Enhanced DWT Filtering Technique for Brain Tumor Detection

Abstract

ABSTRACTThis paper presents Magnetic Resonance Imaging (MRI) brain tumor detection utilizing Fuzzy C Means strategy with an upgraded noise filtering calculation. A novel technique is proposed to enhance the execution of cerebrum tumor discovery. A new calculation for noise filtering is adapted to extract the correct area of tumor, where execution is enhanced by upgrading the threshold task in wavelet filtering strategy as a preprocessing step. Trial results demonstrate that by utilizing proposed calculation, the filtering procedure gives better execution when contrasted with the current methods. The average value of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) for Gaussian noise is improved by 40% and 41.06% and for Rician noise, which is 13.73% and 25.39% higher than the state-of-art methods. After filtering, segmentation is done to point out the tumor region. For segmentation, Otsu and FCM methods are adapted here and a comparison is made between these two methods. Experimental results show that Jaccard and Dice coefficient of Fuzzy C Means (FCM) with enhanced filtering is increased by 3.6% and 1.3% compared to the methods available in the literature.