A novel classification method based on membership function

We propose a method for medical image classification using membership function. Our aim is to classify the image as several classes based on a prior knowledge. For every point, we calculate its membership function, i.e., the probability that the point belongs to each class. The point is finally labeled as the class with the highest value of membership function. The classification is reduced to a minimization problem of a functional with arguments of membership functions. Three novelties are in our paper. First, bias correction and Rudin-Osher-Fatemi (ROF) model are adopted to the input image to enhance the image quality. Second, unconstrained functional is used. We use variable substitution to avoid the constraints that membership functions should be positive and with sum one. Third, several techniques are used to fasten the computation. The experimental result of ventricle shows the validity of this approach.