3D geometric split-merge segmentation of brain MRI datasets

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• 作者：Ioannis Marras ; ¹ ; ^{i.marras.gr@gmail.com" class="auth_mail} ; Nikolaos Nikolaidis ^{nikolaid@aiia.csd.auth.gr" class="auth_mail} ; Ioannis Pitas ^{pitas@aiia.csd.auth.gr" class="auth_mail}
• 关键词：Volume region segmentation ; Volume region splitting ; Volume region merging ; MRI brain tissue segmentation ; Volume tree representation
• 刊名：Computers in Biology and Medicine
• 出版年：1 May, 2014
• 年：2014
• 卷：48
• 期：Complete
• 页码：119-132
• 全文大小：5198 K

文摘

In this paper, a novel method for MRI volume segmentation based on region adaptive splitting and merging is proposed. The method, called Adaptive Geometric Split Merge (AGSM) segmentation, aims at finding complex geometrical shapes that consist of homogeneous geometrical 3D regions. In each volume splitting step, several splitting strategies are examined and the most appropriate is activated. A way to find the maximal homogeneity axis of the volume is also introduced. Along this axis, the volume splitting technique divides the entire volume in a number of large homogeneous 3D regions, while at the same time, it defines more clearly small homogeneous regions within the volume in such a way that they have greater probabilities of survival at the subsequent merging step. Region merging criteria are proposed to this end. The presented segmentation method has been applied to brain MRI medical datasets to provide segmentation results when each voxel is composed of one tissue type (hard segmentation). The volume splitting procedure does not require training data, while it demonstrates improved segmentation performance in noisy brain MRI datasets, when compared to the state of the art methods.