Bayesian technique for image classifying registration.

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.

A classifying registration technique for the estimation of enhancement curves of DCE-CT scan sequences.

In this paper, we propose a new technique for the estimation of contrast enhancement curves of Dynamic Contrast-Enhanced sequences, which takes the most from the interdependence between this estimation problem and the registration problem raised by possible movements occurring in sequences. The technique solves the estimation and registration problems simultaneously in an iterative way. However, unlike previous techniques, a pixel classification scheme is included within the estimation so as to compute enhancement curves on pixel classes instead of single pixels. The classification scheme is designed using a descendant hierarchical approach. Due to this tree approach, the number of classes is set automatically and the whole technique is entirely unsupervised. Moreover, some specific prior information about the shape of enhancement curves are included in the splitting and pruning steps of the classification scheme. Such an information ensures that created classes include pixels having homogeneous and relevant enhancement properties. The technique is applied to DET-CT scan sequences and evaluated using ground truth data. Results show that classifications are anatomically sound and that contrast enhancements are accurately estimated from sequences.