title: Inter-Greedy technique for fusion of different carotid segmentation boundaries leading to high-performance IMT measurement.
abstract: User-based estimation of intima-media thickness (IMT) of carotid arteries leads to subjectivity in its decision support systems, while being used as a cardiovascular risk marker. During automated computer-based decision support, we had developed segmentation strategies that follow three main courses of our contributions: (a) signal processing approach combined with snakes and fuzzy K-means (CULEXsa), (b) integrated approach based on seed and line detection followed by probability based connectivity and classification (CALEXsa), and (c) morphological approach with watershed transform and fitting (WS). We have extended this fusion concept by taking merits of these multiple boundaries, so called, Inter-Greedy (IG) approach. Starting from the technique with the overall least system error (the snake-based one), we iteratively swapped the vertices of the lumen-intima/media-adventitia (LI/MA) profiles until we minimized its overall distance with respect to ground truth. The fusion boundary was the IG boundary. The mean error of Inter-Greedy technique (evaluated on 200 images) yielded 0.32 ± 0.44 pixel (20.0 ± 27.5 microm) for the LI boundary (a 33.3% ± 5.6% improvement over initial best performing technique) and 0.21 ± 0.34 pixel (13.1 ± 21.3 microm) for MA boundary (a 32.3% ± 6.7% improvement). IMT measurement error for Greedy method was 0.74 ± 0.75 pixel (46.3 ± 46.9 microm), a 43.5% ± 2.4% improvement.
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: Guang
authors.initials: G
authors.lastname: Zeng
authors.forename: Jasjit S
authors.initials: JS
authors.lastname: Suri
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002339:Carotid Arteries / Q000000981:diagnostic imaging*; 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; D013382:Subtraction Technique; D017539:Tunica Intima / Q000000981:diagnostic imaging*; D017540:Tunica Media / Q000000981:diagnostic imaging*; D014463:Ultrasonography / Q000379:methods*