Frédéric J. P. Richard

A new image registration technique with free boundary constraints: application to mammography

In this paper, a new image-matching mathematical model is presented with its application to mammogram registration. In a variatianal framework, an energy minimization problem is formulated and a multigrid resolution algorithm is designed. The model focuses on the matching of regions of interest. It also combines several constraints which are both intensity- and segmentation-based. A new feature of our model is combining region matching and segmentation by formulation of the energy minimization problem with free boundary conditions. Moreover, the energy has a new registration constraint. The performances of the new model and an equivalent model with fixed boundary conditions are compared on simulated mammogram pairs. It is shown that the model with free boundary is more robust to initialization inaccuracies than the one with fixed boundary conditions. Both models are applied to several real bilateral mammogram pairs. The model ability to compensate significantly for some normal differences between mammograms is illustrated. Results suggest that the new model could enable some improvements of mammogram comparisons and tumor detection system performances.

Construction and analysis of a head CT-scan database for craniofacial reconstruction

This paper is devoted to the construction of a complete database which is intended to improve the implementation and the evaluation of automated facial reconstruction. This growing database is currently composed of 85 head CT-scans of healthy European subjects aged 20-65 years old. It also includes the triangulated surfaces of the face and the skull of each subject. These surfaces are extracted from CT-scans using an original combination of image-processing techniques which are presented in the paper. Besides, a set of 39 referenced anatomical skull landmarks were located manually on each scan. Using the geometrical information provided by triangulated surfaces, we compute facial soft-tissue depths at each known landmark positions. We report the average thickness values at each landmark and compare our measures to those of the traditional charts of [J. Rhine, C.E. Moore, Facial Tissue Thickness of American Caucasoïds, Maxwell Museum of Anthropology, Albuquerque, New Mexico, 1982] and of several recent in vivo studies [M.H. Manhein, G.A. Listi, R.E. Barsley, et al., In vivo facial tissue depth measurements for children and adults, Journal of Forensic Sciences 45 (1) (2000) 48-60; S. De Greef, P. Claes, D. Vandermeulen, et al., Large-scale in vivo Caucasian facial soft tissue thickness database for craniofacial reconstruction, Forensic Science International 159S (2006) S126-S146; R. Helmer, Schödelidentifizierung durch elektronische bildmischung, Kriminalistik Verlag GmbH, Heidelberg, 1984].

Mammogram registration: a phantom-based evaluation of compressed Breast Thickness variation effects

The temporal comparison of mammograms is complex; a wide variety of factors can cause changes in image appearance. Mammogram registration is proposed as a method to reduce the effects of these changes and potentially to emphasize genuine alterations in breast tissue. Evaluation of such registration techniques is difficult since ground truth regarding breast deformations is not available in clinical mammograms. In this paper, we propose a systematic approach to evaluate sensitivity of registration methods to various types of changes in mammograms using synthetic breast images with known deformations. As a first step, images of the same simulated breasts with various amounts of simulated physical compression have been used to evaluate a previously described nonrigid mammogram registration technique. Registration performance is measured by calculating the average displacement error over a set of evaluation points identified in mammogram pairs. Applying appropriate thickness compensation and using a preferred order of the registered images, we obtained an average displacement error of 1.6 mm for mammograms with compression differences of 1-3 cm. The proposed methodology is applicable to analysis of other sources of mammogram differences and can be extended to the registration of multimodality breast data.

Statistical Tests of Anisotropy for Fractional Brownian Textures. Application to Full-field Digital Mammography

In this paper, we propose a new and generic methodology for the analysis of texture anisotropy. The methodology is based on the stochastic modeling of textures by anisotropic fractional Brownian fields. It includes original statistical tests that permit to determine whether a texture is anisotropic or not. These tests are based on the estimation of directional parameters of the fields by generalized quadratic variations. Their construction is founded on a new theoretical result about the convergence of test statistics, which is proved in the paper. The methodology is applied to simulated data and discussed. We show that on a database composed of 116 full-field digital mammograms, about 60 percent of textures can be considered as anisotropic with a high level of confidence. These empirical results strongly suggest that anisotropic fractional Brownian fields are better-suited than the commonly used fractional Brownian fields to the modeling of mammogram textures.

