Automatic brain hemorrhage segmentation and classification in CT scan images

Abstract

Brain hemorrhage detection and classification is a major help to physicians to rescue patients in an early stage. In this paper, we have tried to introduce an automatic detection and classification method to improve and accelerate the process of physicians' decision-making. To achieve this purpose, at first we have used a simple and effective segmentation method to detect and separate the hemorrhage regions from other parts of the brain, and then we have extracted a number of features from each detected hemorrhage region. We selected some of convenient features by using a Genetic Algorithm (GA)-based feature selection algorithm. Eventually, we have classified the different types of hemorrhages. Our algorithm is evaluated on a perfect set of CT-scan images and the segmentation accuracy for three major types of hemorrhages (EDH, ICH and SDH) obtained 96.22%, 95.14% and 90.04%, respectively. In the classification step, multilayer neural network could be more successful than the KNN classifier because of its higher accuracy (93.3%). Finally, we achieved the accuracy rate of more than 90% for the detection and classification of brain hemorrhages.