Segmentation of Tumor Region from Brain Mri Images Using Fuzzy C-Means Clustering And Seeded Region Growing

Abstract

The detection of brain tumor is one of the most challenging tasks in the field of medical image processing, since brain images are very complicated and tumors can be analyzed efficiently only by the expert radiologists. Therefore, there is a significant need to automate this process. In this paper, a method for the automatic detection of the tumor from the brain magnetic resonance imaging (MRI) images has been proposed. For this, the region-based segmentation of the input MRI image is done. The wavelet-based decomposition of the input image is done and the input image is reconstructed on the basis of soft thresholding for the enhancement of the image. After that, fuzzy c-means clustering (FCM) followed by seeded region growing is applied to detect and segment the tumor from the brain MRI image and finally comparison with Sobel operator is done on the basis of Relative Ultimate Measurement Accuracy (RUMA) and Standard Deviation (SD). The results for the proposed technique are better than the results obtained by using Sobel operator.