Automatic organ localizations on 3D CT images by using majority-voting of multiple 2D detections based on local binary patterns and Haar-like features
Automatic organ localizations on 3D CT images by using majority-voting of multiple 2D detections based on local binary patterns and Haar-like features
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DOI：
10.1117/12.2007466
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发表时间：
2013-02
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通讯作者：
Xiangrong Zhou;Shoutarou Yamaguchi;Xinxin Zhou;Huayue Chen;T. Hara;R. Yokoyama;M. Kanematsu;H. Fujita
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文献类型：
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作者：
Xiangrong Zhou;Shoutarou Yamaguchi;Xinxin Zhou;Huayue Chen;T. Hara;R. Yokoyama;M. Kanematsu;H. Fujita
本文介绍了一种在三维CT扫描图像上实现不同内脏器官区域快速自动定位的方法。该方法结合了目标检测和多数表决技术，以实现鲁棒和快速的器官定位。该方法的基本思想是利用多个特征空间从多个身体方向、多个图像尺度上检测CT图像上三维靶区域的多个二维局部外观，并将所有二维检测结果投票回三维图像空间，以统计方式确定靶器官的一个三维边界矩形。基于局部二值模式和类Haar特征空间的模板匹配，采用包围学习训练多个二维检测器。根据三个方向的坐标直方图，采用协同投票的方法确定目标器官区域三维边界矩形的角点坐标。由于所提出的方法的架构（多个独立的检测连接到多数表决）自然适合并行计算范式和多核CPU硬件，所提出的算法很容易实现高计算效率的全身CT扫描上的器官定位，通过使用通用计算机。我们应用这种方法定位的12种主要器官区域独立的1,300躯干CT扫描。在我们的实验中，我们随机选择了300个CT扫描（具有人类指示的器官和组织位置）进行训练，然后将所提出的方法与训练结果一起应用于其他1，000个CT扫描上的每个目标区域，以进行性能测试。实验结果表明，所提出的方法的可能性，自动定位不同类型的器官的全身CT扫描。
This paper describes an approach to accomplish the fast and automatic localization of the different inner organ regions on 3D CT scans. The proposed approach combines object detections and the majority voting technique to achieve the robust and quick organ localization. The basic idea of proposed method is to detect a number of 2D partial appearances of a 3D target region on CT images from multiple body directions, on multiple image scales, by using multiple feature spaces, and vote all the 2D detecting results back to the 3D image space to statistically decide one 3D bounding rectangle of the target organ. Ensemble learning was used to train the multiple 2D detectors based on template matching on local binary patterns and Haar-like feature spaces. A collaborative voting was used to decide the corner coordinates of the 3D bounding rectangle of the target organ region based on the coordinate histograms from detection results in three body directions. Since the architecture of the proposed method (multiple independent detections connected to a majority voting) naturally fits the parallel computing paradigm and multi-core CPU hardware, the proposed algorithm was easy to achieve a high computational efficiently for the organ localizations on a whole body CT scan by using general-purpose computers. We applied this approach to localization of 12 kinds of major organ regions independently on 1,300 torso CT scans. In our experiments, we randomly selected 300 CT scans (with human indicated organ and tissue locations) for training, and then, applied the proposed approach with the training results to localize each of the target regions on the other 1,000 CT scans for the performance testing. The experimental results showed the possibility of the proposed approach to automatically locate different kinds of organs on the whole body CT scans.