Skip to main content

Integration of Expert Knowledge and Image Analysis Techniques for Medical Diagnosis

  • Conference paper
Image Analysis and Recognition (ICIAR 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4142))

Included in the following conference series:

Abstract

This work reports a new methodology to develop a tumor grade diagnostic system, which is based on the integration of experts’ knowledge with image analysis techniques. The proposed system functions in two-levels and classify tumors according to their histological grade in three categories. In the lower-level, values of certain histopathological variables are automatically extracted by image analysis methods and feed the related concepts of a Fuzzy Cognitive Map (FCM) model. FCM model on the upper level interacts through a learning procedure to calculate the grade scores. Final class accuracy is estimated using the k-nearest classifier. The integrated FCM model yielded an accuracy of 63.63%, 72.41% and 84.21% for tumors of grade I, II, and III respectively. Results are promising, revealing new means for mining quantitative information and encoding significant concepts in decision process. The latter is very important in the field of computer aided diagnosis where the demand for reasoning and understanding is of main priority.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Choi, H., Vasko, J., Bengtsson, E., Jarkrans, T., Malmstrom, U., Wester, K., Busch, C.: Grading of transitional cell bladder carcinoma by texture analysis of histological sections. Anal. Cell Pathol. 6, 327–343 (1994)

    Google Scholar 

  2. Jarkrans, T., Vasko, J., Bengtsson, E., Choi, H., Malmstrom, U., Wester, K., Busch, C.: Grading of transitional cell bladder carcinoma by image analysis of histological sections. Anal. Cell Pathol. 18, 135–158 (1995)

    Google Scholar 

  3. Thiran, J.-P., Macq, B.: Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE T. Bio-Med. Eng. 43, 1011–1020 (1996)

    Article  Google Scholar 

  4. Mckeown, J.M., Ramsay, D.A.: Classification of astrocytomas and malignant astrocytomas by principal components analysis and a neural net. Journal of Neuropathology and Experimental Neurology 55, 1238–1245 (1996)

    Article  Google Scholar 

  5. Pena-Reyes, C.A., Sipper, M.: A fuzzy genetic approach to breast cancer diagnosis. Artif. Intell. Med. 17, 131–155 (1999)

    Article  Google Scholar 

  6. Spyridonos, P., Ravazoula, P., Cavouras, D., Berberidis, K., Nikiforidis, G.: Computer-based grading of haematoxylin-eosin stained tissue sections of urinary bladder carcinomas. Medical Informatics & The Internet in Medicine 26(3), 179–190 (2001)

    Article  Google Scholar 

  7. Belacel, N., Boulassel, M.: Multicriteria fuzzy assignment method: a useful tool to assist medical diagnosis. Artif. Intell. Med. 21, 201–207 (2001)

    Article  Google Scholar 

  8. Spyridonos, P., Cavouras, D., Ravazoula, P., Nikiforidis, G.: Neural network based segmentation and classification system for the automatic grading of histological sections of urinary bladder carcinoma. Analytical and Quantitative Cytology and Histology 24(6), 317–324 (2002)

    Google Scholar 

  9. Spyridonos, P., Cavouras, D., Ravazoula, P., Nikiforidis, G.: A Computer-based diagnostic and prognostic system for assessing urinary bladder tumor grade and predicting cancer recurrence. Medical Informatics & The Internet in Medicine 27(2), 111–122 (2002)

    Article  Google Scholar 

  10. Gil, J., Wu, H., Wang, B.Y.: Image analysis and morphometry in the diagnosis of breast cancer. Microsc. Res. Techniq. 59, 109–118 (2002)

    Article  Google Scholar 

  11. Tasoulis, D., Spyridonos, P., Pavlidis, N., Cavouras, D., Ravazoula, P., Nikiforidis, G., Vrahatis, M.: Urinary Bladder Tumor Grade Diagnosis Using On-Line Trained Neural Networks. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2773, pp. 199–206. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  12. Catto, J., Linkens, D., Abbod, M., Chen, M., Burton, J., Feeley, K., Hamdy, F.: Artificial Intelligence in Predicting Bladder Cancer Outcome: A Comparison of Neuro-Fuzzy Modeling and Artificial Neural Networks. Artif. Intell. Med. 9, 4172–4177 (2003)

    Google Scholar 

  13. Glotsos, D., Spyridonos, P., Petalas, P., Cavouras, D., Ravazoula, P., Dadioti, P., Lekka, I., Nikiforidis, G.: Computer-based malignancy grading of astrocytomas employing a Support Vector Machines Classifier, the WHO grading system, and the regular staining diagnostic procedure Hematoxylin-Eosin. Analytical and Quantitative Cytology and Histology 26(2), 77–83 (2004)

    Google Scholar 

  14. Antala, P., Fannesa, G., Timmermanb, D., Moreaua, Y., Moor, B.D.: Bayesian applications of belief networks and multilayer perceptrons for ovarian tumor classification with rejection. Artificial Intelligence in Medicine 29, 39–60 (2003)

