title: Automatic media-adventitia IVUS image segmentation based on sparse representation framework and dynamic directional active contour model.
abstract: Segmentation of the arterial wall boundaries from intravascular ultrasound images is an important image processing task in order to quantify arterial wall characteristics such as shape, area, thickness and eccentricity. Since manual segmentation of these boundaries is a laborious and time consuming procedure, many researchers attempted to develop (semi-) automatic segmentation techniques as a powerful tool for educational and clinical purposes in the past but as yet there is no any clinically approved method in the market. This paper presents a deterministic-statistical strategy for automatic media-adventitia border detection by a fourfold algorithm. First, a smoothed initial contour is extracted based on the classification in the sparse representation framework which is combined with the dynamic directional convolution vector field. Next, an active contour model is utilized for the propagation of the initial contour toward the interested borders. Finally, the extracted contour is refined in the leakage, side branch openings and calcification regions based on the image texture patterns. The performance of the proposed algorithm is evaluated by comparing the results to those manually traced borders by an expert on 312 different IVUS images obtained from four different patients. The statistical analysis of the results demonstrates the efficiency of the proposed method in the media-adventitia border detection with enough consistency in the leakage and calcification regions.
authors.affiliation: Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
authors.forename: Fahimeh Sadat
authors.initials: FS
authors.lastname: Zakeri
authors.affiliation: Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran. Electronic address: ksetareh@ut.ac.ir.
authors.forename: Seyed Kamaledin
authors.initials: SK
authors.lastname: Setarehdan
authors.affiliation: Clinical Research and Development Center, Qom University of Medical Sciences and Health Services, Qom, Iran.
authors.forename: Somayye
authors.initials: S
authors.lastname: Norouzi
mesh_terms: D063194:Adventitia / Q000000981:diagnostic imaging*; D000465:Algorithms; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008962:Models, Theoretical; D018084:Ultrasonography, Interventional; D061205:Vascular Calcification / Q000000981:diagnostic imaging*
keywords: Active contour model; Calcification; Dynamic directional convolution vector field; Intravascular ultrasound; Media-adventitia segmentation; Side branch regions; Sparse representation