A rapid automatic brain tumor detection method for MRI images using modified minimum error thresholding technique
This proposed work is aimed to develop a rapid automatic method to detect the brain tumor from T2-weighted MRI brain images using the principle of modified minimum error thresholding (MET) method. Initially, modified MET method is applied to produce well segmented and sub-structural clarity for MRI brain images. Further, using FCM clustering the appearance of tumor area is refined. The obtained results are compared with corresponding ground truth images. The quantitative measures of results were compared with the results of those conventional methods using the metrics predictive accuracy (PA), dice coefficient (DC), and processing time. The PA and DC values of the proposed method attained maximum value and processing time is minimum while compared to conventional FCM and k-means clustering techniques. This proposed method is more efficient and faster than the existing segmentation methods in detecting the tumor region from T2-weighted MRI brain images.  2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 7785, 2015
brain tumor thresholding segmentation clustering fuzzy c-means