IEEE Trans Image Process - Bayesian technique for image classifying registration.

Tópicos

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Resumo

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.

Resumo Limpo

paper address complex imag registr issu aris depend intens imag regist spatial homogen situat frequent encount medic imag patholog present one imag modifi local intens depend observ normal tissu usual imag registr model base singl global intens similar criterion fail regist imag blind local deviat intens depend limit also encount contrastenhanc imag exist multipl pixel class differ properti contrast agent absorpt paper propos new model similar criterion adapt local imag classif imag intens depend defin bayesian framework similar criterion mixtur probabl distribut describ depend two class model also includ class map locat pixel two class weigh two mixtur compon registr problem formul energi minim problem maximum posteriori estim problem solv use gradient descent algorithm problem formul resolut imag deform class map estim simultan lead origin combin registr classif call imag classifi registr whenev suffici inform class locat avail applic registr can also perform fix given class map final illustr interest model two real applic medic imag templatebas segment contrastenhanc imag lesion detect mammogram also conduct evalu model simul medic data show abil take account spatial variat intens depend keep good registr accuraci

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