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Modified Fuzzy Linear Discriminant Analysis for Threshold Selection

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Abstract

Otsu’s thresholding method is a popular and efficient method for image segmentation. However, its performance is greatly affected by noise and the population size of object and background. In this paper, a novel thresholding method is proposed based on modified fuzzy linear discriminant analysis (MFLDA). MFLDA is an extension of linear discriminant analysis to fuzzy domain, where the between-class variance is modified as the distance between the centers of background and object. The optimal threshold is selected such that the MFLDA criterion is maximized. Some images are used to test the performance of the proposed thresholding method and results reveal that the proposed method is less affected by noise, the population size of objects and background, and better segmentation results are obtained than Otsu’s method and other classical thresholding methods.

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Acknowledgements

This work is supported the Asia Foresight Program under NSFC Grant (Grant No. 61161140320), the National Natural Science Foundation of China (Grant No. 61121061), the Natural Scientific Research Foundation of the Higher Education Institutions of Hebei Province (Grant No. 2010165).

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Correspondence to Yinggan Tang.

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Tang, Y., Mu, W., Zhang, X. et al. Modified Fuzzy Linear Discriminant Analysis for Threshold Selection. Circuits Syst Signal Process 32, 711–726 (2013). https://doi.org/10.1007/s00034-012-9476-0

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  • DOI: https://doi.org/10.1007/s00034-012-9476-0

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