Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization

Authors:
U. Rajendra Acharya;Oliver Faust;Vinitha Sree S.;A. P. C. Alvin;Ganapathy Krishnamurthi;José C. R. Seabra;JoãO Sanches;Jasjit S. Suri
Affiliations:
Department of Electrical and Computer Engineering, Ann Polytechnic, Singapore 599489, Singapore and Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia;Aberdeen University, Aberdeen, Scotland AB24 3FX, UK;Global Biomedical Technologies Inc., CA, USA;Department of Electrical and Computer Engineering, Ann Polytechnic, Singapore 599489, Singapore;Case Western Reserve University in Cleveland, OH, USA;Department of Electrical and Computer Engineering, Instituto Superior Técnico, Portugal;Department of Electrical and Computer Engineering, Instituto Superior Técnico, Portugal;Fellow AIMBE, CTO, Department of Diagnostic and Monitoring Division, AtheroPoint, LLC, Roseville, CA 95661, USA and Idaho State University (Aff.), ID, USA
Venue:
Computer Methods and Programs in Biomedicine
Year:
2013

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Abstract

Characterization of carotid atherosclerosis and classification into either symptomatic or asymptomatic is crucial in terms of diagnosis and treatment planning for a range of cardiovascular diseases. This paper presents a computer-aided diagnosis (CAD) system (Atheromatic) that analyzes ultrasound images and classifies them into symptomatic and asymptomatic. The classification result is based on a combination of discrete wavelet transform, higher order spectra (HOS) and textural features. In this study, we compare support vector machine (SVM) classifiers with different kernels. The classifier with a radial basis function (RBF) kernel achieved an average accuracy of 91.7% as well as a sensitivity of 97%, and specificity of 80%. Thus, it is evident that the selected features and the classifier combination can efficiently categorize plaques into symptomatic and asymptomatic classes. Moreover, a novel symptomatic asymptomatic carotid index (SACI), which is an integrated index that is based on the significant features, has been proposed in this work. Each analyzed ultrasound image yields on SACI number. A high SACI value indicates that the image shows symptomatic and low value indicates asymptomatic plaques. We hope this SACI can support vascular surgeons during routine screening for asymptomatic plaques.