Medical image segmentation by a constraint satisfaction neural network
C.-T. Chen, Eric Chen-Kuo Tsao, W.-C. Lin · IEEE Transactions on Nuclear Science · 1991
A class of constraint-satisfaction neural networks (CSNNs) is proposed for solving the problem of medical image segmentation, which can be formulated as a constraint-satisfaction problem (CSP). A CSNN consists of a set of objects, a set of labels for each object, a collection of constraint relations linking the labels of neighboring objects, and a topological constraint describing the neighborhood relationship among various objects. Each label for a particular object indicates one possible interpretation for that object. The CSNN can be viewed as a collection of neurons that interconnect with each other. The connections and the topology of a CSNN are used to represent the constraints in a CSP. The mechanism of the neural network is to find a solution that satisfies all the constraints in order to achieve a global consistency. The final solution outlines segmented areas and simultaneously satisfies all the constraints. This technique has been applied to medical images, and the results show that the, method is a very promising approach to image segmentation,.>