A Survey of Recent Image Segmentation Techniques for MRI Brain Images

The brain tumor detection from Magnetic Resonance Images (MRI) is one of the mainly challenging tasks in Medical imaging techniques. MRI used to produce images of soft tissue of the human body, used to analyze the human organs without any surgery. The purpose of segmentation is to simplify and change the representation of an image into meaningful image to analyze. The image segmentation is a very difficult job in the image processing and challenging task for clinical diagnostic tools. Noises present in the Brain MRI images are multiplicative noises and reductions of these noises are difficult task. There are many noise reduction techniques available to cut the noises from brain images to detect tumor or cancer. Accurate segmentation of the MRI images is extremely important and essential for the exact diagnosis by computer aided clinical tools. This paper is to check existing approaches of preprocessing and current segmentation techniques in brain images. The aim of preprocessing is to improve the image quality, removing the irrelevant noises and unwanted parts in the background of the MRI images. There are different types of segmentation algorithms for MRI brain images. Their advantages and disadvantages discussed.

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