A hierarchical evolutionary algorithm for automatic medical image segmentation

Authors:
Chih-Chin Lai;Chuan-Yu Chang
Affiliations:
Department of Electrical Engineering National University of Kaohsiung, Kaohsiung 81148 Taiwan;Department of Computer and Communication Engineering National Yunlin University of Science & Technology, Yunlin 640, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Image segmentation denotes a process of partitioning an image into distinct regions. A large variety of different segmentation approaches for images have been developed. Among them, the clustering methods have been extensively investigated and used. In this paper, a clustering based approach using a hierarchical evolutionary algorithm (HEA) is proposed for medical image segmentation. The HEA can be viewed as a variant of conventional genetic algorithms. By means of a hierarchical structure in the chromosome, the proposed approach can automatically classify the image into appropriate classes and avoid the difficulty of searching for the proper number of classes. The experimental results indicate that the proposed approach can produce more continuous and smoother segmentation results in comparison with four existing methods, competitive Hopfield neural networks (CHNN), dynamic thresholding, k-means, and fuzzy c-means methods.