title: Prediction of atheromatic plaque evolution in carotids using features extracted from the arterial geometry.
abstract: Knowing the arterial geometry might be helpful in the assessment of a plaque rupture event. We present a proof of concept study implementing a novel method which can predict the evolution in time of the atheromatic plaque in carotids using only statistical features which are extracted from the arterial geometry. Four feature selection methods were compared: Quadratic Programming Feature Selection (QPFS), Minimal Redundancy Maximal Relevance (mRMR), Mutual Information Quotient (MIQ) and Spectral Conditional Mutual Information (SPECCMI). The classifier used is the Support Vector Machines (SVM) with linear and Gaussian kernels. The maximum accuracy that was achieved in predicting the variation in the mean value of the Lumen distance from the centerline and the thickness was 71.2% and 70.7% respectively.
authors.forename: Paschalis A
authors.initials: PA
authors.lastname: Bizopoulos
authors.forename: Antonis I
authors.initials: AI
authors.lastname: Sakellarios
authors.forename: Dimitrios D
authors.initials: DD
authors.lastname: Koutsouris
authors.forename: Jannis
authors.initials: J
authors.lastname: Kountouras
authors.forename: Lazaros
authors.initials: L
authors.lastname: Kostretzis
authors.forename: Stella
authors.initials: S
authors.lastname: Karagergou
authors.forename: Lampros K
authors.initials: LK
authors.lastname: Michalis
authors.forename: Dimitrios I
authors.initials: DI
authors.lastname: Fotiadis
mesh_terms: D000465:Algorithms; D002339:Carotid Arteries / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016011:Normal Distribution; D058226:Plaque, Atherosclerotic / Q000175:diagnosis* / Q000473:pathology*; D060388:Support Vector Machine