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Fuzzy segmentation for finger vessel pattern extraction of infrared images

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Abstract

In this paper, an algorithm for robust finger vessel pattern extraction from infrared images is presented based on image processing, edge suppression, fuzzy enhancement, and fuzzy clustering. Initially, the brightness variations of the images are eliminated using histogram normalization and the vessel patterns are enhanced, to facilitate the separation process from other tissue parts through fuzzy clustering. Several vessel measurements and processes are applied, including the second-order local statistical information contained in the Hessian matrix and the matched filter applied in the direction of the largest curvature. Edge suppression reduces sharp brightness changes at finger borders and image contrast is non-linearly increased through fuzzy enhancement. A novel probabilistic fuzzy C-means clustering algorithm is used to derive vessels from the surrounding tissue regions using spatial information in the membership function. Therefore, as shown experimentally, better segmentation and classification rates than the standard C-means algorithm is achieved using the primitive set of features. Moreover, the segmentation results are validated using two cluster-based functions: partition coefficient and partition entropy. Over-segmentation conditions are handled using a two-stage morphological post-processing. Morphological majority filter smoothes the vessel contours and removes small isolated regions which have been misclassified as vessels. Morphological reconstruction is used to obtain outlier-free vessel pattern. The proposed algorithm is evaluated both in real and artificially created images and under different noise types and signal-to-noise ratios, giving excellent segmentation accuracy in the main vessels, even in case where strong artificial noise is used to distort the images. Furthermore, the algorithm can readily be applied in many image enhancement and segmentation applications.

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References

  1. Miura N, Nagasaka A, Miyatake T (2004) Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Mach Vis Appl 15:194–203

    Article  Google Scholar 

  2. Miura N, Nagasaka A, Miyatake T (2004) Feature extraction of finger-vein patterns based on iterative line tracking and its application to personal identification. Syst Comput Jpn 35(7):61–71

    Article  Google Scholar 

  3. Vlachos M, Dermatas E (2006) A finger vein pattern extraction algorithm based on filtering in multiple directions. In: 5th European symposium on biomedical engineering, July 2006

  4. Wu CH, Agam G, Stanchev P (2007) A general framework for vessel segmentation in retinal images. International symposium on computational intelligence in robotics and automation (CIRA), June 2007, pp 37–42

  5. Park GT, Im SK, Choi HS (1997) A person identification algorithm utilizing hand vein pattern. Proc Korea Signal Process Conf 10(1):1107–1110

    Google Scholar 

  6. Im SK, Park HM, Kim SW (2001) A biometric identification system by extracting hand vein patterns. J Korean Phys Soc 38(3):268–272

    Google Scholar 

  7. Hong DU, Im SK, Choi HS (1999) Implementation of real time system for personal identification algorithm utilizing hand vein pattern. Proc IEEK Fall Conf 22(2):560–563

    Google Scholar 

  8. Im SK, Park HM, Kim SW, Chung CK, Choi HS (2000) Improved vein pattern extracting algorithm and its implementation. In: Proceedings of IEEE ICCE, pp 2–3

  9. Im SK, Choi HS, Kim SW (2003) Direction-based vascular pattern extraction algorithm for hand vascular pattern verification, Korea University, Seoul, Korea. ETRI J 25(2)

  10. Vlachos M, Dermatas E (2013) A segmentation method using multiscale and multidirectional matched filters based on dyadic wavelet transform for finger vein pattern extraction. Electr Eng Res 1(1):1–9

  11. Vlachos M, Dermatas E (2009) Finger vein segmentation and centerlines extraction in infrared images. Advanced topics in scattering theory and biomedical engineering: Proceedings of the 9th international workshop on mathematical methods on scattering theory and biomedical engineering, Patras, Greece, 9–11 October 2009, pp 345–352

  12. Tanaka T, Kubo N (2004) Biometric authentication by hand vein patterns. SICE annual conference in Sapporo, August 2004

