Fast Hemorrhage Detection in Brain CT Scan Slices Using Projection Profile Based Decision Tree

Abstract

Detection of a hemorrhage in CT scan slices is one of the crucial steps for a neurosurgeon to diagnose any abnormality and severity in the brain of a patient. It is usually time consuming as there are as many as 256 produced slices from a CT scan machine for each patient. In this paper, we introduce an automatic hemorrhage detection in brain CT slices using features-based approach. We employ decision tree based on 8 features to classify slices to two classes- with and without the sign of hemorrhage. The proposed method is tested on 1,451 CT scan slices and achieves a classification accuracy for up to 99% and it takes 0.12 second to detect slices.