Three-Dimensional Carotid Ultrasound Segmentation Variability Dependence on Signal Difference and Boundary Orientation

Chiu, Bernard; Krasinski, Adam; Spence, J. David; Parraga, Grace; Fenster, Aaron

doi:10.1016/j.ultrasmedbio.2009.08.005

« Previous Next »Ultrasound in Medicine and Biology
Volume 36, Issue 1 , Pages 95-110, January 2010

Three-Dimensional Carotid Ultrasound Segmentation Variability Dependence on Signal Difference and Boundary Orientation

Bernard Chiu
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
- Graduate Program in Biomedical Engineering, University of Western Ontario, London, Ontario, Canada
- Address correspondence to: Bernard Chiu, Robarts Research Institute, Imaging Research Laboratories, P.O. Box 5015, 100 Perth Dr., London, Canada, N6A 5K8.
Adam Krasinski
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada
J. David Spence
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
- Stroke Prevention and Atherosclerosis Research Centre, Robarts Research Institute, London, Ontario, Canada
Grace Parraga
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
- Graduate Program in Biomedical Engineering, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada
Aaron Fenster
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
- Graduate Program in Biomedical Engineering, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada

Received 30 July 2008; received in revised form 8 July 2009; accepted 5 August 2009. published online 09 November 2009.

Abstract

Quantitative measurements of the progression (or regression) of carotid plaque burden are important in monitoring patients and evaluating new treatment options. We previously developed a quantitative metric to analyze changes in carotid plaque morphology from 3-D ultrasound (US) on a point-by-point basis. This method requires multiple segmentations of the arterial wall and lumen boundaries to obtain the local standard deviation (SD) of vessel-wall-plus-plaque thickness (VWT) so that t-tests could be used to determine whether a change in VWT is statistically significant. However, the requirement for multiple segmentations makes clinical trials laborious and time-consuming. Therefore, this study was designed to establish the relationship between local segmentation SD and local signal difference on the arterial wall and lumen boundaries. We propose metrics to quantify segmentation SD and signal difference on a point-by-point basis, and studied whether the signal difference at arterial wall or lumen boundaries could be used to predict local segmentation SD. The ability to predict the local segmentation SD could eliminate the need of repeated segmentations of a 2-D transverse image to obtain the local segmentation standard deviation, thereby making clinical trials less laborious and saving time. Six subjects involved in this study were associated with different degrees of atherosclerosis: three carotid stenosis subjects with mean plaque area >3 cm² and >60% carotid stenosis were involved in a clinical study evaluating the effect of atorvastatin, a cholesterol-lowering and plaque-stabilizing drug; and three subjects with carotid plaque area >0.5 cm² were subjects with moderate atherosclerosis. Our results suggest that when local signal difference is higher than 8 greyscale value (GSV), the local segmentation SD stabilizes at 0.05 mm and is thus predictable. This information provides a target value of local signal difference on the arterial boundaries that should be achieved to obtain an accurate prediction of local segmentation SD. (E-mail: bcychiu@alumni.uwo.ca)

Key Words: 3-D Carotid ultrasound image, Gabor filter-based edge detector, Local segmentation standard deviation, Signal difference, Boundary orientation