Robust brain segmentation using histogram scale-space analysis and mathematical morphology
Robust brain segmentation using histogram scale-space analysis and mathematical morphology
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DOI：
10.1007/bfb0056313
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发表时间：
1998-01-01
期刊：
影响因子：
--
通讯作者：
Frouin, V
中科院分区：
文献类型：
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作者：
Mangin, JF;Coulon, O;Frouin, V
在本文中，我们提出了一个强大的完全无监督的方法，致力于分割的大脑T1加权MR图像。第一步是分析直方图一阶导数和二阶导数的尺度空间。我们首先表明，不同的导数阶极值的轨迹的尺度空间中的交叉遵循规则的拓扑性质。这些属性使我们能够设计一个新的一维信号的结构表示。然后，我们提出了一种算法，使用这种表示来推断统计的灰色和白色物质的灰度值从直方图。这些统计数据被改进的形态学过程所使用，该过程结合两个开口尺寸来分割大脑。该方法已被验证与70个图像来自3个不同的扫描仪和各种MR序列采集。
In this paper, we propose a robust fully non-supervised method dedicated to the segmentation of the brain in T1-weighted MR images. The first step consists in the analysis of the scale-space of the histogram first and second derivative. We show first that the crossings in scale-space of trajectories of extrema of different derivative orders follow regular topological properties. These properties allow us to design a new structural representation of a 1D signal. Then we propose an heuristics using this representation to infer statistics on grey and white matter grey level values from the histogram. These statistics are used by an improved morphological process combining two opening sizes to segment the brain. The method has been validated with 70 images coming from 3 different scanners and acquired with various MR sequences.