title: Role of fuzzy pre-classifier for high performance LI/MA segmentation in B-mode longitudinal carotid ultrasound images.
abstract: The automated segmentation of the carotid artery wall from ultrasound images is required for an accurate measurement of the artery intima-media thickness. Segmentation accuracy of automated techniques is usually limited by noise and artifacts. In 2005, the authors developed a methodology called CULEX whose performance was noise sensitive. The final stage of CULEX segmentation was fuzzy clustering of the pixels, to detect the lumen-intima (LI) and media-adventitia (MA) carotid wall interfaces. In this paper, we show the effect of a fuzzy Mamdani-type pre-classifier used to improve the segmentation performance. Thanks to the Mamdami fuzzy pre-classifier, we optimized the de-fuzzyfication threshold, increasing the LI and MA performance by 62% and 3.5%, respectively. The obtained segmentation errors (55.6 ± 69.4 microm for LI and 34.4 ± 24.4 microm for MA), validated against human tracings and on a 200-images dataset containing a mixture of healthy and plaque vessels.
authors.affiliation: BioLab, Department of Electronics, Politecnico di Torino, Italy. filippo.molinari@polito.it
authors.forename: Filippo
authors.initials: F
authors.lastname: Molinari
authors.forename: Laura
authors.initials: L
authors.lastname: Gaetano
authors.forename: Gabriella
authors.initials: G
authors.lastname: Balestra
authors.forename: Jasjit S
authors.initials: JS
authors.lastname: Suri
mesh_terms: D000465:Algorithms; D002339:Carotid Arteries / Q000000981:diagnostic imaging*; D002340:Carotid Artery Diseases / Q000000981:diagnostic imaging*; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods*