Automated Segmentation and Classification of Brain Magnetic Resonance Imaging

Different approaches are needed as function of the medical images that must be studied. Also, the technique that produced those images is very important in order to know what to apply to a certain medical image in order to get better results. A lot of methods have been proposed in the literature for CT (Computed Tomography) scans, different types of X-rays,MRI images and other radiological techniques. With all this effort done in the research field there is a lot of place for improvements and the medical image processing is a domain in continuos expansion. Why this domain is in continuo expansion and there are no good accepted methods? This is due to the fact that in such an important domain, the accuracy must be very high and the false negative rate must be low. The problem is that is not very easy to obtain such results. Anyway, the idea is to reduce as much as possible the human errors by assisting the physicians and the radiologists with some software that could lead to better results. This is important for human life. What I'll propose in my project is the automated segmentation of brain magnetic resonance images by using some prior knowledge like pixel intensity and some anatomical features. Currently there are no methods widely accepted therefore automatic and reliable methods for tumor detection are of great need and interest. Magnetic Resonance Imaging proved to provide high quality medical images and became widely used especially for brain. The advantages of Magnetic Resonance Imaging are that the spatial resolution is high and provides detailed images. Magnetic Resonance Images are used in detecting brain tumors or in tracking them. The tracking of the tumors is important especially when a patient is under medication in order to observe the changes that appear.