Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100243
Title: Symptomatic vs. asymptomatic plaque classification in carotid ultrasound
Authors: Suri, Jasjit S.
Acharya, U. Rajendra
Faust, Oliver
Alvin, Ang Peng Chuan
Sree, Subbhuraam Vinitha
Molinari, Filippo
Saba, Luca
Nicolaides, Andrew
Keywords: DRNTU::Science::Medicine
Issue Date: 2011
Source: Acharya, U. R., Faust, O., Alvin, A. P. C., Sree, S. V., Molinari, F., Saba, L., et al. (2011). Symptomatic vs. asymptomatic plaque classification in carotid ultrasound. Journal of medical systems, 36(3), 1861-1871.
Series/Report no.: Journal of medical systems
Abstract: Quantitative characterization of carotid atherosclerosis and classification into symptomatic or asymptomatic type is crucial in both diagnosis and treatment planning. This paper describes a computer-aided diagnosis (CAD) system which analyzes ultrasound images and classifies them into symptomatic and asymptomatic based on the textural features. The proposed CAD system consists of three modules. The first module is preprocessing, which conditions the images for the subsequent feature extraction. The feature extraction stage uses image texture analysis to calculate Standard deviation, Entropy, Symmetry, and Run Percentage. Finally, classification is performed using AdaBoost and Support Vector Machine for automated decision making. For Adaboost, we compared the performance of five distinct configurations (Least Squares, Maximum- Likelihood, Normal Density Discriminant Function, Pocket, and Stumps) of this algorithm. For Support Vector Machine, we compared the performance using five different configurations (linear kernel, polynomial kernel configurations of different orders and radial basis function kernels). SVM with radial basis function kernel for support vector machine presented the best classification result: classification accuracy of 82.4%, sensitivity of 82.9%, and specificity of 82.1%. We feel that texture features coupled with the Support Vector Machine classifier can be used to identify the plaque tissue type. An Integrated Index, called symptomatic asymptomatic carotid index (SACI), is proposed using texture features to discriminate symptomatic and asymptomatic carotid ultrasound images using just one index or number. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screening.
URI: https://hdl.handle.net/10356/100243
http://hdl.handle.net/10220/13605
DOI: http://dx.doi.org/10.1007/s10916-010-9645-2
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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