Intracranial Hemorrhage Annotation for CT Brain Images

In this paper, we created a decision-making model to detect intracranial hemorrhage and adopted Expectation Maximization(EM) segmentation to segment the Computed Tomography (CT) images. In this work, basically intracranial hemorrhage is classified into two main types which are intra-axial hemorrhage and extra-axial hemorrhage. In order to ease classification, contrast enhancement is adopted to finetune the contrast of the hemorrhage. After that, k-means is applied to group the potential and suspicious hemorrhagic regions into one cluster. The decision-making process is to identify whether the suspicious regions are hemorrhagic regions or non-regions of interest. After the hemorrhagic detection, the images are segmented into brain matter and cerebrospinal fluid (CSF) by using expectation-maximization (EM) segmentation. The acquired experimental results are evaluated in terms of recall and precision. The encouraging results have been attained whereby the proposed system has yielded 0.9333 and 0.8880 precision for extra-axial and intra-axial hemorrhagic detection respectively, whereas recall rate obtained is 0.9245 and 0.8043 for extra-axial and intra-axial hemorrhagic detection respectively