title: Feature selection applied to ultrasound carotid images segmentation.
abstract: The automated tracing of the carotid layers on ultrasound images is complicated by noise, different morphology and pathology of the carotid artery. In this study we benchmarked four methods for feature selection on a set of variables extracted from ultrasound carotid images. The main goal was to select those parameters containing the highest amount of information useful to classify the pixels in the carotid regions they belong to. Six different classes of pixels were identified: lumen, lumen-intima interface, intima-media complex, media-adventitia interface, adventitia and adventitia far boundary. The performances of QuickReduct Algorithm (QRA), Entropy-Based Algorithm (EBR), Improved QuickReduct Algorithm (IQRA) and Genetic Algorithm (GA) were compared using Artificial Neural Networks (ANNs). All methods returned subsets with a high dependency degree, even if the average classification accuracy was about 50%. Among all classes, the best results were obtained for the lumen. Overall, the four methods for feature selection assessed in this study return comparable results. Despite the need for accuracy improvement, this study could be useful to build a pre-classifier stage for the optimization of segmentation performance in ultrasound automated carotid segmentation.
authors.affiliation: BioLab, Department of Electronics, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.
authors.forename: Samanta
authors.initials: S
authors.lastname: Rosati
authors.forename: Filippo
authors.initials: F
authors.lastname: Molinari
authors.forename: Gabriella
authors.initials: G
authors.lastname: Balestra
mesh_terms: D000368:Aged; D000465:Algorithms; D002339:Carotid Arteries / Q000000981:diagnostic imaging*; D002340:Carotid Artery Diseases / Q000000981:diagnostic imaging*; D059168:Carotid Intima-Media Thickness; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity