A Multi-Atlas Segmentation Algorithm with An Improved Sparse Representation on Brain MR Images

A Multi-Atlas Segmentation Algorithm with An Improved Sparse Representation on Brain MR Images

Macaque brains are very close to human brains, so it’s an effective way to deepen the understanding of human brain functions by studying macaque brain structures. In order to segment subcortical nuclei of macaque brains more accurately, a multi-atlas segmentation algorithm based on an improved sparse representation has been designed in this paper. Firstly, a type of labeling information for atlas brain images is introduced when sparse patch-based representation is constructed, and then mutual information is improved by changing the calculation method of the information entropy, and it is used to measure the similarity between the target image and the atlas images. These two make the weights of the atlas more reasonable during fusion. Secondly, in order to fuse the segmentation results from two methods, nonlocal-patch-weighted method and the sparse representation method, a new similarity index based on a combination of dice coefficient and cosine distance is proposed. Finally, the experimental results show that this algorithm proposed in this paper has improved the accuracy of segmentation of hippocampus, striatum, claustrum and other nuclei, and it has better robustness.