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Toward Unsupervised Classification of Calcified Arterial Lesions

  • Gerd Brunner
  • Uday Kurkure
  • Deepak R. Chittajallu
  • Raja P. Yalamanchili
  • Ioannis A. Kakadiaris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5241)

Abstract

There is growing evidence that calcified arterial deposits play a crucial role in the pathogenesis of cardiovascular disease. This paper investigates the challenging problem of unsupervised calcified lesion classification. We propose an algorithm, US-CALC (UnSupervised Calcified Arterial Lesion Classification), that discriminates arterial lesions from non-arterial lesions. The proposed method first mines the characteristics of calcified lesions using a novel optimization criterion and then identifies a subset of lesion features which is optimal for classification. Second, a two stage clustering is deployed to discriminate between arterial and non-arterial lesions. A histogram intersection distance measure is incorporated to determine cluster proximity. The clustering hierarchies are carefully validated and the final clusters are determined by a new intra-cluster compactness measure. Experimental results indicate an average accuracy of approximately 80% on a database of electron beam CT heart scans.

Keywords

Feature Space Lesion Feature Arterial Lesion Cophenetic Correlation Cluster Step 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Gerd Brunner
    • 1
  • Uday Kurkure
    • 1
  • Deepak R. Chittajallu
    • 1
  • Raja P. Yalamanchili
    • 1
  • Ioannis A. Kakadiaris
    • 1
  1. 1.Computational Biomedicine Lab, Depts. of Computer Science, Elec. & Comp. Engineering, and Biomedical EngineeringUniv. of HoustonHoustonUSA

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