Automatic calculation of chamfer mask coefficients for large masks and anisotropic images

Chamfer disctances are widely used in image analysis, and many ways have been investigated to compute optimal chamfer mask coefficients. Unfortunately, these methods are not systematized: they have to be conducted manually for every mask size or image anisotropy. Since image acquisistion (e.g. medical imaging) can lead to anisotropic discrete grids with unpredictable anisotropy value, automated calculation of chamfer mask coefficients becomes mandatory for efficient distance map computation. This report presentes a systematized calculation of these coefficients based on the automatic construction of chamfer masks of any size associated with a triangulation that allows to derive analytically the relative error with respect to the Euclidean distance, in any 3-D anisotropic lattice and that also allows to compute norm constraints

Systematized calculation of optimal coefficients of 3-D chamfer norms

International audienceChamfer distances are widely used in image analysis, and many ways have been investigated to compute optimal chamfer mask coefficients. Unfortunately, these methods are not systematized: they have to be conducted manually for every mask size or image anisotropy. Since image acquisition (e.g. medical imaging) can lead to anisotropic discrete grids with unpredictable anisotropy value, automated calculation of chamfer mask coefficients becomes mandatory for efficient distance map computation. This article presents a systematized calculation of these coefficients based on the automatic construction of chamfer masks of any size associated with a triangulation that allows to derive analytically the relative error with respect to the Euclidean distance, in any 3-D anisotropic lattice

On optimal chamfer masks and coefficients

This report describes the calculation of local errors in Chamfer masks both in two- and in three-dimensional anisotropic spaces. For these errors, closed forms are given that can be related to the Chamfer mask geometry. Thanks to these calculation, it can be obsrved that the usual Chamfer masks (i.e. 3x3x3 or 5x5x5) have an inhomogeneously distributed error. Moreover, it allows us to design dedicated Chamfer masks by controlling either the complexity of the computation of the distance map (or equivalently the number of vectors in the mask), or the error of the mask in \mathbbZ^2 or in \mathbbZ^3. Last, since Chamfer distances are usually computed with integer weights (and approximate the Euclidean distance up to a multiplicative factor), we demonstrate that the knowledge of the local errors allows a very efficient computation of these weights

3D/4D ultrasound registration of bone

This paper presents a method to reduce the invasiveness of Computer Assisted Orthopaedic Surgery (CAOS) using ultrasound. In this goal, we need to develop a method for 3D/4D ultrasound registration. The premilinary results of this study suggest that the development of a robust and ``realtime'' 3D/4D ultrasound registration is feasible

Automatic 3D seed location and orientation in CT images for prostate brachytherapy

International audienceIn prostate brachytherapy, the analysis of the 3D pose information of each individual implanted seed is one of the critical issues for dose calculation and procedure quality assessment. This paper addresses the development of an automatic image processing solution for the separation, localization and 3D orientation estimation of prostate seeds. This solution combines an initial detection of a set of seed candidates in CT images (using a thresholding and connected component method) with an orientation estimation using principal components analysis (PCA). The main originality of the work is the ability to classify the detected objects based on a priori intensity and volume information and to separate groups of seeds using a modified k-means method. Experiments were carried out on CT images of a phantom and a patient aiming to compare the proposed solution with manual segmentation or other previous work in terms of detection performance and calculation time

Extraction de paramètres morphométriques pour l'étude du réseau micro-vasculaire cérébral

