J Med Syst - Symptomatic vs. asymptomatic plaque classification in carotid ultrasound.

Tópicos

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Resumo

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.

Resumo Limpo

quantit character carotid atherosclerosi classif symptomat asymptomat type crucial diagnosi treatment plan paper describ computeraid diagnosi cad system analyz ultrasound imag classifi symptomat asymptomat base textur featur propos cad system consist three modul first modul preprocess condit imag subsequ featur extract featur extract stage use imag textur analysi calcul standard deviat entropi symmetri run percentag final classif perform use adaboost support vector machin autom decis make adaboost compar perform five distinct configur least squar maximum likelihood normal densiti discrimin function pocket stump algorithm support vector machin compar perform use five differ configur linear kernel polynomi kernel configur differ order radial basi function kernel svm radial basi function kernel support vector machin present best classif result classif accuraci sensit specif feel textur featur coupl support vector machin classifi can use identifi plaqu tissu type integr index call symptomat asymptomat carotid index saci propos use textur featur discrimin symptomat asymptomat carotid ultrasound imag use just one index number hope saci can use adjunct tool vascular surgeon daili screen

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