    Article  Google Scholar 

  15. Kosko, B.: Fuzzy Cognitive Maps. Int. J. Man-Machine Studies 24, 65–75 (1986)

    Article  MATH  Google Scholar 

  16. Kosko, B.: Neural Networks and Fuzzy Systems. Prentice-Hall, New Jersey (1992)

    MATH  Google Scholar 

  17. Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: Active Hebbian Learning to Train Fuzzy Cognitive Maps. Int. J. Approx. Reasoning 37, 219–249 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  18. Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: Unsupervised learning techniques for fine-tuning Fuzzy Cognitive Maps causal links. Int. J. Human-Computer Studies (in press, 2006)

    Google Scholar 

  19. Papageorgiou, E.I., Parsopoulos, K.E., Stylios, C.D., Groumpos, P.P., Vrahatis, M.N.: Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization. Int. J. Intel. Inform. Syst. 25(1), 95–121 (2005)

    Article  Google Scholar 

  20. Papageorgiou, E.I., Groumpos, P.P.: A weight adaptation method for fine-tuning Fuzzy Cognitive Map causal links. Soft Computing Journal 9, 846–857 (2005)

    Article  MATH  Google Scholar 

  21. Papageorgiou, E.I., Spyridonos, P., Ravazoula, P., Stylios, C.D., Groumpos, P.P., Nikiforidis, G.: Advanced Soft Computing Diagnosis Method for Tumor Grading. Artif. Intell. Med. 36, 59–70 (2006)

    Article  Google Scholar 

  22. Epstein, J., Amin, M., Reuter, V., Mostofi, F., The Bladder Consensus Conference Committee: The World Health Organization/International Society of Urological Pathology Consensus Classification of Urothelial (Transitional Cell) neoplasms of the urinary bladder. The American Journal of Surgical Pathology 22(12), 1435–1448 (1998)

    Google Scholar 

  23. Murphy, W.M.: Urothelial neoplasia. In: Pathology and pathobiology of the urinary bladder and prostate, Williams & Wilkins, Baltimore (1992)

    Google Scholar 

  24. Carbin, B.E., Ekman, P., Gustafson, H., Christensen, N.J., Sandstedt, B., Silfversward, C.: Grading of human urothelial carcinoma based on nuclear atypia and mitotic frequency, Part I. Histological description, J. Urol 61, 968–971 (1998)

    Google Scholar 

  25. Bostwick, G., Ramnani, D., Cheng, L.: Diagnosis and grading of bladder cancer and associated lesions. Urologic Clinics of North America 26, 493–507 (1999)

    Article  Google Scholar 

  26. Van der Poel, H., Schaafsma, H.E., Vooijs, P.G., Debruyne, F.M.J., Schalken, J.A.: Review Article. Quantitative light microscopy in Urological Oncology. Journal of Urology 148, 1–13 (1992)

    Google Scholar 

  27. Harralick, R., Shanmugam, K.: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics 3(6), 610–621 (1973)

    Article  Google Scholar 

  28. Ohanian, P., Dubes, R.: Performance evaluation for four classes of textural features. Pattern Recognition 8(25), 819–833 (1992)

    Article  Google Scholar 

  29. Van Velthoven, R., Petein, M., Zlotta, A., Oosterlinck, W.J., Van Der Meijden, A., Zandona, C., Roels, H., Pasteels, J.-L., Schulman, C., Kiss, R.: Computer-assisted chromatin texture characterization of Feulgen-stained nuclei in a series of 331 transitional bladder cell carcinomas. J. Pathol. 173, 235–242 (1994)

    Article  Google Scholar 

  30. Walker, R.F., Jackway, P., Longstaff, I.D.: Improving Co-occurrence Matrix Feature Discrimination. In: Proceedings of DICTA 1995, 3rd Conf. Digital Image Computing: Techniques & Applications, December 6-8, 1995, pp. 643–648 (1995)

    Google Scholar 

  31. Walker, R.F., Jackway, P.T., Lovell, B.: Cervical cell classification via co-occurrence and Markov random field features. In: Proceedings of Digital Image Computing: Techniques and Applications, pp. 294–299 (1995)

    Google Scholar 

  32. Lin, C.T., Lee, C.S.: Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Prentice Hall, Upper Saddle River (1996)

    Google Scholar 

  33. Mostofi, F.K., Davis, C.J., Sesterhenn, I.A.: WHO histologic typing of urinary bladder tumors. Springer, Berlin (1999)

    Google Scholar 

  34. Ooms, E., Anderson, W., Alons, C., Boon, M., Veldhuizen, R.: Analysis of the performance of pathologists in grading of bladder tumors. Human Pathology 14, 140–143 (1983)

    Article  Google Scholar 

  35. Young, R.H.: Papillary lesions of the bladder: a historical prospective with discussion of the WHO/ISUP consensus classification system. In: The United States and Canadian Academy of Pathology Annual Meeting-ISUP Companion Meeting, San Francisco, CA, March 20-26 (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Spyridonos, P., Papageorgiou, E.I., Groumpos, P.P., Nikiforidis, G.N. (2006). Integration of Expert Knowledge and Image Analysis Techniques for Medical Diagnosis. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867661_11

Download citation

  • DOI: https://doi.org/10.1007/11867661_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44894-5

  • Online ISBN: 978-3-540-44896-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics