A minimum description length approach to statistical shape modeling
A minimum description length approach to statistical shape modeling
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DOI：
10.1109/tmi.2002.1009388
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发表时间：
2002-05-01
影响因子：
10.6
通讯作者：
Taylor, CJ
中科院分区：
文献类型：
--
作者：
Davies, RH;Twining, CJ;Taylor, CJ
我们描述了一种从示例边界/表面的训练集中自动构建统计形状模型的方法。这些模型作为分割和解释图像的基础显示出巨大的前景。然而，该方法的一个缺点是需要在一组训练形状的所有成员之间建立一组密集的对应关系。通常，这是通过在每个训练图像上手动定位一组“地标”来实现的，这在二维中是耗时且主观的，而在三维中几乎是不可能的。我们描述了如何通过将对应问题作为寻找训练集中每个形状的参数化问题来自动构建形状模型。我们选择构建“最佳”模型的参数化集。我们将“最佳”定义为最小化训练集的描述长度，认为这会导致模型具有良好的紧凑性、特异性和泛化能力。我们展示了如何表示和操作一组形状参数化以构建最小描述长度模型。给出了几个不同的二维边界训练集的结果，表明所提出的方法构建了比其他方法更好的模型，包括手动标记（当前的黄金标准）。我们还表明该方法可以直接扩展到三个维度。
We describe a method for automatically building statistical shape models from a training set of example boundaries/surfaces. These models show considerable promise as a basis for segmenting and interpreting images. One of the drawbacks of the approach is, however, the need to establish a set of dense correspondences between all members of a set of training shapes. Often this is achieved by locating a set of "landmarks" manually on each training image, which is time consuming and subjective in two dimensions and almost impossible in three dimensions. We describe how shape models can be built automatically by posing the correspondence problem as one of finding the parameterization for each shape in the training set. We select the set of parameterizations that build the "best" model. We define "best" as that which minimizes the description length of the training set, arguing that this leads to models with good compactness, specificity and generalization ability. We show how a set of shape parameterizations can be represented and manipulated in order to build a minimum description length model. Results are given for several different training sets of two-dimensional boundaries, showing that the proposed method constructs better models than other approaches including manual landmarking-the current gold standard. We also show that the method can be extended straightforwardly to three dimensions.