基于两步图谱选择的脑MR图像分割

针对传统的基于多图谱的医学图像分割过程中的相似度加权融合的方法没有考虑图谱集的干扰性和冗余性的不足, 提出一种基于两步图谱选择策略的脑MR图像分割方法. 该方法首先采用一种基于最小角回归的方法进行图谱粗选择, 其次则采用基于豪斯多夫距离的以目标为导向的图谱精选择. 粗选择方法可以在总体上来寻找和目标图像较为相似的图谱,...

Brain MR Image Segmentation Based on Two-stage Atlas Selection

Given that the traditional method of similarity weighted fusion in the process of multi-atlas based medical image segmentation does not consider the interference and redundancy of the atlas set, a method of brain magnetic resonance (MR) image segmentation based on a two-stage atlas selection strategy is proposed. In this method, a method based on minimum angle regression is used for rough atlas selection. Then, a method based on the Hausdorff distance is adopted for target-oriented precise atlas selection. The rough selection method can find the atlas similar to the target image on the whole, remove invalid variables, and reduce the interference and redundancy of the atlas set. The precise selection method pays more attention to the similarity calculation of the target tissue, and the similarity results are not affected by the size and location of the target tissue. Experimental results show that the proposed method is more robust and accurate than the traditional one-stage atlas selection method based on similarity calculation of the rectangular region.