Twining Carole, Davies Rhodri, Taylor Chris
Imaging Sciences, University of Manchester, Manchester, UK.
Inf Process Med Imaging. 2007;20:738-50. doi: 10.1007/978-3-540-73273-0_61.
Determining groupwise correspondence across a set of unlabelled examples of either shapes or images, by the use of an optimisation procedure, is a well-established technique that has been shown to produce quantitatively better models than other approaches. However, the computational cost of the optimisation is high, leading to long convergence times. In this paper, we show how topologically non-trivial shapes can be mapped to regular grids (called shape images). This leads to an initial reduction in computational complexity. By also considering the question of regularisation, we show that a non-parametric fluid regulariser can be applied in a principled manner, the fluid flowing on the shape surface itself, whilst not loosing the computational gain made by the use of shape images. We show that this non-parametric regularisation leads to a further considerable gain, when compared to previous parametric regularisation methods. Quantitative evaluation is performed on biological datasets, and shown to yield a substantial decrease in convergence time, with no loss of model quality.
通过使用优化程序来确定一组未标记的形状或图像示例之间的逐组对应关系，是一种成熟的技术，已证明该技术能比其他方法产生质量更好的模型。然而，优化的计算成本很高，导致收敛时间很长。在本文中，我们展示了如何将拓扑非平凡形状映射到规则网格（称为形状图像）。这导致计算复杂度初步降低。通过考虑正则化问题，我们表明可以以有原则的方式应用非参数流体正则化器，流体在形状表面本身流动，同时不会损失使用形状图像带来的计算优势。我们表明，与以前的参数正则化方法相比，这种非参数正则化会带来进一步的显著优势。对生物数据集进行了定量评估，结果表明收敛时间大幅减少，且模型质量没有损失。
