Abstract
AbstractMulti-atlas-based segmentation (MAS) methods have demonstrated superior performance in the field of automatic image segmentation, and label fusion is an important part of MAS methods. In this paper, we propose a label fusion method that incorporates pixel greyscale probability information. The proposed method combines the advantages of label fusion methods based on sparse representation (SRLF) and weighted voting methods using patch similarity weights (PSWV) and introduces pixel greyscale probability information to improve the segmentation accuracy. We apply the proposed method to the segmentation of deep brain tissues in challenging 3D brain MR images from publicly available IBSR datasets, including images of the thalamus, hippocampus, caudate, putamen, pallidum and amygdala. The experimental results show that the proposed method has higher segmentation accuracy and robustness than the related methods. Compared with the state-of-the-art methods, the proposed method obtains the best putamen, pallidum and amygdala segmentation results and hippocampus and caudate segmentation results that are similar to those of the comparison methods.
Funder
National Natural Science Foundation of China
Program for New Century Excellent Talents in University
Publisher
Springer Science and Business Media LLC
Reference31 articles.
1. Ivana, D., Bart, G. & Wilfried, P. Mri segmentation of the human brain: challenges, methods, and applications. Computational and Mathematical Methods in Medicine. 1–23(2015).
2. Sandra, G. et al. A review on brain structures segmentation in magnetic resonance imaging. Artificial Intelligence in Medicine. 73, 45–69 (2016).
3. Fischl, B. Freesurfer. Neuroimage. 62, 774–781 (2012).
4. Patenaude, B., Smith, S. M., Kennedy, D. N. & Jenkinson, M. A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage. 56, 907–922 (2011).
5. Babalola, K. et al. An evaluation of four automatic methods of segmenting the subcortical structures in the brain. Neuroimage. 47, 1435–1447 (2009).
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献