Image segmentation is a fundamental image processing technology. There are many kinds of image segmentation methods, but most of them are problem oriented. In this paper, image segmentation method based on lateral inhibition network is presented. Lateral inhibition network is a biological vision model. When an image is filtered by a lateral inhibition network, its low frequency components are inhibited while the high frequency components are enhanced. The lateral inhibited image is much easier to be segmented because of its increased inter-class difference and decreased intra-class difference. The parameters of the lateral inhibition network model determine the inhibited image, thus affect the image segmentation result greatly. But there are no assured rules to determine the parameters. We propose an evolutionary strategy (ES) based method to search the optimal weighting parameters of the lateral inhibition network model. The objective function of ES is a multiattribute fitness function that combines multiple criteria of clustering and entropy information. The original image is filtered using the optimal lateral inhibition network and then the inhibited image is segmented by an optimized threshold. Using test images of various characteristics, the proposed method is evaluated by four objective image segmentation evaluation indexes. The experimental results show its validity and universality.

Date Published: 24 October 2006
PDF: 7 pages
Proc. SPIE 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence, 63570V (24 October 2006); doi: 10.1117/12.716905

Published in SPIE Proceedings Vol. 6357:
Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence
Jiancheng Fang; Zhongyu Wang, Editor(s)