An image segmentation method based on maximizing fuzzy correlation and its fast recursive algorithm |
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| Authors: | Yinggan Tang Weiwei Mu Lixing Zhao Gang Zhao |
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| Affiliation: | 1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China;2. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Qinhuangdao, Hebei 066004, China;3. Rochester Institute of Technology, Rochester, NY 14624, United States |
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| Abstract: | In this paper, an image segmentation method is proposed that integrates fuzzy 2-partition into Yen’s maximum correlation thresholding method. A fuzzy 2-partition of the image is obtained by transforming the image into fuzzy domain by means of two parameterized membership functions. Fuzzy correlation is defined to measure the appropriateness of the fuzzy 2-partition. An ideal threshold is calculated from the optimal membership functions’ parameters, which make the corresponding fuzzy 2-partition have maximum fuzzy correlation. In the process of searching the optimal parameters of membership functions, a fast recursive algorithm is presented in order to reduce the computation complexity. Experimental results on synthetic image, brain magnetic resonance (MR) images and casting images show that the proposed method has a satisfactory performance. |
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