Fully Automated Approach to Identify Brain Tumors in 2D MRI Using Thresholding and Region Growing Method?

Automated brain tumor segmentation and detection are vastly important in medical diagnostics because it provides information related to anatomical structures as well as potential abnormal tissue necessary to delineate appropriate surgical planning. As the segmentation of anatomical regions of the brain is the basic problem in medical image analysis. Segmentation of Brain tumor appropriately is a difficult task in MRI. The MRI image is an image that produces a high contrast images indicating regular and irregular tissues that help to discriminate the overlapping in margin of ach limb. But when the edges of tumor is not sharpen then the segmentation results are not accurate i.e. segmentation may be over or under. This may be happened due to initial stage of the tumors. So , in this paper a modified method of tumor line detection and segmentation is used to separate the irregular from the regular surrounding tissue to get a real identification of involved and noninvolved area that help the surgeon to distinguish the involved area precisely. The method proposed here uses a thresholding approach for initial segmentation then the region is filled using seeded region growing method and detect the tumor boundaries in 2D MRI for different cases. This method that can be validated segmentation on 2D MRI Data.