Saito Hideki, Katsuragawa Shigehiko, Hirai Toshinori, Kakeda Shingo, Kourogi Yukunori
Kumamoto University Graduate School of Health Sciences.
Nihon Hoshasen Gijutsu Gakkai Zasshi. 2010 Sep 20;66(9):1169-77. doi: 10.6009/jjrt.66.1169.
This paper proposes a computerized method for automated detection of acute cerebral infarction (ACI) on CT images. This method is based on the difference value of image features in the two regions-of-interests (ROIs) selected at symmetrical positions. In our computerized method, first, we segmented the brain parenchyma by the thresholding technique after correction of inclination of the midsagittal plane with translation and rotation of the image. Then we selected the middle cerebral artery (MCA) region of the brain parenchyma. Moreover, many ROIs with a 32×32 matrix size were selected in the MCA region. In addition, image features in each ROI were determined from the statistical analysis, the co-occurrence matrix and the run length matrix. Finally, ROIs with ACI were classified by using a linear discriminant analysis with difference values of image features in two ROIs at symmetrical positions. Nineteen cases with ACI and normal 14 cases were employed in this study. As a result of our experiments, the sensitivity of detection of ACI was 88.0% with an average number of false positives of 4.6 per case. Our computerized method provided a relatively high performance for detection of ACI. Therefore, we believe this method would be useful for an algorithm of a computer-aided diagnosis to detect ACI on CT images.
本文提出了一种在CT图像上自动检测急性脑梗死（ACI）的计算机化方法。该方法基于在对称位置选择的两个感兴趣区域（ROI）中图像特征的差值。在我们的计算机化方法中，首先，在通过图像的平移和旋转校正正中矢状面的倾斜度后，我们采用阈值技术对脑实质进行分割。然后我们选择脑实质的大脑中动脉（MCA）区域。此外，在MCA区域中选择了许多大小为32×32矩阵的ROI。另外，每个ROI中的图像特征是通过统计分析、共生矩阵和游程长度矩阵来确定的。最后，利用线性判别分析，根据对称位置的两个ROI中图像特征的差值对患有ACI的ROI进行分类。本研究采用了19例ACI患者和14例正常对照。我们的实验结果显示，ACI检测的灵敏度为88.0%，平均每例假阳性数为4.6。我们的计算机化方法在检测ACI方面具有较高的性能。因此，我们认为该方法对于在CT图像上检测ACI的计算机辅助诊断算法将是有用的。
