A Novel Method of Brain Tumor Detection from MRI Images Using Fuzzy C Means Clustering and Neural Networks – A Survey

Abstract: Magnetic Resonance Imaging (MRI) is the major technique to detect the brain tumor. In the proposed system classification of MRI images are done using data mining techniques. A new hybrid technique is proposed based on neural networks and fuzzy c-means for brain tumor classification. The proposed algorithm is a combination of neural networks and fuzzy c-means clustering. In the proposed algorithm an image is enhanced using enhancement techniques such as mid-range stretch and contrast improvement. Double thresholding and morphological operations are used for skull striping. Fuzzy c-means clustering (FCM clustering) is used for the segmentation of the image to detect the suspicious region in MRI images of the brain. Gray level run length matrix (GLRLM) is used for extraction of feature from the brain image, after which neural networks technique is applied to classify the brain MRI images, which provide accurate and more effective result for classification of brain MRI images.

Keywords: Data mining, MRI, Neural networks, Gray level run length matrix, Fuzzy c-means clustering.