Prostate segmentation in 3D US images using the cardinal-spline-based discrete dynamic contour

Mingyue Ding; Congjin Chen; Yunqiu Wang; Igor Gyacskov; Aaron Fenster

doi:10.1117/12.480370

30 May 2003 Prostate segmentation in 3D US images using the cardinal-spline-based discrete dynamic contour

Mingyue Ding, Congjin Chen, Yunqiu Wang, Igor Gyacskov, Aaron Fenster

Proceedings Volume 5029, Medical Imaging 2003: Visualization, Image-Guided Procedures, and Display; (2003) https://doi.org/10.1117/12.480370
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

Our slice-based 3D prostate segmentation method comprises of three steps. 2) Boundary deformation. First, we chose more than three points on the boundary of the prostate along one direction and used a Cardinal-spline to interpolate an initial prostate boundary, which has been divided into vertices. At each vertex, the internal and external forces were calculated. These forces drived the evolving contour to the true boundary of the prostate. 3) 3D prostate segmentation. We propoaged the final contour in the initial slice to adjacent slices and refined them until all prostate boundaries of slices are segmented. Finally, we calculated the volume of the prostate from a 3D mesh surface of the prostate. Experiments with the 3D US images of six patient prostates demonstrated that our method efficiently avoided being trapped in local minima and the average percentage error was 4.8%. In 3D prostate segementation, the average percentage error in measuring the prostate volume is less than 5%, with respect to the manual planimetry.

Citation Download Citation

Mingyue Ding, Congjin Chen, Yunqiu Wang, Igor Gyacskov, and Aaron Fenster "Prostate segmentation in 3D US images using the cardinal-spline-based discrete dynamic contour", Proc. SPIE 5029, Medical Imaging 2003: Visualization, Image-Guided Procedures, and Display, (30 May 2003); https://doi.org/10.1117/12.480370

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