Showing papers by "Nicholas Ayache published in 1995"

Building, registrating, and fusing noisy visual maps

Abstract: This paper deals with the problem of building three-dimen sional descriptions (we call them visual maps) of the environ ment of a mobile robot using passive vision. These maps are local (i.e., attached to specific frames of reference). Since noise is present, they incorporate information about the ge ometry of the environment and about the uncertainty of the parameters defining the geometry. This geometric uncertainty is directly related to its source (i.e., sensor uncertainty). We show how visual maps corresponding to different positions of the robot can be registered to compute a better estimate of its displacement between the various viewpoint positions, as suming an otherwise static environment. We use these esti mates to fuse the different visual maps and reduce locally the uncertainty of the geometric primitives which have found correspondents in other maps. We propose to perform these three tasks (building, registrating, and fusing visual maps) within the general framework of extended Kalman filter...

Automatic Retrieval of Anatomical Structures in 3D Medical Images

Abstract: This paper describes a method to automatically generate the mapping between a completely labeled reference image and 3D medical images of patients. To achieve this, we combined three techniques: the extraction of 3D feature lines, their non-rigid registration and the extension of the deformation to the whole image space using warping techniques. As experimental results, we present the retrieval of the cortical and ventricles structures in MRI images of the brain.

A General Scheme for Automatically Building 3D Morphometric Anatomical Atlases: application to a Skull Atlas

Abstract: In this research report, we present a general scheme for building entirely automatically a morphometric anatomical atlas from 3D medical images (CT-Scan, Magnetic Resonance Imagery). We detail each step of the method, including the non-rigid registration algorithm, 3D feature lines averaging, and statistical processes. We apply the method to obtain a quantitative atlas of crest lines of the skull. Finally, we use the resulting atlas to study a craniofacial disease: we show how we can obtain qualitative and quantitative medical results by contrasting a skull affected by Crouzon's disease with the atlas.

A New Framework for Fusing Stereo Images with Volumetric Medical Images

Abstract: Some medical interventions require knowing the correspondence between an MRI/CT image and the actual position of the patient. Examples are in neurosurgery or radiotherapy, but also in video surgery (laparoscopy). Recently, computer vision techniques have been proposed to find this correspondence without any artificial markers. Following the pioneering work of [GLPI+94], [CZH+94], [CDT+92], [SHK94] and [STAL94], we propose in this paper an alternative approach.

Dense Non-Rigid Motion Estimation in Sequences of Medical Images Using Differential Constraints

Abstract: We describe a new method for computing the displacement vector field in time sequences of 2D or 3D images (4D data). The method is an energy-minimizing method; the energy is splitted into two terms, with one term matching differential singularities in the images, and the other constraining the regularity of the field. In order to reduce the computational time, we introduce an adaptive mesh the resolution of which depends on the value of the gradient intensity. We present experimental results on medical images.

Superquadrics and Free-Form Deformations: A Global Model to Fit and Track 3D Medical Data

Abstract: Recovery of 3-D data with simple parametric models has been the subject of many studies over the last ten years. Many have used the notion of superquadrics, introduced for graphics in [4]. It appears, however, that although superquadrics can describe a wide variety of forms, they are too simple to recover and describe complex shapes.

3D-2D projective registration of free-form curves and surfaces

Abstract: Some medical interventions require knowing the correspondence between an MRI/CT pre-operative image and the actual position of the patient. Examples occur in neurosurgery, radiotherapy, interventional radiology, but also in video surgery (laparoscopy). We present in this article three new techniques for performing this task without artificial markers. We find the 3D-2D projective transformation (composition of a rigid displacement and a perspective projection) which maps a 3D object onto a 2D image of this object. Depending on the object model (curve or surface), and on the 2D image acquisition system (X-Ray, video), the techniques are different but the framework is common. It does not depend on the initial relative positions of the objects and deals with the occlusions and the outliers. Results are presented on real medical data to demonstrate the validity of our approach. >

Dense Non-Rigid Motion Estimation in Sequences of 3D Images Using Differential Constraints

Abstract: We describe a new method for computing the displacement vector field in time sequences of 3D images (4D data). The method is an energy-minimizing method; the energy is splitted into two terms, with one term matching differential singularities in the images, and the other constraining the regularity of the field. In order to reduce the computational time, we introduce an adaptive volume mesh the resolution of which depends on the presence of high gradient. We next perform a modal analysis, which allows a compact representation of the deformation by a reduced number of parameters. We present experimental results on synthetic data and on medical images.

Accurate and robust stereovision with a large number of aerial images

Abstract: Automatic computation of 3D reconstruction of scenes is traditionally based on the use of the normalized cross-correlation technique to match stereoscopic images. This matching technique called area-based matching technique allows to retrieve which pixels on images are the projection of the same 3D point of the analyzed scene. In the case of high resolution stereoscopic images which include a ground resolution up to a few decimeters, the matching problem is more difficult because of the presence shadow areas, hidden parts, important discontinuities in the 3D structures and textureless or repetitive-texture regions. These characteristics of high resolution stereoscopic images appear as real obstacles to the area-based matching technique with binocular stereovision approach. In this paper, we present a novel method to compare a dense scene 3D reconstruction from a large number of aerial images. It achieves robust and accurate reconstruction and deals with local occlusions and surface discontinuities. The principle of the algorithm is based on the simultaneous matching of images with a cross-correlation technique. The location of each cameras is unconstrained and a calibration stage is used to retrieve the epipolar geometry. We finally show the feasibility of this approach to produce robust and accurate matchings on results achieved with synthetic and real images.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.