Carotid far wall characterization using LBP, Laws' Texture Energy and wall variability: a novel class of Atheromatic systems.
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.
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
U Rajendra
UR
Acharya
Vinitha Sree
VS
S
Muthu Rama Krishnan
MR
M
Luca
L
Saba
Filippo
F
Molinari
Shoaib
S
Shafique
Andrew
A
Nicolaides
Jasjit S
JS
Suri
D000465:Algorithms; D056808:Biostatistics; D002340:Carotid Artery Diseases / Q000175:diagnosis / Q000000981:diagnostic imaging; D017536:Carotid Artery, Common / Q000000981:diagnostic imaging; D059168:Carotid Intima-Media Thickness; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D058226:Plaque, Atherosclerotic / Q000175:diagnosis / Q000000981:diagnostic imaging; D060388:Support Vector Machine