Direction-adaptive grey-level morphology. Application to 3D vascular brain imaging

International audienceSegmentation and analysis of blood vessels is an important issue in medical imaging. In 3D cerebral angiographic data, the vascular signal is however hard to accurately detect and can, in particular, be disconnected. In this article, we present a procedure utilising both linear, Hessian-based and morphological methods for blood vessel edge enhancement and reconnection. More specifically, multi-scale second-order derivative analysis is performed to detect candidate vessels as well as their orientation. This information is then fed to a spatially-variant morphological filter for reconnection and reconstruction. The result is a fast and effective vessel-reconnecting method

Morphology-based cerebrovascular atlas

International audienceCerebrovascular atlases can be used to improve medical tasks requiring the analysis of 3D angiographic data. The generation of such atlases remains however a complex and infrequently considered issue. The existing approaches rely on information exclusively related to the vessels. We alternatively investigate a new way, consisting of using both vascular and morphological information (i.e., cerebral structures) to improve the accuracy and relevance of the obtained vascular atlases. Experiments emphasise improvements in the main steps of the atlas generation process impacted by the use of morphological information. An example of cerebrovascular atlas obtained from a dataset of 56 MRAs acquired from several acquisition devices is finally provided

Filtrage d'objets fins : applications à l'analyse d'images vasculaires

The motivation of this work is filtering of elongated curvilinear objects in digital images. Their narrowness presents difficulties for their detection. In addition, they are prone to disconnections due to noise, image acquisition artefacts and occlusions by other objects. This work is focused on thin objects detection and linkage. For these purposes, a hybrid second-order derivative-based and morphological linear filtering method is proposed within the framework of scale-space theory. The theory of spatially-variant morphological filters is discussed and efficient algorithms are presented. From the application point of view, our work is motivated by the diagnosis, treatment planning and follow-up of vascular diseases. The first application is aimed at the assessment of arteriovenous malformations (AVM) of cerebral vasculature. The small size and the complexity of the vascular structures, coupled to noise, image acquisition artefacts, and blood signal heterogeneity make the analysis of such data a challenging task. This work is focused on cerebral angiographic image enhancement, segmentation and vascular network analysis with the final purpose to further assist the study of cerebral AVM. The second medical application concerns the processing of low dose X-ray images used in interventional radiology therapies observing insertion of guide-wires in the vascular system of patients. Such procedures are used in aneurysm treatment, tumour embolization and other clinical procedures. Due to low signal-to-noise ratio of such data, guide-wire detection is needed for their visualization and reconstruction. Here, we compare the performance of several line detection algorithms. The purpose of this work is to select a few of the most promising line detection methods for this medical applicationLe but de ce travail est de filtrer les objets fins et curvilinéaires dans les images numériques. Leur détection est en soit difficile du fait de leur finesse spatiale. De plus, le bruit, les artefacts de l'acquisition et les occlusions induites par d'autres objets introduisent des déconnexions. De ce fait, la reconnection des objets fins est également nécessaire. Dans ce but, une méthode hybride à base de dérivés secondes et de filtrage linéaire morphologique est proposée dans le cadre de la théorie espace-échelle. La théorie des filtres morphologiques spatialement variants et des algorithmes sont présentés. Du point de vue applicatif, notre travail est motivé par le diagnostic, la planification du traitement et le suivi des maladies vasculaires. La première application étudie les malformations artero-veineuses (MAV) dans le cerveau. L'analyse de telles données est rendue difficile par la petite taille, la complexité des vaisseaux couplés à diverses sources de bruit et à leur topologie, sans compter les artefacts d'acquisition et l'hétérogénéité du signal sanguin. Ainsi, nous nous sommes intéressés à l'amélioration et la segmentation des images angiographiques cérébrales dans le but d'aider à l'étude des MAVs cérébrales. La seconde application concerne le traitement des images en rayons X à faible dose utilisées en radiologie interventionelle dans le cas de l'insertion de guides dans les vaisseaux sanguins des patients. De telles procédures sont utilisées dans les traitements des anévrismes, des obstructions de tumeurs et d'autres procédures similaires. Dû au faible ratio signal à bruit, la détection des guides est indispensable pour leurs visualisations et leurs reconstructions. Dans ce travail, nous comparons la performance des algorithmes de filtrage d'objets linéiques. Le but étant de sélectionner les méthodes de détection les plus prometteuses dans le cadre de cette application médicale. La seconde application concerne le traitement des images X-ray à faible dose utilisées en radiologie interventionelle dans le cas d'insertion de guides dans les vaisseaux de patients. De telles procédures sont utilisées dans les traitements des anévrysmes, obstructions des tumeurs et d'autres procédures. Dû au faible ratio du signal-bruit, la détection des guides est indispensable pour leurs visualisations et leurs reconstructions. Dans ce travail, nous comparons la performance des algorithmes de filtrage d'objets linéaires. Le but est de sélectionner les méthodes de détection les plus prometteuses dans le cadre de cette application médical

Curvilinear morpho-Hessian filter

ISBN : 978-1-4244-2002-5International audienceThe motivation of this paper is the filtering of thin elongated objects, such as veins, fibres etc. In particular, we focus our attention on detecting thin segments and linking them when disconnection is due to noise. A hybrid Hessian-based and morphological linear filtering method is proposed within the framework of scale space theory. For each pixel the second- derivative response kernel is collected and formed into a Hessian matrix which undergoes eigen analysis. From there, the best eigenvalue response along with the corresponding eigenvector is chosen on different scales. Using the resulting principal directions of linear segment pixels, morphological filters are used to track and connect the linears

Semi-connections and hierarchies

International audienceConnectivity is the basis of several methodological concepts in mathematical morphology. In graph-based approaches, the notion of connectivity can be derived from the notion of adjacency. In this preliminary work, we investigate the effects of relaxing the symmetry property of adjacency. In particular, we observe the consequences on the induced connected components, that are no longer organised as partitions but as covers, and on the hierarchies that are obtained from such components. These hierarchies can extend data structures such as component-trees and partition-trees, and the associated filtering and segmentation paradigms, leading to improved image processing tools

Continuous maximum flow segmentation method for nano-particle interaction analysis

In recent years, tomographic 3D reconstruction approaches using Electrons rather than X-Rays have become popular. Such images produced with a Transmission Electron Microscope (TEM) make it possible to image nanometerscale materials in 3D. However, they are also noisy, limited in contrast, and PGP: A812 3708 B2E1 3B42 500E D50C 49A0 88FA C060 3F65 1 most often have a very poor resolution along the axis of the electron beam. The analysis of images stemming from such modalities, whether fully or semi automated, is therefore more complicated. In particular, segmentation of objects is difficult. In this article, we propose to use the continuous maximum flow segmentation method based on a globally optimal minimal surface model. The use of this fully automated segmentation and filtering procedure hal-00865924, version 1- 25 Sep 2013 is illustrated on two different nano-particle samples and provide comparisons with other classical segmentation methods. The main objectives are the measurement of the attraction rate of polystyrene beads to silica nano-particle (for the first sample) and interaction of silica nano-particles with large unilamellar liposomes (for the second sample). We also illustrate how precise measurements such as contact angles can be performed

Spatially-variant morpho-Hessian filter: Efficient implementation and application

International audienceElongated objects are more difficult to filter than more isotropic ones because they locally comprise fewer pixels. For thin linear objects, this problem is compounded because there is only a restricted set of directions that can be used for filtering, and finding this local direction is not a simple problem. In addition, disconnections can easily appear due to noise. In this paper we tackle both issues by combining a linear filter for direction finding and a morphological one for filtering. More specifically, we use the eigen-analysis of the Hessian for detecting thin, linear objects, and a spatially-variant opening or closing for their enhancement and reconnection. We discuss the theory of spatially-variant morphological filters and present an efficient algorithm. The resulting spatially-variant morphological filter is shown to successfully enhance directions in 2D and 3D examples illustrated with a brain blood vessel segmentation problem

Segmentation of 3D nano-scale polystyrene balls

Pages: 37-3