Segmentation and Classification for Brain MRI Image Based on Modified FCM with Zernike Moment Classifier

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2017 by IJETT Journal
Volume-44 Number-2
Year of Publication : 2017
Authors : K.Bhargavi, Dr. T. Sreenivasulu Reddy
DOI :  10.14445/22315381/IJETT-V44P214


K.Bhargavi, Dr. T. Sreenivasulu Reddy "Segmentation and Classification for Brain MRI Image Based on Modified FCM with Zernike Moment Classifier", International Journal of Engineering Trends and Technology (IJETT), V44(2),66-71 February 2017. ISSN:2231-5381. published by seventh sense research group

Automatic segmentation of brain tissues from MRI is of great importance for clinical application and scientific research. We propose a robust discriminative enhancement and segmentation methods from the view of information theoretic learning. Adaptive Histogram Equalization(AHE) is used to enhance the image which has a tendency to over-amplify noise in relatively homogeneous regions of an image. A variant of adaptive histogram equalization called Contrast Limited Adaptive Histogram Equalization (CLAHE) prevents this by limiting the amplification. So we use CLAHE method to improve the contrast of the image. Fuzzy clustering using fuzzy C-means (FCM) algorithm proved to be superior over the other clustering approaches in terms of segmentation efficiency. But the major drawback of the FCM algorithm is the huge computational time required for convergence. The effectiveness of the FCM algorithm in terms of computational rate is improved by modifying the cluster center and membership value updating criterion. In this paper, the application of modified FCM algorithm for MR brain tumor detection is explored. Experimental results show superior results for the Modified FCM algorithm in terms of the performance measures. Along with this we have used Gray Level Co-Occurrence Matrix which is used for feature extraction and a classifier called Zernike Moment Classifier is used to classify the image whether it is normal or abnormal.


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discriminative segmentation, fuzzy C-means(FCM) algorithm, modifiedFCM algorithm, zernike moment classfier.