Brain Tumor Detection using K-SVD Denoising and Fuzzy C-means Clustering

In MRI images, the amount of data is too much for manual interpretation and analysis. Because of high quantity data in MRI images, the tumor segmentation and classification is very hard. During past few years, brain tumor segmentation in MRI (magnetic resonance imaging) has become an emergent research area in the field of medical imaging system So far, brain tumors are identified by the expertise of the radiologists but this paper presents a new technology of detecting the tumor by simply feeding the MRI (magnetic resonance imaging) of the brain into the system to obtain knowledge on the occurrence of the brain tumor. Initially the MRI brain image quality enhanced through K-SVD (Singular Value Decomposition) Denoising algorithm. An efficient algorithm is proposed for tumor detection based on the Spatial Fuzzy C-Means Clustering, this paper introduces a novel module in combining the techniques to detect brain tumor and to obtain the result.