An Efficient Fuzzy Technique for Detection of Brain Tumor

Abstract

  In this epoch Medical Image segmentation is one of the most challenging problems in the research field of MRI scan image classification and analysis. The importance of image segmentation is to identify various features of the image that are used for analyzing, interpreting and understanding of images. Image segmentation for MRI of brain is highly essential due to accurate detection of brain tumor. This paper presents an efficient image segmentation technique that can be used for detection of tumor in the Brain. This innovative method consists of three steps. First is Image enhancement to improve the quality of the tumor image by eliminating noise and to normalize the image. Second is fuzzy logic which produce optimal threshold to avoid the fuzziness in the image and makes good regions regarding Image and tumor part of the Image. Third is novel OTSU technique applied for separating the tumor regions in the MRI. This method has produced better results than traditional extended OTSU method.

The Optimal Thresholding Technique for Image Segmentaion Using Fuzzy Otsu Method

“…To estimate the fuzziness of an image and to determine an adequate threshold value, by utilizing the measure of fuzziness, Ambar Dutta, et al [17], proposed a two-stage fuzzy set theoretic approach to image thresholding. To avoid the fuzziness in the image and makes good regions in the image, a new segmentation technique by using the fuzzy logic which produce optimal threshold value was proposed by C Naga Raju et al [18]. C Naga Raju et al [19], proposed a new technique to address the multidimensional optimized problem with optimal thresholding using fuzzy entropy for image enhancement.…”