A target-oriented segmentation method for specific tissues in MRI images of the brain

The multi-atlas based segmentation method can achieve the accurate segmentation of specific tissues of the human brain in the magnetic resonance imaging (MRI). The correct image registration and fusion scheme used in this method have an impact on the accuracy of segmentation. Similar to any traditional rigid registration method, we use the same method in our proposed target-oriented registration for the coarse registration between the target image and atlas image. However, to improve the registration accuracy in the area to be segmented, we propose a target-oriented image registration method for the refinement. We employ the distribution probability of the tissue (to be segmented) in the sparse patch-based label fusion process. Our aim is to determine if the proposed registration method can contribute the segmentation accuracy and which label fusion method is a good fit with this target-oriented registration. To evaluate the efficiency of our proposed method, we compare the performance of the majority voting method (MV), the nonlocal patch-based method (Nonlocal-PBM) and the sparse patch-based method (Sparse-PBM). Experimental results show that more accurate segmentation results can be obtained with the proposed registration method in this study. This result can provide more accurate clinical diagnosis information.

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