Detection of Brain Tumor Using Modified Histo-Thresholding-Novel Approach

In medical image processing brain tumor detection is one of the challenging tasks, since brain images are complicated and tumors can be analyzed only by expert physicians. The knowledge of volume of a tumor plays an important in the treatment of malignant tumors. Brain cancer can be counted among the most deadly and intractable diseases. Tumors may be embedded in regions of the brain that are critical to orchestrating the body's vital functions, while they shed cells to invade other parts of the brain, forming more tumors too small to detect using conventional imaging techniques. Brain cancer's location and ability to spread quickly makes treatment with surgery or radiation like fighting an enemy hiding out among minefields and caves. Manual segmentation of brain tumors from Magnetic Resonance images is a challenging and time consuming task so in this paper brain tumor is detected at various levels. First the pre-processing is reduce noise and then edge detection is done by using canny filter, then Segmentation is done by means of histogram clustering in which the tumor affected image is divided into two parts and threshold value is set, based on this value tumor is detected. Secondly the other technique involved is superimposing of the tumor affected with the healthy image. This method does not require any initialization while the others require an initialization inside the tumor. The third method in which, the histogram is calculated and the threshold is fixed. This work is carried in MRI image.