Detecting brain tumor in Magnetic Resonance Images using Hidden Markov Random Fields and Threshold techniques

Brain tumors are created by abnormal and uncontrolled cell division inside the brain. The segmentation of brain tumors which is carried out manually from MRI is a crucial and time consuming task. The accuracy of detecting brain tumor location and size takes the most important role in the successful diagnosis and treatment of tumors. So the detection of brain tumor needs to be fast and accurate. Brain tumor detection is considered a challenging mission in medical image processing. This paper concerns presenting an approach which will be useful for improved detection of brain tumor using Hidden Markov Random Fields (HMRF) and Threshold methods. The proposed method has been developed in this research in order to construct hybrid method. The aim of this paper is to introduce a scheme for tumor detection in Magnetic Resonance Imaging (MRI) images using (HMRF) and Threshold techniques. These methods have been applied on 3 different patient data sets. They have the property of organizing their soothing effect on the final segment of brain tumor homogeneous tissue regions, while the edges between different tissues constituents are better kept.