, the flood-fill operation is used to improve the segmentation quality. The results demonstrate that the method based on kernel sparse coding has better capacity and higher segmentation accuracy with low computation cost.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biomedical Signal Processing and Control - Volume 47, January 2019, Pages 387-392
نویسندگان
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