Unsupervised Fiber Bundles Registration using Weighted Measures Geometric Demons

International audienceBrain image registration aims at reducing anatomical variability across subjects to create a common space for group analysis. Multi-modal approaches intend to minimize cortex shape variations along with internal structures, such as fiber bundles. A di ficulty is that it requires a prior identi fication of these structures, which remains a challenging task in the absence of a complete reference atlas. We propose an extension of the log-Geometric Demons for jointly registering images and fi ber bundles without the need of point or ber correspondences. By representing fi ber bundles as Weighted Measures we can register subjects with di fferent numbers of fiber bundles. The ef ficacy of our algorithm is demonstrated by registering simultaneously T1 images and between 37 and 88 ber bundles depending on each of the ten subject used. We compare results with a multi-modal T1 + Fractional Anisotropy (FA) and a tensor-based registration algorithms and obtain superior performance with our approach

Surface fluid registration of conformal representation: Application to detect disease burden and genetic influence on hippocampus

abstract: In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometty (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E(is an element of)4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research.NOTICE: this is the author’s version of a work that was accepted for publication in NEUROIMAGE. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neuroimage, 78, 111-134 [2013] http://dx.doi.org/10.1016/j.neuroimage.2013.04.01

Statistical Shape Analysis of Large Datasets Based on Diffeomorphic Iterative Centroids

In this paper, we propose an approach for template-based shape analysis of large datasets, using diffeomorphic centroids as atlas shapes. Diffeomorphic centroid methods fit in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework and use kernel metrics on currents to quantify surface dissimilarities. The statistical analysis is based on a Kernel Principal Component Analysis (Kernel PCA) performed on the set of initial momentum vectors which parametrize the deformations. We tested the approach on different datasets of hippocampal shapes extracted from brain magnetic resonance imaging (MRI), compared three different centroid methods and a variational template estimation. The largest dataset is composed of 1,000 surfaces, and we are able to analyse this dataset in 26 h using a diffeomorphic centroid. Our experiments demonstrate that computing diffeomorphic centroids in place of standard variational templates leads to similar shape analysis results and saves around 70% of computation time. Furthermore, the approach is able to adequately capture the variability of hippocampal shapes with a reasonable number of dimensions, and to predict anatomical features of the hippocampus, only present in 17% of the population, in healthy subjects

Diffeomorphic brain registration under exhaustive sulcal constraints.

International audienceThe alignment and normalization of individual brain structures is a prerequisite for group-level analyses of structural and functional neuroimaging data. The techniques currently available are either based on volume and/or surface attributes, with limited insight regarding the consistent alignment of anatomical landmarks across individuals. This article details a global, geometric approach that performs the alignment of the exhaustive sulcal imprints (cortical folding patterns) across individuals. This DIffeomorphic Sulcal-based COrtical (DISCO) technique proceeds to the automatic extraction, identification and simplification of sulcal features from T1-weighted Magnetic Resonance Image (MRI) series. These features are then used as control measures for fully-3-D diffeomorphic deformations. Quantitative and qualitative evaluations show that DISCO correctly aligns the sulcal folds and gray and white matter volumes across individuals. The comparison with a recent, iconic diffeomorphic approach (DARTEL) highlights how the absence of explicit cortical landmarks may lead to the misalignment of cortical sulci. We also feature DISCO in the automatic design of an empirical sulcal template from group data. We also demonstrate how DISCO can efficiently be combined with an image-based deformation (DARTEL) to further improve the consistency and accuracy of alignment performances. Finally, we illustrate how the optimized alignment of cortical folds across subjects improves sensitivity in the detection of functional activations in a group-level analysis of neuroimaging data

Development of acquisition system and algorithms for registration towards modeling displacement and deformation of the contour on the digital image

