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Automatic Extraction of the Centerline of Corpus Callosum from Segmented Mid-Sagittal MR Images

机译:从分段中矢状MR图像自动提取语料库胼callosum的中心线

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摘要

The centerline, as a simple and compact representation of object shape, has been used to analyze variations of the human callosal shape. However, automatic extraction of the callosal centerline remains a sophisticated problem. In this paper, we propose a method of automatic extraction of the callosal centerline from segmented mid-sagittal magnetic resonance (MR) images. A model-based point matching method is introduced to localize the anterior and posterior endpoints of the centerline. The model of the endpoint is constructed with a statistical descriptor of the shape context. Active contour modeling is adopted to drive the curve with the fixed endpoints to approximate the centerline using the gradient of the distance map of the segmented corpus callosum. Experiments with 80 segmented mid-sagittal MR images were performed. The proposed method is compared with a skeletonization method and an interactive method in terms of recovery error and reproducibility. Results indicate that the proposed method outperforms skeletonization and is comparable with and sometimes better than the interactive method.
机译:中心线作为物体形状的简单和紧凑的表示,已经用于分析人类调用形状的变化。然而,愈伤组织中心线的自动提取仍然是一个复杂的问题。在本文中,我们提出了一种从分段中矢状磁共振(MR)图像自动提取调用中心线的方法。引入了一种基于模型的点匹配方法来定位中心线的前后端点。端点的模型由形状上下文的统计描述符构成。采用主动轮廓建模与固定端点驱动曲线,以使用分段胼callosum的距离图的距离图的梯度近似于中心线。进行80分段中矢状MR图像的实验。在恢复误差和再现性方面,将所提出的方法与骨架化方法和交互方法进行比较。结果表明,所提出的方法优于骨骼化,与交互方法相当。

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