A Novel Approach for Efficient Segmentation Methods for turmeric Detection in MRI Images

Abstract

Brain tumor extraction and its evaluation are challenging tasks in medical image processing because brain  image and its structure is complicated that can be analyzed  only by expert radiologists. Segmentation plays an  important role in the processing of medical images. MRI  (magnetic resonance imaging) has become an important aspect in medical diagnostic tool for diagnosis of brain and  other medical images. MRI  (magnetic resonance imaging) has emerged as a principally useful medical diagnostic instrument for analysis of brain and other medical images. This paper presents a comparative study of segmentation methods implemented for tumor detection. The methods include clustering with patchs segmentation algorithm, optimized patches and clustering with genetic algorithm and optimized genetic segmentation algorithm. The traditional generic algorithm is sensitive to the initial cluster centers. Genetic patches and clustering techniques are used to detect tumor in MRI of brain images. The experimental results  indicate that genetic turmeric algorithm  not only eliminate the over-segmentation problem, but also provide fast and efficient  clustering results.