Brain Tumor De-noising MRI Image and Superpixel SLIC segmentation

Abstract

Human brain tumors are the most lethal type of cancer problems, a high level of precision and its non-invasive nature. Since MRI image is particularly well suited for brain tumor investigations due to its excellent contrast for soft tissue problems, non-invasive nature, high spatial resolution and ability to segment brain tumors; this is significant in the area of MRI. In this paper, the brain tumor is segmented with superpixels using a simple linear iterative cluster (SLIC). MRI image of the malignancy, which has significantly progressed, particularly in the stages of infection. Patients who are receiving therapy have much higher chances of survival than those who are not, especially early in the course of their illness. Brain tumor segmentation can be analyzed precisely with a magnetic resonance image (MRI), which gives a proper anatomical structural study. The pathological regions like cancer, multiple sclerosis lesions, can be viewed perfectly. Pixel-wise segmentation is being appeared in image segmentation to for a cluster. It can also be used to divide an image into various subregions. In the field of MRI brain tumor segmentation, is important because MRI are especially well suited for brain studies due to its high quality contrast for soft tissue.