An automated detection method for the MCA dot sign of acute stroke in unenhanced CT

The hyperdense middle cerebral artery (MCA) dot sign representing a thromboembolus is one of the important computed tomography (CT) findings for acute stroke on unenhanced CT images. Our purpose in this study was to develop an automated method for detection of the MCA dot sign of acute stroke on unenhanced CT images. The algorithm of the method which we developed consisted of 5 major steps: extraction of the sylvian fissure region, initial identification of MCA dots based on the morphologic top-hat transformation, feature extraction of candidates, elimination of false positives (FPs) by use of a rule-based scheme, and classification of candidates using a support vector machine (SVM) classifier with four features. Our database comprised 297 CT images obtained from seven patients with the MCA dot sign. The performance of this scheme for classification of the MCA dot sign was evaluated by means of a leave-one-case out method. The performance of the classification by use of the SVM achieved a maximum sensitivity of 97.5% (39/40) at a FP rate of 1.28 per image. The sensitivity for detection of the MCA dot sign was 97.5% (39/40) with a FP rate of 0.5 per hemisphere. The method we developed has the potential to detect the MCA dot sign of acute stroke on unenhanced CT images.