Centralna tema ovog rada je primena sistema za akviziciju slike u cilju procene i modelovanja deformacija i pomeranja objekata koji su snimljeni. Glavna metoda koja je pri tom korišćena je metoda registracije slika. Sam postupak registracije podrazumeva skup algoritama i metoda kojim se vrši pronalaženje transformacije koja preslikava prostor jedne slike u prostor druge. Ukoliko se radi o slikama istog objekta u različitim položajima ili konfiguracijama moguće je odrediti pomeranja i deformacije željene tačke poznavanjem ove transformacije. U radu su opisani već postojeći algoritmi, sa svojim najznačajnijim svojstvima. Na bazi ovih osobina razvijen je metod registracije baziran na rešavanju Laplasove jednačine za elektrostatičko polje. Ovakav pristup je moguć zahvaljujući činjenici da gradijent deformacija odgovara linijama elektrostatičkog polja, koje je dobijeno rešavanjem Laplasove jednačine i zadovoljava sva bitna svojstva koja treba da ima registraciona transformacija. Ove osobine se odnose na glatkost polja deformacije, postojanje inverzne funkcije i zabranu ukrštanja linija polja. Sam postupak rešavanja navedene jednačine i određivanje tražene transformaicje sproveden je primenom metode konačnih elemenata pri čemu je korišćena formulacija minimuma energija sistema. Jedna od inspiracija za rad na metodama registracije slike bio je i problem procene mehaničkih karakteristika tkiva aorte sa aneurizmom. U radu je opisana realizacija i način rada sistema koji je iskorišćen za karakterizaciju mehaničkih svojstava aorte, koji kao izlazne podatke daje informaciju o pomeranjima skupa tačaka tkiva kao i o vrednostima pritiska fluida koji izaziva ta pomeranja. Deformacije su procenjene primenom metoda segmentacije slike i izdvajanja ivica nakon čega je primenjen metod registracije slike kojom je određena deformacija tačaka tkiva u određenim vremenskim trenucima. Na osnovu ovih vrednosti primenom genetskog algoritma određena je vrednost Jangovog modula tkiva pri čemu je korišćen mehanički model deformacije tkiva. Analiza hoda upotrebom slika hoda je takođe jedan od izazova kada je u pitanju neinvazivna dijagnostika i praćenje stanja dijagnostifiko- vanih kao i zdravih subjekata. U ovom radu je prikazan postupak određivanja mehaničkog naprezanja hrskavice primenom slika snimljenih kamerom i vrednostima sile normalne reakcije podloge koja nastaje tokom hoda. Za procenu deformacija hrskavice korišćeni su algoritmi registracije slike između slika dobijenih sa kamere i slika dobijenih kompjuterizovanom tomografijom. Postupkom optimizacije procenjeni su i mehanički parametri hrskavice (Jangov modul i Poasonov koeficijent).The main aim of this thesis is the application of image acquisition system for the purpose of assessing and modeling the deformation and displacement of the objects acquired in digital images. The technique used in the study is method of image registration. The procedure of the registration includes a set of algorithms and methods which performs the assessment of transformation that maps the space of one image to another one. If there are images of the same object in different positions or configurations it is possible to determine the displacement and deformation of the desired point of understanding this transformation. The thesis describes the existing algorithms, along with their most important properties. The novel algorithms for image registration is developed based of solving the Laplace equation for electrostatic field. This approach is possible due to the fact that the transformation which corresponds to the deformation gradient field lines of the electrostatic field, which is obtained by solving the Laplace equation satisfies all essential features that should have the registration transformation. These properties are related to the smoothness of the deformation field, the existence of an inverse function of the prohibition of crossing the line field. The procedure for solving the above equation and determining the required transformation was conducted using finite element method with use of a formulation of minimum energy of the system. The motivation for this thesis was consideration problem of evaluation mechanical properties of tissues affected aortic aneurysm. The paper describes the implementation and operation of the system that was used to characterize the mechanical properties of the aorta, which as output data provides information about a set of deformation points on the tissue surface as well as the values of applied fluid pressure. Strains at the certain moment of time were estimated using the image segmentation method and edges extraction, and finally image registration is applied. Using strain values in the mechanical model of tissue, and genetic algorithm as optimization technique, the Young's modulus is assessment. Gait analysis based on the images data is also one of the challenges in non-invasive diagnosis and monitoring of both diagnosed patients and healthy subjects.. This thesis presents a method for determining the mechanical stress of the cartilage using the camera image, and the values of the normal ground reaction force, which is generated during the walk, for assessment of cartilage deformation algorithms were used image registration of images obtained from the camera and the images obtained by computed tomography. Mechanical parameters of cartilage (Young's modulus and Poisson's ratio) are evaluated in the optimization process

Recalage et analyse d'un couple d'images (application aux mammographies)

