Segmentation and estimation of brain tumor volume in computed tomography scan images using hidden Markov random field Expectation Maximization algorithm

Abstract

Brain tumors have been created by abnormal and uncontrolled cell division inside the brain. A crucial and lengthy task is the segmentation of brain tumors, which can be gained manually with the help of Computed Tomography (CT). Treatment, diagnosis, signs and symptoms of the brain tumors mainly depend on the volume, shapes and location of the tumors. The accuracy and time of detecting brain tumor are vital contributions in the successful diagnosis and treatment of tumors. Therefore, the detection of brain tumor needs to be fast and accurate. Brain tumor segmentation and volume estimation have been considered a challenge mission in medical image processing. The main aim of this paper is that with the help of hidden Markov random field- Expectation Maximization (HMRF-EM) and threshold method, a novel approach of improving the segmentation of brain tumors from CT scan images is produced. The segmentation and volume estimation images are obtained by the study of 2D images. We calculate the volume of tumor using a new approach based on 2D images estimations and voxel space. In order to validate the proposed approach a comparison is carried out with a manual method using Mango software which, the noise or impurities are less than Mango software in measurement of tumor volume.