Bayesian Technique for Image Classifying Registration
Bayesian Technique for Image Classifying Registration
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DOI：
10.1109/tip.2012.2200495
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发表时间：
2012-09
影响因子：
10.6
通讯作者：
M. Hachama;A. Desolneux;F. Richard
中科院分区：
文献类型：
--
作者：
M. Hachama;A. Desolneux;F. Richard
在本文中，我们解决了一个复杂的图像配准问题，该问题出现在待配准图像的强度之间的相关性在空间上不均匀时。在医学成像中，当其中一幅图像中存在的病理改变了在正常组织上观察到的局部强度相关性时，经常会遇到这种情况。通常的基于单一全局强度相似性准则的图像配准模型无法配准此类图像，因为它们对强度相关性的局部偏差视而不见。在对比增强图像中也会遇到这种限制，其中存在具有不同造影剂吸收特性的多个像素类别。在本文中，我们提出了一种新模型，其中通过对图像强度相关性进行分类，使相似性准则在局部适应图像。在贝叶斯框架下定义，相似性准则是描述两类相关性的概率分布的混合。该模型还包括一个类别图，它定位两类像素并对两个混合成分进行加权。配准问题既被表述为一个能量最小化问题，也被表述为一个最大后验估计问题。它使用梯度下降算法来求解。在问题的表述和求解中，图像变形和类别图同时被估计，导致了一种我们称之为图像分类配准的配准和分类的原始组合。在应用中，只要有关于类别位置的足够信息可用，也可以通过固定给定的类别图来单独进行配准。最后，我们通过医学成像中的两个实际应用来说明我们模型的优势：对比增强图像的基于模板的分割和乳腺X光片中的病变检测。我们还在模拟医学数据上对我们的模型进行了评估，并展示了它在考虑强度相关性的空间变化的同时保持良好配准精度的能力。
In this paper, we address a complex image registration issue arising while the dependencies between intensities of images to be registered are not spatially homogeneous. Such a situation is frequently encountered in medical imaging when a pathology present in one of the images modifies locally intensity dependencies observed on normal tissues. Usual image registration models, which are based on a single global intensity similarity criterion, fail to register such images, as they are blind to local deviations of intensity dependencies. Such a limitation is also encountered in contrast-enhanced images where there exist multiple pixel classes having different properties of contrast agent absorption. In this paper, we propose a new model in which the similarity criterion is adapted locally to images by classification of image intensity dependencies. Defined in a Bayesian framework, the similarity criterion is a mixture of probability distributions describing dependencies on two classes. The model also includes a class map which locates pixels of the two classes and weighs the two mixture components. The registration problem is formulated both as an energy minimization problem and as a maximum a posteriori estimation problem. It is solved using a gradient descent algorithm. In the problem formulation and resolution, the image deformation and the class map are estimated simultaneously, leading to an original combination of registration and classification that we call image classifying registration. Whenever sufficient information about class location is available in applications, the registration can also be performed on its own by fixing a given class map. Finally, we illustrate the interest of our model on two real applications from medical imaging: template-based segmentation of contrast-enhanced images and lesion detection in mammograms. We also conduct an evaluation of our model on simulated medical data and show its ability to take into account spatial variations of intensity dependencies while keeping a good registration accuracy.