  13. Ding Y, Zhuang D, Wang K (2005) A study of hand vein recognition method. In: Proceedings of the IEEE international conference on mechatronics and automation, Niagara Falls, Canada, July 2005

  14. Paquit V, Price JR, Seulin R, Meriaudeau F, Farahi RH, Tobin KW, Ferrell TL (2006) Near-infrared imaging and structured light ranging for automatic catheter insertion. In: Proceedings of SPIE 6141, medical imaging 2006: visualization, image-guided procedures, and display

  15. Zeman HD, Lovhoiden G, Vrancken C (2002) The clinical evaluation of vein contrast enhancement. Proc SPIE 4615:61–70

    Article  Google Scholar 

  16. Zeman HD, Lovhoiden G, Vrancken C (2005) Prototype vein contrast enhancer. Opt Eng 44(8):086401

  17. Bezdek J, Hall L, Clarke L (1993) Review of MR image segmentation using pattern recognition. Med Phys 20:1033–1048

    Article  Google Scholar 

  18. Lyer NS, Kandel A, Schneider M (2002) Feature-based fuzzy classification for interpretation of mammograms. Fuzzy Sets Syst 114:271–280

    Google Scholar 

  19. Yang MS, Hu YJ, Lin KC, Lin CC (2002) Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithms. Magn Reson Imaging 20:173–179

    Article  Google Scholar 

  20. Pedrycz W, Waletzky J (1997) Fuzzy clustering with partial supervision. IEEE Trans Syst Man Cybern Part B Cybern 27:787–795

    Article  Google Scholar 

  21. Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T (2002) A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 21:193–199

    Article  Google Scholar 

  22. Chuang KS, Tzeng HL, Chen S, Wu J, Chen TJ (2006) Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30:9–15

    Article  Google Scholar 

  23. Florack LMJ et al (1992) Scale and the differential structure of images. Imaging Vis Comput 10(6):376–388

    Article  MathSciNet  Google Scholar 

  24. Koenderink JJ (1984) The structure of images. Biol Cybern 50:363–370

    Article  MATH  MathSciNet  Google Scholar 

  25. Frangi AF et al (1998) Multiscale vessel enhancement filtering, medical image computing and computer-assisted interventation—MICCAI’98. Lecture notes in computer science, vol 1496, pp 130–137

  26. Lindeberg T (1996) Edge detection and ridge detection with automatic scale selection. In: Proceedings of conference on computer vision and pattern recognition, San Francisco, pp 465–470

  27. Otsu N (1979) A threshold selection method from grey-level histograms. IEEE Trans Syst Man Cybern. SMC-9, pp 62–66

  28. Yu C, Qing H, Zhang L (2008) A research on extracting low quality human finger vein pattern characteristics. In: 2nd international conference on bioinformatics and biomedical engineering (ICBBE), pp 1876–1879

  29. Bezdek JC (1974) Cluster validity with fuzzy sets. J Cybern 3:58–73

    Article  MathSciNet  Google Scholar 

  30. Bezdek JC (1975) Mathematical models for systematic and taxonomy. In: Proceedings of eighth international conference on numerical taxonomy, pp 143–66

  31. Gongalez R, Woods R, Eddins S (2003) Digital image processing using matlab. Prentice Hall, Englewood Cliffs

    Google Scholar 

  32. Vincent L (1993) “Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans Pattern Anal Mach Intell 13(6):583–598

    Article  MathSciNet  Google Scholar 

  33. Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M (1989) Detection of blood vessels in retinal images using two dimensional matched filters. IEEE Trans Med Imaging 8(3):263–269

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Technology, University of Patras, Patras, Greece

    Marios Vlachos & Evangelos Dermatas

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  1. Marios Vlachos
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  2. Evangelos Dermatas
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Correspondence to Marios Vlachos.

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Vlachos, M., Dermatas, E. Fuzzy segmentation for finger vessel pattern extraction of infrared images. Pattern Anal Applic 18, 901–919 (2015). https://doi.org/10.1007/s10044-014-0413-7

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  • DOI: https://doi.org/10.1007/s10044-014-0413-7

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