An improved label fusion approach with sparse patch-based representation for MRI brain image segmentation.

The multi-atlas patch-based label fusion LF method mainly focuses on the measurement of the patch similarity which is the comparison between the atlas patch and the target patch. To enhance the LF performance, the distribution probability about the target can be used during the LF process. Hence, we consider two LF schemes: in the first scheme, we keep the results of the interpolation so that we can obtain the labels of the atlas with discrete values between 0 and 1 instead of binary values in the label propagation. In doing so, each atlas can be treated as a probability atlas. Second, we introduce the distribution probability of the tissue to be segmented in the sparse patch-based LF process. Based on the probability of the tissue and sparse patch-based representation, we propose three different LF methods which are called LF-Method-1, LF-Method-2, and LF-Method-3. In addition, an automated estimation method about the distribution probability of the tissue is also proposed. To evaluate the accuracy of our proposed LF methods, the methods were compared with those of the nonlocal patch-based LF method Nonlocal-PBM, the sparse patch-based LF method Sparse-PBM, majority voting method, similarity and truth estimation for propagated segmentations, and hierarchical multi-atlas LF with multi-scale feature representation and label-specific patch partition HMAS. Based on our experimental results and quantitative comparison, our methods are promising in the magnetic resonance image segmentation. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 23-32, 2017