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An efficient 3D deformable model with a self-optimising mesh

机译:具有自优化网格的高效3D变形模型

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摘要

Deformable models are a powerful and popular tool for image segmentation, but in 3D imaging applications the high computational cost of fitting such models can be a problem. A further drawback is the need to select the initial size and position of a model in such a way that it is close to the desired solution. This task may require particular expertise on the part of the operator, and, furthermore, may be difficult to accomplish in three dimensions without the use of sophisticated visualisation techniques. This article describes a 3D deformable model that uses an adaptive mesh to increase computational efficiency and accuracy. The model employs a distance transform in order to overcome some of the problems caused by inaccurate initialisation. The performance of the model is illustrated by its application to the task of segmentation of 3D MR images of the human head and hand. A quantitative analysis of the performance is also provided using a synthetic test image.
机译:变形模型是用于图像分割的强大且流行的工具,但是在3D成像应用中,拟合此类模型的高计算成本可能是个问题。另一个缺点是需要选择模型的初始大小和位置,使其接近所需的解决方案。该任务可能需要操作员方面的专门知识,而且,如果不使用复杂的可视化技术,可能很难在三个维度上完成。本文介绍了一种3D变形模型,该模型使用自适应网格来提高计算效率和准确性。该模型采用距离变换,以克服初始化不准确引起的一些问题。该模型的性能通过将其应用于人头和手的3D MR图像分割任务来说明。还使用合成测试图像对性能进行了定量分析。

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