Toward unsupervised classification of calcified arterial lesions.
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 intracluster compactness measure. Experimental results indicate an average accuracy of approximately 80% on a database of electron beam CT heart scans.
Brunner
G
G
Computational Biomedicine Lab, Depts. of Computer Science, Elec. & Comp. Engineering, and Biomedical Engineering, Univ. of Houston, Houston, TX, USA. gbrunner@uh.edu
Kurkure
U
U
Chittajallu
D R
DR
Yalamanchili
R P
RP
Kakadiaris
I A
IA
D000465:Algorithms; D001185:Artificial Intelligence; D002114:Calcinosis / Q000000981:diagnostic imaging*; D017023:Coronary Angiography / Q000379:methods*; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*