Fingerprint image segmentation using modified fuzzy c-means algorithm

Jia-Yin Kang; Cheng-Long Gong; Wen-Juan Zhang

doi:10.4236/jbise.2009.28096

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JBiSE> Vol.2 No.8, December 2009

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Fingerprint image segmentation using modified fuzzy c-means algorithm

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DOI: 10.4236/jbise.2009.28096 6,038 Downloads 11,951 Views Citations

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Jia-Yin Kang, Cheng-Long Gong, Wen-Juan Zhang

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ABSTRACT

Fingerprint segmentation is a crucial step in fingerprint recognition system, and determines the results of fingerprint analysis and recognition. This paper proposes an efficient approach for fingerprint segmentation based on modified fuzzy c-means (FCM). The proposed method is realized by modifying the objective function in the Szilagyi’s algorithm via introducing histogram-based weight. Experimental results show that the proposed approach has an efficient performance while segmenting both original fingerprint image and fingerprint images corrupted by different type of noises.

KEYWORDS

Fingerprint; Segmentation; Fuzzy C-means; Histogram; Robustness

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Kang, J. , Gong, C. and Zhang, W. (2009) Fingerprint image segmentation using modified fuzzy c-means algorithm. Journal of Biomedical Science and Engineering, 2, 656-660. doi: 10.4236/jbise.2009.28096.

References

[1]	J. P. Yin, E. Zhu and X. J. Yang. (2007) Two steps for fingerprint segmentation. Image and Vision Computing, 25, 1391–1403.

[2]	D. Zhang and Y. Wang. (2006) Medical image segmentation based on FCM clustering and rough set. Chinese Journal of Scientific Instrument, 27, 1683–1687.

[3]	W. J. Chen, M. L. Giger and U. Bick. (2006) A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. Academic Radiology, 13, 63–72.

[4]	J. M. Gorriz, J. Ramirez and E. W. Lang. (2006) Hard c-means clustering for voice activity detection. Speech Communication, 48, 1638–1649.

[5]	K. S. Chuang, H. L. Tzeng and S. W. Chen. (2006) Fuzzy c-means clustering with spatial information for image segmentation. Computerized Medical Imaging and Graphics, 30, 9–15.

[6]	J. Li, X. B. Gao and L. C. Jiao, (2006) A new feature weighted fuzzy clustering algorithm, Acta Electronica Sinica, 34, 89–92.

[7]	S. C. Chen and D. Q. Zhang. (2004) Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans. Systems Man Cybernet, B, 34, 1907–1916.

[8]	L. Szilagyi, Z. Benyo and S. M. Szilagyii. (2003) MR brain image segmentation using an enhanced fuzzy c- means algorithm. 25th Annual International Conference of IEEE EMBS, 17–21.