Fuzzy clustering with spatial constraints for image thresholding

• Autorzy: Yang Y. , Zheng C. , Lin P.
• Języki publikacji: EN

Abstrakty

EN

Image thresholding plays an important role in image segmentation. This paper presents a novel fuzzy clustering based image thresholding technique, which incorporates the spatial neighborhood information into the standard fuzzy c-means (FCM) clustering algorithm. The prior spatial constraint, which is defined as weight in this paper, is inspired by the k-nearest neighbor (k-NN) algorithm and is modified from two aspects in order to improve the performance of image thresholding. The algorithm is initialized by a fast FCM algorithm, in which the iteration is carried out with the statistical gray level histogram of image instead of the conventional whole data of image; therefore its convergence is fast. Extensive experiment results and both qualitative and quantitative comparative studies with several existing methods on the thresholding of some synthetic and real images illustrate the effectiveness and robustness of the proposed algorithm.

Słowa kluczowe

EN

image thresholding; fuzzy c-means; k-nearest neighbo; fuzzy thresholding

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2005
• Tom: Vol. 35, nr 4
• Strony: 943--954
• Opis fizyczny: Bibliogr. 19 poz.

Twórcy

autor

Yang Y.

• Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China

autor

Zheng C.

• Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China

autor

Lin P.

• Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China

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