Automatic rib segmentation and labeling in computed tomography scans using a general framework for detection, recognition and segmentation of objects in volumetric data
Automatic rib segmentation and labeling in computed tomography scans using a general framework for detection, recognition and segmentation of objects in volumetric data
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DOI：
10.1016/j.media.2006.10.001
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发表时间：
2007-02-01
影响因子：
10.9
通讯作者：
Viergever, Max A.
中科院分区：
文献类型：
--
作者：
Staal, Joes;van Ginneken, Bram;Viergever, Max A.
介绍了一种用于胸部CT扫描中完整肋骨的自动分割和标记系统。该方法使用一个通用的框架来自动检测、识别和分割三维医学图像中的目标。该框架包括五个阶段：(1)相关图像结构的检测，(2)图像基元的构建，(3)基元的分类，(4)分类基元的分组和识别，(5)基于获得的组的完全分割。对于这种应用，首先从3D数据中提取1D脊线。然后，从脊体素构造线元素形式的基元。接下来，训练分类器来对前景(肋骨)和背景中的基元进行分类。在分组阶段，从前景基元形成中心线，并为中心线分配肋数。在最终分割阶段，中心线作为种子区域生长算法的初始化。该方法在20个CT扫描上进行了测试。分类正确率为97.5%(敏感度为96.8%，特异度为97.8%)。分类后的肋骨识别率为98.4%。最后的分割被定性地评估，并且对于所有肋骨中的80%以上是非常准确的，除此之外还有轻微的误差。(C)2006爱思唯尔B.V.保留所有权利。
A system for automatic segmentation and labeling of the complete rib cage in chest CT scans is presented. The method uses a general framework for automatic detection, recognition and segmentation of objects in three-dimensional medical images. The framework consists of five stages: (1) detection of relevant image structures, (2) construction of image primitives, (3) classification of the primitives, (4) grouping and recognition of classified primitives and (5) full segmentation based on the obtained groups. For this application, first 1D ridges are extracted in 3D data. Then, primitives in the form of line elements are constructed from the ridge voxels. Next a classifier is trained to classify the primitives in foreground (ribs) and background. In the grouping stage centerlines are formed from the foreground primitives and rib numbers are assigned to the centerlines. In the final segmentation stage, the centerlines act as initialization for a seeded region growing algorithm. The method is tested on 20 CT-scans. Of the primitives, 97.5% is classified correctly (sensitivity is 96.8%, specificity is 97.8%). After grouping, 98.4% of the ribs are recognized. The final segmentation is qualitatively evaluated and is very accurate for over 80% of all ribs, with slight errors otherwise. (C) 2006 Elsevier B.V. All rights reserved.