Marie-Josèphe Deshayes

Shape variability and spatial relationships modeling in statistical pattern recognition

We focus on the problem of shape variability modeling in statistical pattern recognition. We present a nonlinear statistical model invariant to affine transformations. This model is learned on an ordinate set of points. The concept of relations between model components is also taken in account. This model is used to find curves and points partially occulted in the image. We present its application on medical imaging in cephalometry.

Linear and non-linear model for statistical localization of landmarks

This paper presents and compares 3 methods for the statistical localization of partially occulted landmarks. In many real applications, some information is visible in images and some parts are missing or occulted. These parts are estimated by 3 statistical approaches: a rigid registration, a linear method derived from PCA, which represents spatial relationships, and a nonlinear model based upon kernel PCA. Applied to the cephalometric problem, the best method exhibits a mean error of 3.3 mm, which is about 3 times the intra-expert variability.

Computer assisted landmarking of cephalometric radiographs

We address the problem of finding an initial estimation of the location of landmarks on an image, when the landmarks are difficult to distinguish on the image and when the locations are dependent together from external forces such as growth. Our method solves the problem using an adaptive coordinate space where locations are registered. In this space, variability is greatly reduced. A training set is observed to build automatically a mean and a variability model of the landmarks. This model is used to predict the initial estimation on a new image. This method is applied to the difficult problem of the interpretation of cephalograms, with good results.

Contour tracking by minimal cost path approach: application to cephalometry

In this paper, a minimal cost approach is used for contour tracking with a good robustness. Dynamic programming was chosen for its efficiency. This general method is applied to the extraction of the cranial contour on high-resolution X-Ray images. As a first step for automated localization of cephalometric points, an ellipse is then fitted on the extracted contour. This method was tested on 424 X-Ray images, with different acquisition parameters.

First steps toward automatic location of landmarks on X-ray images

We address the problem of locating some anatomical bone structures on lateral cranial X-ray images. These structures are landmarks used in orthodontic therapy. The main problem in this pattern recognition application is that the landmarks are difficult to distinguish on images even for the human expert, because of lateral projection of the X-ray process. We propose a 3 steps approach: the first step provides a statistical estimation of the landmarks, using an adaptive coordinates space; the second step computes a region of interest around the estimated landmark; and in the third step, each landmark is precisely located using its anatomical definition. This paper describes the two first generic steps and gives examples of the last step for two anatomical points.

Augmented Reality and Semi-automated Landmarking of Cephalometric Radiographs

In this paper, we propose computer assisted visualization for manual landmarking of specific points on cephalometric radiographs. The signal to noise ratio of radiographs is very low, because of superimposing of anatomical structures, dissymetries or artefacts. On radiographs of children, the localization of cephalometric points presents a great inter-subject, and inter- and intra-expert varibility, which is considerably reduced by considering an adaptative coordinates space. This coordinates space allows us to obtain statistical landmarking of cephalometric points used to define regions of interest. Each region takes advantage of a specific image processing, to enhance local and particular features (bone or suture). An augmented reality image is presented to the human expert, to focus on main sutures and bones in a small region of interest. This method is applied to the nettlesome problem of the interpretation of cephalometric radiographs, and provides satisfying results according to a cephalometric expert.

Partially observed objects localization with PCA and KPCA models

We deal with the problem of partially observed objects. These objects are defined by sets of points and their shape variations are represented by a statistical model. We present two models: a linear model based on PCA and a non-linear model based on KPCA (kernel PCA). The present work attempts to localize non visible parts of an object from visible parts and from the model, explicitly. using the variability represented by the model. Both are applied to the cephalometric problem with good results.