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Yunqiu Wang

Publications and source records attributed to Yunqiu Wang.

2 recordsLinked to original sources

Semiautomatic three-dimensional segmentation of the prostate using two-dimensional ultrasound images.

In this paper, we report on two methods for semiautomatic three-dimensional (3-D) prostate boundary segmentation using 2-D ultrasound images. For each method, a 3-D ultrasound prostate image was sliced into the series of contiguous 2-D images, either in a parallel manner, with a uniform slice spacing of 1 mm, or in a rotational manner, about an axis approximately through the center of the prostate, with a uniform angular spacing of 5 degrees. The segmentation process was initiated by manually placing four points on the boundary of a selected slice, from which an initial prostate boundary was determined. This initial boundary was refined using the Discrete Dynamic Contour until it fit the actual prostate boundary. The remaining slices were then segmented by iteratively propagating this result to an adjacent slice and repeating the refinement, pausing the process when necessary to manually edit the boundary. The two methods were tested with six 3-D prostate images. The results showed that the parallel and rotational methods had mean editing rates of 20% and 14%, and mean (mean absolute) volume errors of -5.4% (6.5%) and -1.7% (3.1%), respectively. Based on these results, as well as the relative difficulty in editing, we conclude that the rotational segmentation method is superior.

Algorithms↗

Testing and optimization of a semiautomatic prostate boundary segmentation algorithm using virtual operators.

Image analysis tasks such as size measurement and landmark-based registration require the user to select control points in an image. The output of such algorithms depends on the choice of control points. Since the choice of points varies from one user to the next, the requirement for user input introduces variability into the output of the algorithm. In order to test and/or optimize such algorithms, it is necessary to assess the multiplicity of outputs generated by the algorithm in response to a large set of inputs; however, the input of data requires substantial time and effort from multiple users. In this paper we describe a method to automate the testing and optimization of algorithms using "virtual operators," which consist of a set of spatial distributions describing how actual users select control points in an image. In order to construct the virtual operator, multiple users must repeatedly select control points in the image on which testing is to be performed. Once virtual operators are generated, control points for initializing the algorithm can be generated from them using a random number generator. Although an initial investment of time is required from the users in order to construct the virtual operator, testing and optimization of the algorithm can be done without further user interaction. We illustrate the construction and use of virtual operators by testing and optimizing our prostate boundary segmentation algorithm. The algorithm requires the user to select four control points on the prostate as input.

Algorithms↗