MRI Brain Tissue Classification Using Unsupervised Optimized Extenics-Based Methods

MRI (Magnetic resonance imaging, MRI) is a very important contrast in the clinical diagnosis, using radio wave pulses, so that the body of water molecules in the resonance of the hydrogen atom, and change the magnetic field generated by the echo signals into images by a computer. After a long clinical trial, have proved to be harmless. MRI is not invasive to human body, does not produce ionizing radiation, direction scans, three dimensional images and high contrast resolution and many other advantages, so the message so that it provides a large and rich set of organizations, but for medical personnel, inadequate amount large or will affect the results of judgments, so issues classification algorithms, become the focus of this study. Physician interpretation of a large number of medical imaging in the past with medical knowledge, modified extension now through the optimization method for physicians as a reference, MRI does substantially reduce the burden of multiple large spectrum of information on physicians, make Diagnostics more efficient, precise focus lies. Finally, K-meansTools for automatic segmentation and brain (FMRIB's Automated Segmentation Tool, FAST), and the use of receiver operating characteristics (Receiver Operating Characteristic, ROC) to effectiveness evaluation, and proved its superiority.