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Fingerprint classification using computational intelligence algorithms in medical diagnostics

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Fingerprint classification is used by anthropologist in detection of genetic disorders in infants.This paper describes application of image processing and pattern recognition methods in classification of fingerprints. Fingerprint classifiers, which are part of an automatic system for rapid screen diagnosing of trisomy 21 (Down Syndrome) in infants, are created and discussed. The system is a tool supporting medical decision by automatic processing of dermatoglyphic prints and detecting features indicating presence of genetic disorder. Images of dermatoglyphic prints are pre-processed before the classification stage to extract features analyzed by the Support Vector Machines algorithm. Application of an algorithm based on multi-scale pyramid decomposition of the image is proposed for the ridge orientation calculation. RBF and triangular kernel types are used in the training of SVM multi-class systems generated with the one–vs–one scheme. The experiments conducted on the database of the Collegium Medicum Jagiellonian University in Cracow show the effectiveness of the proposed approach in classification of infants’ fingerprints.
Rocznik
Strony
95--100
Opis fizyczny
Bibliogr. 14 poz., rys., zdj.
Twórcy
autor
  • Akademia Górniczo Hutnicza, Wydział EAiE
autor
  • Katedra Automatyki, Uniwersytet Rzeszowski, Wydział Matematyczno-Przyrodniczy
autor
  • Katedra Informatyki
Bibliografia
  • 1. Berg C.: Harmonic Analysis on Semigroups: Theory of Positive Definite and Related Functions, Springer–Verlag, Berlin 1984.
  • 2. Boughorbel S., Tarel J.P.: Conditionally positive definite kernels for SVM based image recognition. IEEE International Conference on Multimedia and Expo (ICME) 2005: 113-116.
  • 3. Chikkerur S., Cartwright A.N., Govindaraju V.: Fingerprint enhancement using STFT analysis. Pattern Recognition 2007, vol. 40: 198 – 211.
  • 4. Feng X.G., Milanfar P.: Multiscale principal components analysis for image local orientation estimation. The 36th Asilomar Conference on Signals, Systems and Computers 2002, vol. 1: 478- 482.
  • 5. Fleuret F., Sahbi H.: Scale-invariance of support vector machines based on the triangular kernel. Proceedings of the Workshop on Statistical and Computational Theories of Vision of the IEEE International Conference on Computer Vision (ICCV/SCTV) 2003, online.
  • 6. Maltoni D., Maio D., Jain A.K., Prabhakar S.: Handbook of Fingerprint Recognition. Springer–Verlag, Berlin 2003.
  • 7. Otsu N.: A threshold selection method from gray-level histogram. IEEE Transactions on Systems, Man and Cybernetics 1979, vol. 9, no. 1: 62-66.
  • 8. Preuss M.: A screening test for patients suspected of having Turner syndrome. Clinical Genetics 1976, no. 10: 145-155.
  • 9. Reed T.: Dermatoglyphics in Down's syndrome. Clinical Genetics 1974, no. 6: 236.
  • 10. Reed T.E., Borgaonkar D.S., Conneally P.M., Yu P., Nance W.E. & Christian J.C.: Dermatoglyphic nomogram for the diagnosis of Down's syndrome. J. Pediat. 1970, no. 77: 1024-1032.
  • 11. Scholkopf B.: The kernel trick for distances. Proceedings of Neural Information Processing Systems (NIPS) 2000: 301-307.
  • 12. Steinwart I., Christmann A.: Support Vector Machines. Springer Science, 2008.
  • 13. Tornjova–Randelova S.G.: Dermatoglyphic characteristics of patients with Turner’s syndrome. Medicine Anthropologie 1990, vol. 43, no. 4: 96-100.
  • 14. Zuiderveld K.: Contrast Limited Adaptive Histogram Equalization, Graphics Gems IV. Academic Press, 1994.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-016c8db9-c918-4864-acc6-aefeba2138e4
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