Integration of expert knowledge and image analysis techniques for medical diagnosis

  • Authors:

  • P. Spyridonos;E. I. Papageorgiou;P. P. Groumpos;G. N. Nikiforidis

  • Affiliations:

  • Computer Laboratory, School of Medicine, University of Patras, Rion, Greece;Laboratory for Automation and Robotics, Department of Electrical and Computer Engineering, University of Patras, Rion, Greece;Laboratory for Automation and Robotics, Department of Electrical and Computer Engineering, University of Patras, Rion, Greece;Computer Laboratory, School of Medicine, University of Patras, Rion, Greece

  • Venue:
  • ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
  • Year:
  • 2006

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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.