Skip to main content
SpringerLink
Log in

A Preliminary Analysis on HEp-2 Pattern Classification: Evaluating Strategies Based on Support Vector Machines and Subclass Discriminant Analysis

  • Conference paper
  • First Online:
Biomedical Engineering Systems and Technologies (BIOSTEC 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 452))

Abstract

The categorization of different staining patterns in HEp-2 cell slides by means of indirect immunofluorescence (IIF) is important for the differential diagnosis of autoimmune diseases. The clinical practice usually relies on the visual evaluation of the slides, which is time-consuming and subject to the specialist’s experience. Thus, there is a growing demand for computer-aided solutions capable of automatically classifying HEp-2 staining patterns. In the attempt to identify the most suited strategy for this task, in this work we compare two approaches based on Support Vector Machines and Subclass Discriminant Analysis. These techniques classify the available samples, characterized through a limited set of optimal textural attributes that are identified with a feature selection scheme. Our experimental results show that both strategies have a good concordance with the diagnosis of the human specialist and show the better performance of the Subclass Discriminant Analysis (91 % accuracy) compared to Support Vector Machines (87 % accuracy).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Egerer, K., Roggenbuck, D., Hiemann, R., Weyer, M.G., Buttner, T., Radau, B., Krause, R., Lehmann, B., Feist, E., Burmester, G.R.: Automated evaluation of autoantibodies on human epithelial-2 cells as an approach to standardize cell-based immunofluorescence tests. Arthritis Res. Ther. 12(2), 1–9 (2010)

    Article  Google Scholar 

  2. Creemers, C., Guerti, K., Geerts, S., Van Cotthem, K., Ledda, A., Spruyt, V.: HEp-2 cell pattern segmentation for the support of autoimmune disease diagnosis. In: Proceedings of ISABEL 2011, vol. 28, pp. 1–5 (2011)

    Google Scholar 

  3. Hsieh, R.Y., Huang, Y.C., Chung, C.W., Huang, Y.L.: HEp-2 cell classification in indirect immunofuorescence images. In: Proceedings of ICICS 2009, vol. 26, pp. 211–214 (2009)

    Google Scholar 

  4. Perner, P., Perner, H., Muller, B.: Mining knowledge for HEp-2 cell image classification. Artif. Intell. Med. 26, 161–173 (2002)

    Article  Google Scholar 

  5. Sack, U., Knoechner, S., Warschkau, H., Pigla, U., Emmrich, F., Kamprad, M.: Computer-assisted classification of HEp-2 immunofluorescence patterns in autoimmune diagnostics. Autoimmun. Rev. 2(5), 298–304 (2003)

    Article  Google Scholar 

  6. Soda, P., Iannello, G.: A hybrid multi-expert systems for HEp-2 staining pattern classification. In: Proceedings of ICIAP 2007, pp. 685–690 (2007)

    Google Scholar 

  7. Di Cataldo, S., Bottino, A., Ficarra, E., Macii, E.: Applying textural features to the classification of HEp-2 cell patterns in IIF images. In: 21st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, Japan, 11–15 November (2012)

    Google Scholar 

  8. MIVIA Lab. http://nerone.diiie.unisa.it/zope/mivia/databases/db_database/biomedical/. Accessed: September 2012

  9. Tozzoli, R., Bizzaro, N., Tonutti, E., Villalta, D., Bassetti, D., Manoni, F., Piazza, A., Pradella, M., Rizzotti, P.: Guidelines for the laboratory use of autoantibody tests in the diagnosis and monitoring of autoimmune rheumatic diseases. Am. J. Clin. Pathol. 117(2), 316–324 (2002)

    Article  Google Scholar 

  10. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)

    Article  Google Scholar 

  11. Soh, L., Tsatsoulis, C.: Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans. Geosci. Remote Sens. 37(2), 780–795 (1999)

