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)


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.


Feature Space Lesion Feature Arterial Lesion Cophenetic Correlation Cluster Step 
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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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