Identifying abdominal organs using robust fuzzy inference model
Chien-Cheng Lee, Pau‐Choo Chung · 2004
The paper proposes to identify abdominal organs from CT image series, by using the shape descriptors, fuzzy rules, and fuzzy-inference-based radial basis function (RBF) neural network. A number of descriptors are applied to ascertain the segmented regions and to form fuzzy rules in our inference system. It has been demonstrated that the RBF neural network and the fuzzy inference are functional equivalent. The traditional RBF network takes Gaussian functions as its basis junctions and adopts the least squares criterion as the objective function. However, it suffers from two major problems. First, it is difficult to approximate constant values. Second, when the training patterns incur a large error, the network will interpolate these training patterns incorrectly. In order to cope with these problems, a robust RBF network is proposed in this paper to recognize the organ of interest.