Toward Unsupervised Classification of Calcified Arterial Lesions

Authors:
Gerd Brunner;Uday Kurkure;Deepak R. Chittajallu;Raja P. Yalamanchili;Ioannis A. Kakadiaris
Affiliations:
Computational Biomedicine Lab, Depts. of Computer Science, Elec. & Comp. Engineering, and Biomedical Engineering, Univ. of Houston, Houston, USA;Computational Biomedicine Lab, Depts. of Computer Science, Elec. & Comp. Engineering, and Biomedical Engineering, Univ. of Houston, Houston, USA;Computational Biomedicine Lab, Depts. of Computer Science, Elec. & Comp. Engineering, and Biomedical Engineering, Univ. of Houston, Houston, USA;Computational Biomedicine Lab, Depts. of Computer Science, Elec. & Comp. Engineering, and Biomedical Engineering, Univ. of Houston, Houston, USA;Computational Biomedicine Lab, Depts. of Computer Science, Elec. & Comp. Engineering, and Biomedical Engineering, Univ. of Houston, Houston, USA
Venue:
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Year:
2008

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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.