Key result
A Computer Aided Diagnostic technique using texture features and an SVM classifier achieved an 89.5% accuracy in classifying carotid far wall IMT regions into symptomatic and asymptomatic classes.
Why the study?
Does a CAD technique using LBP and LTE texture features accurately classify carotid IMT regions into symptomatic and asymptomatic classes?
Does a CAD technique using LBP and LTE texture features accurately classify carotid IMT regions into symptomatic and asymptomatic classes?
A novel CAD technique using texture features and SVM achieved 89.5% accuracy in classifying carotid IMT regions as symptomatic or asymptomatic.
May support automated carotid plaque classification; leaves open prospective validation before clinical use.
In this work, we present a Computer Aided Diagnostic (CAD) technique (a class of Atheromatic systems) that classifies the automatically segmented carotid far wall Intima-Media Thickness (IMT) regions along the common carotid artery into symptomatic and asymptomatic classes. We extracted texture features based on Local Binary Patterns (LBP) and Law's Texture Energy (LTE) and used the significant features to train and test the Support Vector Machine classifier. We developed the classifiers using three-fold stratified cross validation data resampling technique on 342 IMT wall regions. An accuracy of 89.5% was registered. Thus, the proposed technique is accurate, robust, non-invasive, fast, objective, and cost-effective, and hence, will add more value to the existing carotid plaque diagnostics protocol.
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Acharya et al. (2012) studied Carotid artery disease (n=342). Computer Aided Diagnostic (CAD) technique using LBP and LTE texture features with SVM classifier was evaluated on Classification accuracy into symptomatic and asymptomatic classes. A Computer Aided Diagnostic technique using texture features and an SVM classifier achieved an 89.5% accuracy in classifying carotid far wall IMT regions into symptomatic and asymptomatic classes.
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