Model Based Multiscale Detection and Reconstruction of 3D Vessels

The segmentation of 3D brain vessels is an important issue for physicians in order to operate an aneurysm. We introduce new vessel models for selecting a subset of interesting points near the vessel center. We also present a new approach to segment and reconstruct 3D brain vessels. The response at one scale is obtained by integrating along a circle the first derivative of the intensity in the radial direction. We also use a vessel model to choose a good parameter for a gamma-normalizatio- n of the response obtained at each scale. Once the parameter gamma is fixed, we find the relation between a vessel radius and the scale at which it is detected. From the multiscale response, we create a smoothed skeleton of the vessels and we reconstruct the vessels from their centerlines and their radii. The method has been tested on a large variety of 3D images of cerebral vessels, with excellent results. Vessels of various size and contrast are detected with remarkable robustness, even when they are close or tangent to another vessel, and most junctions are preserved. Results are obtained in a few minutes on a Dec-Alpha workstation, for a 128^3 image. This work was done in collaboration with General Electric Medical Systems Europe (GEMSE)

Directional Anisotropic Diffusion Applied to Segmentation of Vessels in 3D Images

Anisotropic Diffusion is a new method derived from the convolution with a Gaussian, which allows to reduce the noise in the image without blurring the frontiers between different regions. This process can be applied in medical image analysis to segment the different anatomical structures. In this report, we introduce a new implementation of the anisotropic diffusion which allows us to reduce the noise and better preserve small structures like vessels in 3D images. This method is based on the differentiation of the diffusion in the direction of the gradient, and in the directions of the minimum and the maximum curvature. This algorithm gave good results on both synthetic and real images. We append to this work a part of the master's thesis of the first author (in French) which details several points of interest

Fast sub-voxel re-initialization of the distance map for level set methods

Redistancing the implicit surface is currently the most time-consuming stage in Level Set Methods, usually accomplished by applying the Fast Marching algorithm to a binarized image. We propose to apply a faster and linear approximation of the Euclidian Distance while maintaining the sub-voxel accuracy of the interface

TRAITEMENT MULTI-ECHELLE (APPLICATIONS A L'IMAGERIE MEDICALE ET A LA DETECTION TRIDIMENSIONNELLE DE VAISSEAUX)

GRENOBLE-MI2S (384212302) / SudocNICE-BU Sciences (060882101) / SudocSudocFranceF

ISSN:....-.... Algorithms for Extracting Vessel Centerlines

1 The detection of the vessels centerlines is a useful preprocessing step for 3D quantification of stenosis, topological representation of the vessel tree and registration with an atlas, virtual endoscopy, and visualization of the vascular network. We propose to compare three classes of algorithms leading to centerline representations of the vessels. The first one relies on a pre-segmentation of the vessels, given by a binary image, to compute a topological invariant skeleton. The second method extracts sub-voxel centerlines as ridges of the image intensity. It uses the gradient and the Hessian matrix to interpolate the zero-crossings of the gradient vector in the cross-sectional directions. The third method uses an integration of the gradient information along circles of different radii in the cross-sections, in order to find points located at equal distance from the contours. We present results on a Magnetic Resonance Angiography, show the advantages and the drawbacks of each method and present some perspectives. 1 Grant support: NIH P41-RR13218 and CIMI

Directional Anisotropic Diffusion Applied to Segmentation of Vessels in 3D Images

International audienceno abstrac

Anisotropic Diffusion of Ultrasound Constrained by Speckle Noise Model.

Ultrasound images provide the clinician with non-invasive, low cost, and real-time images that can help them in diagnosis and plannnig and therapy. However, although the human eye is able to derive the meaningful information from these images, automatic processing is very difficult because of the noise and artefacts present in the image. In this work, we propose to extend the current anisotropic diffusion technique to deal with the speckle noise present in the Ultrasound images. To this end, we use a previously derived model of the noise, and we write the restoration scheme as a energy minization constrained by the noise model and parameters. This approach leads to a new data attachment term whose optimal weight can be automatically estimated. 1 1 This work was supported by CIMIT