A New Image Registration Technique with Free Boundary Constraints: Application to Mammography

In this paper, a new image-matching mathematical model is presented for the mammogram registration. In a variational framework, an energy minimization problem is formulated and a multigrid resolution algorithm is designed. The model focuses on the matching of regions of interest. It also combines several constraints which are both intensity and segmentation based. A new feature of our model is combining region matching and segmentation by formulation of the energy minimization problem with free boundary conditions. Moreover, the energy has a new registration constraint. The performances of models with and without free boundary are compared on a simulated mammogram pair. It is shown that the new model with free boundary is more robust to initialization inaccuracies than the one without. The interest of the new model for the real mammogram registration is also illustrated.

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 SAEM algorithm for the estimation of template and deformation parameters in medical image sequences

This paper is about object deformations observed throughout a sequence of images. We present a statistical framework in which the observed images are defined as noisy realizations of a randomly deformed template image. In this framework, we focus on the problem of the estimation of parameters related to the template and deformations. Our main motivation is the construction of estimation framework and algorithm which can be applied to short sequences of complex and highly-dimensional images. The originality of our approach lies in the representations of the template and deformations, which are defined on a common triangulated domain, adapted to the geometry of the observed images. In this way, we have joint representations of the template and deformations which are compact and parsimonious. Using such representations, we are able to drastically reduce the number of parameters in the model. Besides, we adapt to our framework the Stochastic Approximation EM algorithm combined with a Markov Chain Monte Carlo procedure which was proposed in 2004 by Kuhn and Lavielle. Our implementation of this algorithm takes advantage of some properties which are specific to our framework. More precisely, we use the Markovian properties of deformations to build an efficient simulation strategy based on a Metropolis-Hasting-Within-Gibbs sampler. Finally, we present some experiments on sequences of medical images and synthetic data.

A local technique based on vectorized surfaces for craniofacial reconstruction

In this paper, we focus on the automation of facial reconstruction. Since they consider the whole head as the object of interest, usual reconstruction techniques are global and involve a large number of parameters to be estimated. We present a local technique which aims at reaching a good trade-off between bias and variance following the paradigm of non-parametric statistics. The estimation is localized on patches delimited by surface geodesics between anatomical points of the skull. The technique relies on a continuous representation of the individual surfaces embedded in the vectorial space of extended normal vector fields. This allows to compute deformations and averages of surfaces. It consists in estimating the soft-tissue surface over patches. Using a homogeneous database described in [31], we obtain results on the chin and nasal regions with an average error below 1mm, outperforming the global reconstruction techniques.

A new approach for the registration of images with inconsistent differences

We focus on the image registration problem. Mathematically, this problem consists of minimizing an energy which is composed of a regularization term and a similarity term. The similarity term, which depends on image intensities, has to be chosen according to the nature of image grey-level dependencies. Its adequacy always depends on the validity of some assumptions about these dependencies. But, in medical applications, there are many situations where these assumptions are not confirmed. In particular, intensity variations caused by observed pathologies may not be consistent with assumptions. Such variations may distort the registration constraints and cause registration errors. In order to cope with this problem, we propose a new approach which takes into account the possible inconsistencies in the computation of the registration constraints. This approach is described in two different points of view. First, we formulate a new minimization problem with an extra unknown which measures the degree of inconsistency on each pixel. Then, we show that this problem is equivalent to another one which can be related to the usual ones. We also outline several ways to generalize our approach and propose an algorithm to numerically solve these problems. Finally, we illustrate on synthetic data some characteristics of the algorithm when dealing with inconsistent image differences.

A comparative study of markovian and variational image-matching techniques in application to mammograms

In this paper, we focus our interest on the image-matching problem. This major problem in Image Processing has received a considerable attention in the last decade. However, contrarily to other image-processing problems such as image restoration, the image-matching problem have been mainly tackled using a single approach based on variational principles. In this paper, our motivation is to investigate the feasibility of another famous image-processing approach based on Markov random fields (MRF). For that, we propose a discrete and stochastic image-matching framework which is equivalent to an usual variational one and suitable for an MRF-based approach. In this framework, we describe multigrid implementations of two algorithms: an iterated conditional modes (ICM) and a simulated annealing. We apply these algorithms for the registration of mammograms and compare their performances to those of an usual variational algorithm. We come to the conclusion that MRF-based techniques are optimization techniques which are relevant for the mammogram application. We also point out some of their specific properties and mention interesting perspectives offered by the markovian approach.