Neural network shape: Organ shape representation with radial basis function neural networks

Abstract

We propose to represent the shape of an organ using a neural network classifier. The shape is represented by a function learned by a neural network. Radial Basis Function (RBF) is used as the activation function for each perceptron. The learned implicit function is a combination of radial basis functions, which can represent complex shapes. The organ shape representation is learned using classification methods. Our testing results show that the neural network shape provides the best representation accuracy. The use of RBF provides a rotation, translation and scaling invariant feature to represent the shape. Experiments show that our method can accurately represent the organ shape.