|International Journal of Applied Information Systems|
|Foundation of Computer Science (FCS), NY, USA|
|Volume 11 - Number 9|
|Year of Publication: 2017|
|Authors: Sreedhanya S., Chhaya S. Pawar|
Sreedhanya S., Chhaya S. Pawar . An Automatic Brain Tumor Detection and Segmentation using Hybrid Method. International Journal of Applied Information Systems. 11, 9 ( Jan 2017), 6-11. DOI=10.5120/ijais2017451641
In the field of medical image processing, brain tumor detection and segmentation using MRI scan has become one of the most important and challenging research areas. In which manual detection and segmentation of brain tumors using brain MRI scan forms a large part of human intervention for detection and segmentation taken per patient, is both tedious and has huge internal and external observer detection and segmentation variability. Hence, there is high demand for an automatic brain tumor detection and segmentation using brain MR images to overcome manual segmentation. So in current days a number of methods have proposed by researchers. But still there is no complete automated system developed yet, is due to accuracy and robustness issues. So, this paper provides a review of the methods and techniques that used to detect and segment brain tumor through MRI segmentation. Finally, the paper concludes with one of the efficient hybrid method which shows high accuracy on detection of brain tumor with proposed Gaussian Mixture Model (GMM).