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A fast level set model of multi‐atlas labels fusion for 3D MRI tissues segmentation with application of split Bregman method
Author(s) -
Yang Yunyun,
Ruan Sichun,
Qin Xuxu,
Tian Dongcai
Publication year - 2018
Publication title -
mathematical methods in the applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 65
eISSN - 1099-1476
pISSN - 0170-4214
DOI - 10.1002/mma.5403
Subject(s) - segmentation , robustness (evolution) , artificial intelligence , fusion , level set (data structures) , energy functional , pattern recognition (psychology) , atlas (anatomy) , image segmentation , term (time) , computer science , image fusion , mathematics , scale space segmentation , computer vision , image (mathematics) , physics , mathematical analysis , paleontology , biochemistry , chemistry , linguistics , philosophy , quantum mechanics , biology , gene
In this paper, we propose a new fast level set model of multi‐atlas labels fusion for 3D magnetic resonance imaging (MRI) tissues segmentation. The proposed model is aimed at segmenting regions of interest in MR images, especially the tissues such as the amygdala, the caudate, the hippocampus, the pallidum, the putamen, and the thalamus. We first define a new energy functional by taking full advantage of an image data term, a length term, and a label fusion term. Different from using the region‐scalable fitting image data term and length term directly, we define a new image data term and a new length term, which is also incorporated with an edge detect function. By introducing a spatially weight function into the label fusion term, segmentation sensitivity to warped images can be largely improved. Furthermore, the special structure of the new energy functional ensures the application of the split Bregman method, which is a significant highlight and can improve segmentation efficiency of the proposed model. Because of these promotions, several good characters, such as accuracy, efficiency, and robustness have been exhibited in experimental results. Quantitative and qualitative comparisons with other methods have demonstrated the superior advantages of the proposed model.