Dans le monde de la recherche, l analyse du signal et plus particulièrement d image, est un domaine très actif, de par la variété des applications existantes, avec des problématiques telles que la compression de données, la vidéo-surveillance ou encore l analyse d images médicales pour ne prendre que quelques exemples. Le mémoire s inscrit dans ce dernier domaine particulièrement actif. Le nombre d appareils d acquisition existant ainsi que le nombre de clichés réalisés, entraînent la production d une masse importante d informations à traiter par les praticiens. Ces derniers peuvent aujourd hui être assistés par l outil informatique. Dans cette thèse, l objectif est l élaboration d un système d aide au diagnostic, fondé sur l analyse conjointe, et donc la comparaison d images médicales. Notre approche permet de détecter des évolutions, ou des tissus aberrants dans un ensemble donné, plutôt que de tenter de caractériser, avec un très fort a priori, le type de tissu cherché.Cette problématique permet d appréhender un aspect de l analyse du dossier médical d un patient effectuée par les experts qui est l étude d un dossier à travers le suivi des évolutions. Cette tâche n est pas aisée à automatiser. L œil humain effectue quasi-automatiquement des traitements qu il faut reproduire. Avant de comparer des régions présentes sur deux images, il faut déterminer où se situent ces zones dans les clichés. Toute comparaison automatisée de signaux nécessite une phase de recalage, un alignement des composantes présentes sur les clichés afin qu elles occupent la même position sur les deux images. Cette opération ne permet pas, dans le cadre d images médicales, d obtenir un alignement parfait des tissus en tous points, elle ne peut que minimiser les écarts entre tissus. La projection d une réalité 3D sur une image 2D entraîne des différences liées à l orientation de la prise de vue, et ne permet pas d analyser une paire de clichés par une simple différence entre images. Différentes structurations des clichés ainsi que différents champs de déformation sont ici élaborés afin de recaler les images de manière efficace.Après avoir minimisé les différences entre les positions sur les clichés, l analyse de l évolution des tissus n est pas menée au niveau des pixels, mais à celui des tissus eux-mêmes, comme le ferait un praticien. Afin de traiter les clichés en suivant cette logique, les images numériques sont réinterprétées, non plus en pixels de différentes luminosités, mais en motifs représentatifs de l ensemble de l image, permettant une nouvelle décomposition des clichés, une décomposition parcimonieuse. L atout d une telle représentation est qu elle permet de mettre en lumière un autre aspect du signal, et d analyser sous un angle nouveau, les informations nécessaires à l aide au diagnostic.Cette thèse a été effectuée au sein du laboratoire LIPADE de l Université Paris Descartes (équipe SIP, spécialisée en analyse d images) en collaboration avec la Société Fenics (concepteur de stations d aide au diagnostic pour l analyse de mammographies) dans le cadre d un contrat Cifre.In the scientific world, signal analysis and especially image analysis is a very active area, due to the variety of existing applications, with issues such as file compression, video surveillance or medical image analysis. This last area is particularly active. The number of existing devices and the number of pictures taken, cause the production of a large amount of information to be processed by practitioners. They can now be assisted by computers.In this thesis, the problem addressed is the development of a computer diagnostic aided system based on conjoint analysis, and therefore on the comparison of medical images. This approach allows to look for evolutions or aberrant tissues in a given set, rather than attempting to characterize, with a strong a priori, the type of fabric sought.This problem allows to apprehend an aspect of the analysis of medical file performed by experts which is the study of a case through the comparison of evolutions.This task is not easy to automate. The human eye performs quasi-automatically treatments that we need to replicate.Before comparing some region on the two images, we need to determine where this area is located on both pictures. Any automated comparison of signals requires a registration phase, an alignment of components present on the pictures, so that they occupy the same space on the two images. Although the characteristics of the processed images allow the development of a smart registration, the projection of a 3D reality onto a 2D image causes differences due to the orientation of the tissues observed, and will not allow to analyze a pair of shots with a simple difference between images. Different structuring of the pictures and different deformation fields are developed here to efficiently address the registration problem.After having minimized the differences on the pictures, the analysis of tissues evolution is not performed at pixels level, but the tissues themselves, as will an expert. To process the images in this logic, they will be reinterpreted, not as pixels of different brightness, but as patterns representative of the entire image, enabling a new decomposition of the pictures. The advantage of such a representation is that it allows to highlight another aspect of the signal, and analyze under a new perspective the information necessary to the diagnosis aid.This thesis has been carried out in the LIPADE laboratory of University Paris Descartes (SIP team, specialized in image analysis) and in collaboration with the Society Fenics (designer of diagnosis aid stations in the analysis of mammograms) under a Cifre convention. The convergence of the research fields of those teams led to the development of this document.PARIS5-Bibliotheque electronique (751069902) / SudocSudocFranceF