Review on Automatic Brain Tumor Detection Technique

Shweta A. Ingle, Snehal M. Gajbhiye

Abstract: Brain tumor is abnormal growth of cells within brain which may be cancerous or non-cancerous. MRI is the preferred technology out of various available technologies for the diagnosis and evaluation of brain tumor. The current work presents various segmentation techniques that are employed to detect brain tumor. The algorithm based on segmentation using clustering technique deals with steps such as preprocessing, segmentation, feature extraction and classification of MR images. The integration of K-means and Fuzzy C-means (KFCM) clustering algorithm is used, that reduces the problem of gray level selection and noise sensitivity to FCM. K-means algorithm is used for initial segmentation. On the basis of updated membership and automatic cluster selection, a sharp segmented image is obtained from modified FCM technique. This technique is used to select the small deviation for gray level in tumor region for proper segmentation of brain tumor from MRI image.

Keywords: Threshold, Classifier, clustering, K-mean, Fuzzy C-mean, KFCM

How to Cite?: Shweta A. Ingle, Snehal M. Gajbhiye, "Review on Automatic Brain Tumor Detection Technique", Volume 6 Issue 2, February 2017, International Journal of Science and Research (IJSR), Pages: 1553-1557, https://www.ijsr.net/getabstract.php?paperid=ART20171047, DOI: https://dx.doi.org/10.21275/ART20171047