Neural networks for medical image segmentation

A class of Constraint Satisfaction Neural Networks (CSNN) 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 relationships 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 many images in different domains and the results show that this CSNN method is a very promising approach for image segmentation.

[1]  Azriel Rosenfeld,et al.  Image Segmentation by Pixel Classification in (Gray Level, Edge Value) Space , 1978, IEEE Transactions on Computers.

[2]  Wei-Chung Lin,et al.  Constraint propagation neural networks for Huffman-Clowes scene labeling , 1991, IEEE Trans. Syst. Man Cybern..

[3]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[4]  C Koch,et al.  Analog "neuronal" networks in early vision. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Theodosios Pavlidis,et al.  Integrating Region Growing and Edge Detection , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Jun S. Huang,et al.  Statistical theory of edge detection , 1988, Comput. Vis. Graph. Image Process..

[7]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Bir Bhanu,et al.  Segmentation of natural scenes , 1987, Pattern Recognit..

[9]  J. A. Hertz,et al.  A network system for image segmentation , 1989, International 1989 Joint Conference on Neural Networks.

[10]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[11]  Donald Geman,et al.  Boundary Detection by Constrained Optimization , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[13]  Anil K. Jain,et al.  MRF model-based algorithms for image segmentation , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.