Brain Tumor Detection Using Histogram Thresholding to Get the Threshold point

Abstract

In this paper, a computer-based method for defining tumor region in the brain using MRI images is presented. Today's modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Brain tumor, a mass of tissue that grows out of control is one of the major causes for the increase in mortality among children and adults. Segmenting the regions of brain is the major challenge in tumor detection. A large number of effective segmentation algorithms have been used for segmentation in grey scale images ranging from simple edge-based methods to composite high-level approaches using modern and advanced pattern recognition approaches. Brain tumor detection is one of the challenging tasks in medical image processing. Four MRI images were collected and the experiments were conducted for statistical analysis also. A comparative study is made and the results are of great interest and practical utility.