Marras Ioannis, Nikolaidis Nikolaos, Pitas Ioannis
Aristotle University of Thessaloniki, Department of Informatics, Box 451, 54124 Thessaloniki, Greece.
Comput Biol Med. 2014 May;48:119-32. doi: 10.1016/j.compbiomed.2014.02.013. Epub 2014 Mar 6.
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.
本文提出了一种基于区域自适应分割与合并的磁共振成像（MRI）体积分割新方法。该方法称为自适应几何分割合并（AGSM）分割，旨在寻找由均匀几何3D区域组成的复杂几何形状。在每个体积分割步骤中，会检查几种分割策略并激活最合适的策略。还引入了一种找到体积最大均匀性轴的方法。沿着该轴，体积分割技术将整个体积划分为多个大的均匀3D区域，同时，它以这样一种方式更清晰地定义体积内的小均匀区域，即它们在后续合并步骤中具有更大的存活概率。为此提出了区域合并标准。所提出的分割方法已应用于脑MRI医学数据集，以在每个体素由一种组织类型组成时提供分割结果（硬分割）。体积分割过程不需要训练数据，并且与现有方法相比，它在有噪声的脑MRI数据集中表现出了改进的分割性能。
