title: Completely automated multiresolution edge snapper--a new technique for an accurate carotid ultrasound IMT measurement: clinical validation and benchmarking on a multi-institutional database.
abstract: The aim of this paper is to describe a novel and completely automated technique for carotid artery (CA) recognition, far (distal) wall segmentation, and intima-media thickness (IMT) measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale-space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen-intima (LI) and media-adventitia (MA) interfaces for the far (distal) wall and IMT measurement. Our database of 365 B-mode longitudinal carotid images is taken from four different institutions covering different ethnic backgrounds. The ground-truth (GT) database was the average manual segmentation from three clinical experts. The mean distance  ± standard deviation of CAMES with respect to GT profiles for LI and MA interfaces were 0.081  ± 0.099 and 0.082  ± 0.197 mm, respectively. The IMT measurement error between CAMES and GT was 0.078   ± 0.112 mm. CAMES was benchmarked against a previously developed automated technique based on an integrated approach using feature-based extraction and classifier (CALEX). Although CAMES underestimated the IMT value, it had shown a strong improvement in segmentation errors against CALEX for LI and MA interfaces by 8% and 42%, respectively. The overall IMT measurement bias for CAMES improved by 36% against CALEX. Finally, this paper demonstrated that the figure-of-merit of CAMES was 95.8% compared with 87.4% for CALEX. The combination of multiresolution CA recognition and far-wall segmentation led to an automated, low-complexity, real-time, and accurate technique for carotid IMT measurement. Validation on a multiethnic/multi-institutional data set demonstrated the robustness of the technique, which can constitute a clinically valid IMT measurement for assistance in atherosclerosis disease management.
authors.affiliation: Department of Electronics, Politecnico di Torino, Torino, Italy. filippo.molinari@polito.it
authors.forename: Filippo
authors.initials: F
authors.lastname: Molinari
authors.forename: Constantinos S
authors.initials: CS
authors.lastname: Pattichis
authors.forename: Guang
authors.initials: G
authors.lastname: Zeng
authors.forename: Luca
authors.initials: L
authors.lastname: Saba
authors.forename: U Rajendra
authors.initials: UR
authors.lastname: Acharya
authors.forename: Roberto
authors.initials: R
authors.lastname: Sanfilippo
authors.forename: Andrew
authors.initials: A
authors.lastname: Nicolaides
authors.forename: Jasjit S
authors.initials: JS
authors.lastname: Suri
mesh_terms: D000465:Algorithms; D002339:Carotid Arteries / Q000000981:diagnostic imaging*; D016208:Databases, Factual; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D017539:Tunica Intima / Q000000981:diagnostic imaging; D017540:Tunica Media / Q000000981:diagnostic imaging; D014463:Ultrasonography