Study Method Dataset & Size Measures Findings
Zhou et al. [108]
Modified DP
N=200 (32) videos Overall error, Total time, Bland-Altman plots
Accuracy improved when the algorithm was refined using snake.
Faita et al. [109]
Gradient-based edge detection
N=150
Bland-Altman plots
variation in mean bias ± SD of 0.001 ± 0.035. High accuracy and real-time application
Rossi et al. [110]
Adventitial delineation using sustain attack filter, intimal delineation using MAB.
N=36 (12)
Bland-Altman plots, Measurement error
Intra-observer variability = 0, suitable for clinical trials, comparison with synthetic ultrasound images. Radiofrequency envelopes analysed.
Golemati et al. [112]
Hough transform, Canny edge detection. Both B-mode and M-mode ultrasound
N=5
Radial displacement of ROI
Accurate for non-stenotic. Atherosclerotic plaque affected the result.
Loizou et al. [113]
Snake algorithm Speckle reduction
N=100
Bland-Altman plots
intra-observer error = 0.08 Hausdorff distance = 5.2
Petroudi et al. [90]
Active contour, Speckle removal
N=100
Mean absolute distance. Polyline distance. Hausdorff distance.
Mean absolute distance error = 0.095±0.0615 mm, Polyline distance = 0.096±0.034 mm. Hausdorff distance = 0.176±0.047 mm.
Santhiyakumari et al. [114]
Active contour segmentation. Semi-automatic ROI identification
N=100 63 normal
Coefficient of variation, Pearson’s CC, Wilcoxon metric
Inter-method error = 0.09 mm CV = 18.9%
Destrempes et al. [115]
Expectation maximisation algorithm, Nakagami distribution
N=30
Mean distance, Hausdorff distance Error in: LI = 0.46 mm MA = 0.41 mm
Molinari et al. [117]
Local statistics. Integrated approach (Greedy algorithm).
N=200
Polyline distance, Mean system error Error in: LI = 26.3±55.6 µm MA = 16.2±31.3 µm IMT = 83.1±61.8 µm
Ilea et al. [119]
Unsupervised IMC video segmentation
40 and 772 frames
coefficient of variation, Bland-Altman plot
Auto tracking of IMT variations in a cardiac cycle.