title: Evaluation of an algorithm for semiautomated segmentation of thin tissue layers in high-frequency ultrasound images.
abstract: An algorithm consisting of speckle reduction by median filtering, contrast enhancement using top- and bottom-hat morphological filters, and segmentation with a discrete dynamic contour (DDC) model was implemented for nondestructive measurements of soft tissue layer thickness. Algorithm performance was evaluated by segmenting simulated images of three-layer phantoms and high-frequency (40 MHz) ultrasound images of porcine aortic valve cusps in vitro. The simulations demonstrated the necessity of the median and morphological filtering steps and enabled testing of user-specified parameters of the morphological filters and DDC model. In the experiments, six cusps were imaged in coronary perfusion solution (CPS) then in distilled water to test the algorithm's sensitivity to changes in the dimensions of thin tissue layers. Significant increases in the thickness of the fibrosa, spongiosa, and ventricularis layers, by 53.5% (p < 0.001), 88.5% (p < 0.001), and 35.1% (p = 0.033), respectively, were observed when the specimens were submerged in water. The intraobserver coefficient of variation of repeated thickness estimates ranged from 0.044 for the fibrosa in water to 0.164 for the spongiosa in CPS. Segmentation accuracy and variability depended on the thickness and contrast of the layers, but the modest variability provides confidence in the thickness measurements.
authors.affiliation: University of Western Ontario, London, Ontario, N6A 5B9, Canada.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Qiu
authors.forename: Joy
authors.initials: J
authors.lastname: Dunmore-Buyze
authors.forename: Derek R
authors.initials: DR
authors.lastname: Boughner
authors.forename: James C
authors.initials: JC
authors.lastname: Lacefield
mesh_terms: D000465:Algorithms; D000818:Animals; D001021:Aortic Valve / Q000000981:diagnostic imaging*; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D066298:In Vitro Techniques; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013552:Swine; D014463:Ultrasonography