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

Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery

International audienceOBJECTIVE: To improve the planning of hepatic surgery, we have developed a fully automatic anatomical, pathological, and functional segmentation of the liver derived from a spiral CT scan. MATERIALS AND METHODS: From a 2 mm-thick enhanced spiral CT scan, the first stage automatically delineates skin, bones, lungs, kidneys, and spleen by combining the use of thresholding, mathematical morphology, and distance maps. Next, a reference 3D model is immersed in the image and automatically deformed to the liver contours. Then an automatic Gaussian fitting on the imaging histogram estimates the intensities of parenchyma, vessels, and lesions. This first result is next improved through an original topological and geometrical analysis, providing an automatic delineation of lesions and veins. Finally, a topological and geometrical analysis based on medical knowledge provides hepatic functional information that is invisible in medical imaging: portal vein labeling and hepatic anatomical segmentation according to the Couinaud classification. RESULTS: Clinical validation performed on more than 30 patients shows that delineation of anatomical structures by this method is often more sensitive and more specific than manual delineation by a radiologist. CONCLUSION: This study describes the methodology used to create the automatic segmentation of the liver with delineation of important anatomical, pathological, and functional structures from a routine CT scan. Using the methods proposed in this study, we have confirmed the accuracy and utility of the creation of a 3D liver model compared with the conventional reading of the CT scan by a radiologist. This work may allow improved preoperative planning of hepatic surgery by more precisely delineating liver pathology and its relationship to normal hepatic structures. In the future, this data may be integrated with computer-assisted surgery and thus represents a first step towards the development of an augmented-reality surgical system

Generalized ellipsoids and anisotropic filtering for segmentation improvement in 3D medical imaging

Deformable models have demonstrated to be very useful techniques for image segmentation. However, they present several weak points. Two of the main problems with deformable models are the following: (1) results are often dependent on the initial model location, and (2) the generation of image potentials is very sensitive to noise. Modeling and preprocessing methods presented in this paper contribute to solve these problems. We propose an initialization tool to obtain a good approximation to global shape and location of a given object into a 3D image. We also introduce a novel technique for corner preserving anisotropic diffusion filtering to improve contrast and corner measures. This is useful for both guiding initialization (global shape) and subsequent deformation for fine tuning (local shape).This work was supported by the Spanish Government and the Xunta de Galicia by projects TIC2000-0399-C02-02 and PGIDT99PXI20606B, respectively.2005-04-01S

Automatical segmentation : Application to 3D angiograms of the liver

As part of a hepatic surgery simulator, we have developed a new method for the extraction of the portal vein's vascular tree i n 3D liver angioscanners . In practice, this tree is used to localize the different anatomical segments that correspond to the unit o f surgical ablation of the liver . Our method thus facilitates the surgeon's task by automatically giving the 3D model of the portal vei n in a three-step segmentation . The first step reduces the image to the ROI defined by the liver contours and increases its qualit y by an anisotropic filtering . The second step performs the segmentation of vascular networks by a global thresholding followed b y a local analysis . The third step translates a priori knowledge in topological and geometrical constraints . This last step allows to remove mistakes due to the anisotropy of the images by disconnecting the different vascular trees in order to extract the porta l vein . Results on 12 patients, validated by a radiologist, showed that the algorithm automatically extracts the principal branche s of the portal vein, allowing to delimit the anatomical segments defined in the conventional liver anatomy .Dans le cadre de la réalisation d'un simulateur de chirurgie laparoscopique' du foie, nous avons développé une nouvelle méthode permettant d'extraire dans les angioscanners 3D du foie, le réseau vasculaire de la veine porte. Ce réseau est utilisé en pratique pour repérer les différents segments anatomiques, qui représentent l'unité d'intervention dans les exérèses2 du foie. Notre méthode facilite ainsi la tâche des chirurgiens, en leur fournissant automatiquement le modèle 3D de la veine porte par une segmentation décomposée en trois étapes. La première étape réduit l'image à la région d'intérêt correspondant au contour du foie et améliore sa qualité en réalisant un filtrage anisotrope. La seconde segmente les réseaux vasculaires et appliquant un seuillage global, suivi d'une analyse locale. La troisième étape traduit les connaissances a priori que nous avons des réseaux vasculaires, en contraintes topologiques et géométriques. Cette dernière étape permet de corriger les problèmes résultant de l'anisotropie des images, en déconnectant les différentes arborescences du foie pour en extraire la veine porte. Les résultats obtenus sur douze patients, et vérifiés par un radiologue, montrent que l'algorithme extrait automatiquement les principales branches de la veine porte, permettant de délimiter les segments anatomiques définis dans l'anatomie conventionnelle du foie

Computational methods to predict and enhance decision-making with biomedical data.

The proposed research applies machine learning techniques to healthcare applications. The core ideas were using intelligent techniques to find automatic methods to analyze healthcare applications. Different classification and feature extraction techniques on various clinical datasets are applied. The datasets include: brain MR images, breathing curves from vessels around tumor cells during in time, breathing curves extracted from patients with successful or rejected lung transplants, and lung cancer patients diagnosed in US from in 2004-2009 extracted from SEER database. The novel idea on brain MR images segmentation is to develop a multi-scale technique to segment blood vessel tissues from similar tissues in the brain. By analyzing the vascularization of the cancer tissue during time and the behavior of vessels (arteries and veins provided in time), a new feature extraction technique developed and classification techniques was used to rank the vascularization of each tumor type. Lung transplantation is a critical surgery for which predicting the acceptance or rejection of the transplant would be very important. A review of classification techniques on the SEER database was developed to analyze the survival rates of lung cancer patients, and the best feature vector that can be used to predict the most similar patients are analyzed

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

International audienceno abstrac

Directional anisotropic diffusion applied to segmentation of vessels in 3D images

Theme 3 - Interaction homme-machine, images, donnees, connaissances. Projet EpidaureAvailable at INIST (FR), Document Supply Service, under shelf-number : 14802 E, issue : a.1996 n.3064 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Amélioration des ouvertures par chemins pour l'analyse d'images à N dimensions et implémentations optimisées

La détection de structures fines et orientées dans une image peut mener à un très large champ d'applications en particulier dans le domaine de l'imagerie médicale, des sciences des matériaux ou de la télédétection. Les ouvertures et fermetures par chemins sont des opérateurs morphologiques utilisant des chemins orientés et flexibles en guise d'éléments structurants. Ils sont utilisés de la même manière que les opérateurs morphologiques utilisant des segments orientés comme éléments structurants mais sont plus efficaces lorsqu'il s'agit de détecter des structures pouvant être localement non rigides. Récemment, une nouvelle implémentation des opérateurs par chemins a été proposée leur permettant d'être appliqués à des images 2D et 3D de manière très efficace. Cependant, cette implémentation est limitée par le fait qu'elle n'est pas robuste au bruit affectant les structures fines. En effet, pour être efficaces, les opérateurs par chemins doivent être suffisamment longs pour pouvoir correspondre à la longueur des structures à détecter et deviennent de ce fait beaucoup plus sensibles au bruit de l'image. La première partie de ces travaux est dédiée à répondre à ce problème en proposant un algorithme robuste permettant de traiter des images 2D et 3D. Nous avons proposé les opérateurs par chemins robustes, utilisant une famille plus grande d'éléments structurants et qui, donnant une longueur L et un paramètre de robustesse G, vont permettre la propagation du chemin à travers des déconnexions plus petites ou égales à G, rendant le paramètre G indépendant de L. Cette simple proposition mènera à une implémentation plus efficace en terme de complexité de calculs et d'utilisation mémoire que l'état de l'art. Les opérateurs développés ont été comparés avec succès avec d'autres méthodes classiques de la détection des structures curvilinéaires de manière qualitative et quantitative. Ces nouveaux opérateurs ont été par la suite intégrés dans une chaîne complète de traitement d'images et de modélisation pour la caractérisation des matériaux composite renforcés avec des fibres de verres. Notre étude nous a ensuite amenés à nous intéresser à des filtres morphologiques récents basés sur la mesure de caractéristiques géodésiques. Ces filtres sont une bonne alternative aux ouvertures par chemins car ils sont très efficaces lorsqu'il s'agit de détecter des structures présentant de fortes tortuosités ce qui est précisément la limitation majeure des ouvertures par chemins. La combinaison de la robustesse locale des ouvertures par chemins robustes et la capacité des filtres par attributs géodésiques à recouvrer les structures tortueuses nous ont permis de proposer un nouvel algorithme, les ouvertures par chemins robustes et sélectives.The detection of thin and oriented features in an image leads to a large field of applications specifically in medical imaging, material science or remote sensing. Path openings and closings are efficient morphological operators that use flexible oriented paths as structuring elements. They are employed in a similar way to operators with rotated line segments as structuring elements, but are more effective as they can detect linear structures that are not necessarily locally perfectly straight. While their theory has always allowed paths in arbitrary dimensions, de facto implementations were only proposed in 2D. Recently, a new implementation was proposed enabling the computation of efficient d-dimensional path operators. However this implementation is limited in the sense that it is not robust to noise. Indeed, in practical applications, for path operators to be effective, structuring elements must be sufficiently long so that they correspond to the length of the desired features to be detected. Yet, path operators are increasingly sensitive to noise as their length parameter L increases. The first part of this work is dedicated to cope with this limitation. Thus, we will propose an efficient d-dimensional algorithm, the robust path operators, which use a larger family of flexible structuring elements. Given an arbitrary length parameter G, path propagation is allowed if disconnections between two pixels belonging to a path is less or equal to G and so, render it independent of L. This simple assumption leads to a constant memory bookkeeping and results in a low complexity. The developed operators have been compared qualitatively and quantitatively to other efficient methods for the detection of line-like features. As an application, robust path openings have been integrated into a complete chain of image processing for the modelling and the characterization of glass fibers reinforced polymer. Our study has also led us to focus our interest on recent morphological connected filters based on geodesic measurements. These filters are a good alternative to path operators as they are efficient at detecting the so-called "tortuous" shapes in an image which is precisely the main limitation of path operators. Combining the local robustness of the robust path operators with the ability of geodesic attribute-based filters to recover "tortuous" shapes have enabled us to propose another original algorithm, the selective and robust path operators.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF