A Study on Segmenting Brain Tumor MRI Images

Abstract

Segmentation of image has traditionally been referred to as the initial stage in image processing. A successful segmentation output will make image processing analysis considerably further easier. There are several image segmentation techniques and methodologies available. Clustering is the most widely used segmentation algorithm for image processing. Segmentation of tumor using magnetic resonance imaging (MRI) data is a critical procedure yet time-consuming process manually carried out by medical specialists. Due to the considerable difference in the tumor tissue appearances across patients, as well as their occasionally similar resemblance to normal tissues, automating this procedure is difficult. MRI is a sort of sophisticated medical imaging that offers precise information on the human soft tissues. To identify and segment the brain tumor using MRI images, several brain tumor segmentation and detection approaches are analyzed. The benefits and drawbacks of these approaches for brain tumor identification and segmentation are analyzed, with an emphasis on illuminating the benefits and limitations of these techniques for brain tumor segmentation and detection. The MRI image usage in segmentation and detection on various processes is also covered. This analysis provides an overview of several segmentation methods for identifying brain tumors from MRI images of the brain, as well as the usage of various Clustering Techniques.