Fuzzy c-means clustering algorithm with a novel penalty term for image segmentation

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

Abstrakty

EN

Fuzzy clustering techniques, especially fuzzy c-means (FCM) clustering algorithm, have been widely used in automated image segmentation. However, as the conventional FCM algorithm does not incorporate any information about spatial context, it is sensitive to noise. To overcome this drawback of FCM algorithm, a novel penalized fuzzy c-means (PFCM) algorithm for image segmentation is presented in this paper. The algorithm is formulated by incorporating the spatial neighbourhood information into the original FCM algorithm with a penalty term. The penalty term acts as a regularizer in this algorithm, which is inspired by the neighbourhood expectation maximization (NEM) algorithm and is modified in order to satisfy the criterion of the FCM algorithm. Experimental results on synthetic, simulated and real images indicate that the proposed algorithm is effective and more robust to noise and other artifacts than the standard FCM algorithm.

Słowa kluczowe

EN

image segmentation; fuzzy clustering; fuzzy c-means; expectation maximization

• Wydawca: Stowarzyszenie Elektryków Polskich ; Polska Akademia Nauk ; Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
• Czasopismo: Opto-Electronics Review
• Rocznik: 2005
• Tom: Vol. 13, No. 4
• Strony: 309--315
• Opis fizyczny: Bibliogr. 22 poz., il.

Twórcy

autor

Yang Y.

• Key laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi'an Jiaotong University, 710049 Xi'an, China

autor

Zheng C.

• Key laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi'an Jiaotong University, 710049 Xi'an, China

autor

Lin P.

• Key laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi'an Jiaotong University, 710049 Xi'an, China

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