A Computerized Method for Detection of Acute Cerebral Infarction on CT Images

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.