    Article  Google Scholar 

  12. Clausi, D.A.: An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sens. 28(1), 45–62 (2002)

    Article  Google Scholar 

  13. Sorwar, G., Abraham, A., Dooley, L.S.: Texture classification based on DCT and soft computing. In: Proceedings of FUZZ-IEEE 01, 2–5 December 2001

    Google Scholar 

  14. Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE Trans. Comput. 23, 90–93 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  15. Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)

    Article  Google Scholar 

  16. Temko, A., Camprubi, C.N.: Classification of acoustic events using SVM-based clustering schemes. Pattern Recognit. 39(4), 682–694 (2006)

    Article  MATH  Google Scholar 

  17. Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., Vapnik, V.: Feature selection for SVMs. Adv. Neural Inf. Process. Syst. 13, 668–674 (2000)

    Google Scholar 

  18. Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. PAMI 27, 1226–1238 (2005)

    Article  Google Scholar 

  19. Kurgan, L.A., Cios, K.J.: Caim discretization algorithm. IEEE Trans. Knowl. Data Eng. 16(2), 145–153 (2004)

    Article  Google Scholar 

  20. Ververidis, D., Kotropoulos, C.: Fast and accurate feature subset selection applied into speech emotion recognition. Els. Sig. Process. 88(12), 2956–2970 (2008)

    Article  MATH  Google Scholar 

  21. Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, San Diego (1990)

    MATH  Google Scholar 

  22. Swets, D.L., Weng, J.J.: Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 831–836 (1996)

    Article  Google Scholar 

  23. Etemad, K., Chellapa, R.: Discriminant analysis for recognition of human face images. J. Optical Soc. Am. A 14(8), 1724–1733 (1997)

    Article  Google Scholar 

  24. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Article  Google Scholar 

  25. Boulgouris, N.V., Plataniotis, K.N., Micheli-Tzanakou, E.: Discriminant analysis for dimensionality reduction: an overview of recent developments. In: Biometrics: Theory, Methods, and Applications. Wiley, New York (2009)

    Google Scholar 

  26. Zhu, M., Martinez, A.M.: Subclass discriminant analysis. IEEE Trans. PAMI 28(8), 1274–1286 (2006)

    Article  Google Scholar 

  27. Martinez, A.M., Zhu, M.: Where are linear feature extraction methods applicable? IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1934–1944 (2005)

    Article  Google Scholar 

  28. Gkalelis, N., Mezaris, V., Kompatsiaris, I.: Mixture subclass discriminant analysis. IEEE Sig. Process. Lett. 18(5), 319–322 (2011)

    Article  Google Scholar 

  29. Kim, S.-W.: A pre-clustering technique for optimizing subclass discriminant analysis. Pattern Recogn. Lett. 31(6), 462–468 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy

    Ihtesham Ul Islam, Santa Di Cataldo, Andrea Bottino, Enrico Macii & Elisa Ficarra

Authors
  1. Ihtesham Ul Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Santa Di Cataldo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrea Bottino
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Enrico Macii
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Elisa Ficarra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea Bottino .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Mireya Fernández-Chimeno

  2. Instituto Gulbenkian de Ciência, Oeiras, Portugal

    Pedro L. Fernandes

  3. Boston College, Chestnut Hill, Massachusetts, USA

    Sergio Alvarez

  4. University of Guelph, Guelph, Canada

    Deborah Stacey

  5. University of Vic, Vic, Spain

    Jordi Solé-Casals

  6. Technical University of Lisbon, Lisbon, Portugal

    Ana Fred

  7. New University of Lisbon, Lisboa, Portugal

    Hugo Gamboa

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ul Islam, I., Di Cataldo, S., Bottino, A., Macii, E., Ficarra, E. (2014). A Preliminary Analysis on HEp-2 Pattern Classification: Evaluating Strategies Based on Support Vector Machines and Subclass Discriminant Analysis. In: Fernández-Chimeno, M., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2013. Communications in Computer and Information Science, vol 452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44485-6_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-44485-6_13

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44484-9

  • Online ISBN: 978-3-662-44485-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation