Comparative analysis of segmentation techniques on MRI brain tumor images

Abstract

Image Segmentation is considered a necessary technique in medical image processing in extracting the required features for identifying abnormalities in the scanned image. The segmentation of Magnetic Resonance (MR) Brain images helps the radiologist or clinician to classify the tumors or other lesion with accuracy. The manual segmentation technique requires experienced personnel or experts in the classification of brain tumors. To overcome this hindrance many automatic segmentation techniques are introduced. In this proposed work, a comparative study is made on different segmentation techniques like Threshold based, Edge detection, Region growing, Watershed, and K-mean clustering techniques. The mentioned techniques are evaluated and the performance of the different methods are qualitatively analyzed by classifying the tumors from the MR brain images based on the area of segmentation, number of pixels, processing time, and so on.