Our aim is to provide anatomists and neuroanatomists with software tools to quantitatively analyze 3-D images of the cerebral micro-vascular network. Such analyses require that input images be both of high resolution and of large size, to take into account the smallest capillaries and cover areas of the brain sufficiently wide to be statistically relevant, respectively. As it cannot be acquired at once, we propose to pave the image area with smaller images acquired with a confocal microscope to obtain an "image mosaic". We developed dedicated building tools for this kind of mosaic to allow large and precise images. But these images are to large to be loaded and process at once in the memory of a standard computer. We therefor developed dedicated image analysis tools (filtering, thresholding, mathematical morphology, discrete topology tools...) for such mosaic which process with sub-images. Micro-vascular network analysis requires vessels' center lines extraction and a vessel diameters estimation. Discrete geometry lends itself as a particularly appropriate and powerful framework for this kind of processes. We indeed have to compute distance map on each image point. To obtain the best trade-off between precision and computational cost, we choosed chamfer distances. One of our contributions was to propose an automatic computation of chamfer coefficients adapted to any grid anisotropy. The use of such distance maps can guide skeletonisaton algorithms. Such algorithms require to keep the global property of topology. As we can only access sub-images we proposed a new skeletonization algorithm which minimizes the number of disk access to guaranty an acceptable computational time as well as a good skeleton localization. The developed algorithms have been integrated within the ergonomic software Amira and are currently in use at the INSERM research institute.L'objectif de cette thèse est de fournir des outils logiciels aux anatomistes et neuro-anatomistes afin de permettre une analyse tridimensionnelle quantitative des réseaux micro-vasculaires cérébraux. Cette analyse demande des images de très haute résolution (permettant de tenir compte du plus petit capillaire), mais aussi des images couvrant une surface du cortex suffisamment large pour être statistiquement significative. Comme elle ne peut être acquise en une seule fois, nous proposons de paver la surface à imager de plusieurs petites images et de créer ainsi une grande "mosaïque d'images". Chaque image est acquise grâce à un microscope confocal dont la résolution impose une grille anisotrope. Nous avons alors développé des outils de reconstruction spécifiques pour ce genre de mosaïques afin de générer des images à la fois très étendues et très précises. Or ces images sont trop volumineuses pour être chargées et traitées en une seule fois dans la mémoire d'un ordinateur standard. Nous avons donc développé des outils spécifiques de traitement d'image (filtrage, seuillage, outils de morphologie mathématique, de topologie discrète...) décomposés en traitements en sous-images. L'étude quantitative du réseau micro-vasculaire cérébral nécessite l'extraction des lignes centrales et une estimation des diamètres des vaisseaux. La géométrie discrète offre un cadre de travail rapide et puissant pour ce type de calculs. En effet, nous devons calculer une carte de distance en tout point de l'image. Afin d'avoir la meilleure précision possible tout en gardant un temps de traitement raisonnable, nous avons choisi une carte de distance du chanfrein. Une de nos contributions a été de proposer un calcul automatique des coefficients de chanfrein permettant de s'adapter à tout type d'anisotropie de grille. L'utilisation de telles cartes de distances permet de guider des algorithmes de squelettisation. De tels outils nécessitent la conservation d'une propriété globale, la topologie. Comme nous nous plaçons dans un cadre où l'on a accès qu'à des sous images, nous avons proposé un nouvel algorithme de squelettisation qui minimise le nombre d'accès à des sous-images afin de garantir un temps de calcul acceptable, tout en localisant correctement le squelette. Ces algorithmes ont été intégrés dans le logiciel ergonomique Amira et sont utilisés par les chercheurs de l'unité U455 de l'INSERM

3-D chamfer distances and norms in anisotropic grids

International audienceChamfer distances are widely used in image analysis and many authors have investigated the computation of optimal chamfer mask coefficients. Unfortunately, these methods are not systematized: calculations have to be conducted manually for every mask size or image anisotropy. Since image acquisition (e.g. medical imaging) can lead to discrete anisotropic grids with unpredictable anisotropy value, automated calculation of chamfer mask coefficients becomes mandatory for efficient distance map computations. This article presents an automatic construction for chamfer masks of arbitrary sizes. This allows, first, to derive analytically the relative error with respect to the Euclidean distance, in any 3-D anisotropic lattice, and second, to compute optimal chamfer coefficients. In addition, the resulting chamfer map verifies discrete norm conditions

3-D chamfer distances and norms in anisotropic grids

Abstract Chamfer distances are widely used in image analysis and many authors have investigated the computation of optimal chamfer mask coefficients. Unfortunately, these methods are not systematized: calculations have to be conducted manually for every mask size or image anisotropy. Since image acquisition (e.g. medical imaging) can lead to discrete anisotropic grids with unpredictable anisotropy value, automated calculation of chamfer mask coefficients becomes mandatory for efficient distance map computations. This article presents an automatic construction for chamfer masks of arbitrary sizes. This allows, first, to derive analytically the relative error with respect to the Euclidean distance, in any 3-D anisotropic lattice, and second, to compute optimal chamfer coefficients. In addition, the resulting chamfer map verifies discrete norm conditions.

Non invasive ultrasound-based bone tracking

International audienceIn the field of computer assisted orthopaedic surgery (CAOS), the motion of bones is generally tracked using a three-dimensional position measurement system. This is typically carried out by invasively attaching a marker to the bone. This is performed by inserting pins or screws, creating holes and causing trauma to the tissue and structure. This may increase the risk of bone fracture, infection and cause extra pain owing to extra inscisions. The development of 3D ultrasound (US) offers interesting prospects for surgical navigation. Indeed, this non-invasive data and real-time imaging technology could reduce the invasiveness of CAOS by replacing the optical trackers which are currently used. This implies the development of a method for rigid registration in order to track a bony structure. This method has to be at the same time robust and real-time. However, due to speckle, shadowing effects and the poor quality of US images, the registration of this imaging modality is a challenging process. The approach we develop consists in registering a reference 3D US volume to real time 4D US orthogonal slices. Since little information is present in these 4D images and to maximize their overlap to the reference volume, an initial stage consists in building a panoramic reference volume by registering overlapping 3D US volumes. This paper describes the registration method and the evaluation of the algorithm