Level Set Based on Brain Radiological Densities for Stroke Segmentation in CT Images

Abstract

Cardiovascular diseases (CVD) are the leading cause of death worldwide, and every year more people die of these diseases. Aiming to assist medical diagnoses through Computerized Tomography (CT) scans, this work proposes a new approach to segment CT images of the brain damaged by stroke. The proposed method takes into account two improvements of the level set method based on the likelihood of Normal distribution. The first improvement is to handle the grayscale image input according to a range analysis of the image intensity scale, adopting 80 HU for the window width and 40 HU for the center level. In addition, we propose an optimal level set initialization, where the zero level set is determined by analyzing the brain density. These improvements to the level set method generate efficient stroke segmentation in CT images of the brain. The results of the proposed method are compared against those of the level set algorithm based on the coherent propagation method, and also those from the Watershed and Region Growing algorithms using a ground truth built by a specialist. The experimental results show that the proposed method presents superior performance, and that it is a promising tool to assist medical diagnoses.