Multiple Classifier Systems in Texton-Based Approach for the Classification of CT Images of Lung

  • Mehrdad J. Gangeh
  • Lauge Sørensen
  • Saher B. Shaker
  • Mohamed S. Kamel
  • Marleen de Bruijne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6533)

Abstract

In this paper, we propose using texton signatures based on raw pixel representation along with a parallel multiple classifier system for the classification of emphysema in computed tomography images of the lung. The multiple classifier system is composed of support vector machines on the texton signatures as base classifiers and combines their decisions using product rule. The proposed approach is tested on 168 annotated regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. Texton-based approach in texture classification mainly has two parameters, i.e., texton size and k value in k-means. Our results show that while aggregation of single decisions by SVMs over various k values using multiple classifier systems helps to improve the results compared to single SVMs, combining over different texton sizes is not beneficial. The performance of the proposed system, with an accuracy of 95%, is similar to a recently proposed approach based on local binary patterns, which performs almost the best among other approaches in the literature.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mehrdad J. Gangeh
    • 1
  • Lauge Sørensen
    • 2
  • Saher B. Shaker
    • 3
  • Mohamed S. Kamel
    • 1
  • Marleen de Bruijne
    • 2
    • 4
  1. 1.Department of Electrical and Computer EngineeringUniversity of WaterlooCanada
  2. 2.Department of Computer ScienceUniversity of CopenhagenDenmark
  3. 3.Department of Respiratory MedicineGentofte University HospitalHellerupDenmark
  4. 4.Biomedical Imaging Group RotterdamErasmus MCThe Netherlands

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