Towards cardiovascular risk stratification using imaging data.

In spite of the advancement and proliferation of cardiovascular imaging data, the rate of deaths due to unpredicted heart attack remains high. Thus, it becomes imperative to develop novel computational tools to mine quantitative parameters from imaging data for early detection and diagnosis of asymptomatic cardiovascular disease. In this paper, we present our progress towards developing a computational framework to mine cardiac imaging data and provide quantitative measures for developing a new risk assessment method. Specifically, we present computational methods developed for the detection of coronary calcification and segmentation of thoracic aorta in non-contrast cardiac computed tomography, and detection of neovessels in plaques in intravascular ultrasound imaging data.

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.