An Empirical Model Of Brain Shape

. A method is presented to systematically encode brain shape variation, observed from actual image samples, in the form of empirical distributions that can be applied to guide the Bayesian analysis of future image studies. Unlike eigendecompositions based on intrinsic features of a physical model, our modal basis for describing anatomic variation is derived directly from spatial mappings which bring previous brain samples into alignment with a reference configuration. The resultant representation ensures parsimony, yet captures information about the variation across the entire volumetric extent of the brain samples, and facilitates analyses that are governed by the measured statistics of anatomic variability rather than by the physics of some assumed model. Key words: Bayesian image analysis, shape models, cerebral anatomy 1. Introduction An understanding of the natural variation in human neuroanatomy is of fundamental clinical and scientific interest. In the interpretation of medica..

Inter- and intra-individual data fusion in medical imagning applied to the anatomical modeling of the human brain

Many research efforts in 3D medical imaging have been directed towards the definition of efficient and fast display and processing tools . Some very promising results are already available allowing a better access and a better use of the contents of medical images . The objective of this paper is to extend the use of existing data fusion methods toward their application in medicine (radiation therapy, epilepsy surgery, conventional neurosurgery etc.). Data fusion facilitates a better use of 3D image data by providing methods for the fusion of data from multiple modalities e.g ., multimodal registration and fusion between anatomical and functional data, the fusion of data from different patients or with a priori knowledge (models and/or atlases) and the recognition of complex anatomical structures and their symbolic identifications, when they are not explicitly described by the image contents. Three aspects of data fusion are considered in this paper with a particular emphasis on brain imaging. The first one concerns the combination of images and/or generic data; specifically, methods for multimodality registration and matching of data from different individuals by means of warping models . The second aspect concerns the identification of anatomical structures . Finally, the paper presents a state of the art 3D display technique to render the combined data. Perspectives are presented concerning the links between these numerical fusion procedures and their complementary symbolic procedures (data bases and knowledge representation systems).Beaucoup d'efforts de recherche en imagerie 3D médicale ont été dirigés vers la définition d'outils de traitement et de visualisation efficaces et rapides. Des résultats très encourageants sont disponibles aujourd'hui pour améliorer l'accès et l'utilisation médicale du contenu des images. Notre objectif dans ce papier est d'étendre le champ d'utilisation des méthodes de fusion de données à des fins d'applications précises (radiothérapie, chirurgie de l'epilepsie, neurochirurgie conventionnelle, etc.). L'amélioration de l'utilisation des données 3D passe par un effort de recherche plus poussé dans le domaine de la fusion de données. Cela concerne notamment la comparaison d'informations multi-capteurs (fusion multi-modalités, fusion d'informations anatomo-fonctionnelles,...), la fusion d'informations multi-patients ou venant de connaissances a priori (modèles) et enfin la reconnaissance de structures anatomiques complexes et leur identification symbolique, lorsqu'elles ne sont pas explicitement décrites par le contenu des images. Le problème de la fusion de données peut se traduire sous la forme i) d'une fusion de données multi-capteurs d'informations anatomiques et/ou fonctionnelles et ii) d'une fusion de données multi-individus qui, circonscrits au domaine cérébral, passe par l'utilisation de modèles d'anamorphose. Ces deux aspects mis bout à bout forment la trame méthodologique nécessaire à la modélisation anatomique des structures cérébrales. C'est dans ce cadre que se situent les travaux présentés dans ce papier. Le problème de fusion de données est abordé à la fois sous l'angle de la combinaison d'images et/ou de données génériques: problème du recalage multi-modalités et de la mise en correspondance de données entre individus (modèles de déformation appliqués au cerveau humain), sous l'angle de l'identification de structures anatomiques présentes sur les images (segmentation et étiquetage semantique) et enfin sous l'angle de la visualisation 3D des différentes informations. Des perspectives seront données pour ce qui concerne le lien entre ces procédures de fusion numériques et leurs compléments symboliques (bases de données et de connaissances

Probabilistic Matching Of Brain Images

. Image matching has emerged as an important area of investigation in medical image analysis. In particular, much attention has been focused on the atlas problem, in which a template representing the structural anatomy of the human brain is deformed to match anatomic brain images from a given individual. The problem is made difficult because there are important differences in both the gross and local morphology of the brain among normal individuals. We have formulated the image matching problem under a Bayesian framework. The Bayesian methodology facilitates a principled approach to the development of a matching model. Of special interest is its capacity to deal with uncertainty in the estimates, a potentially important but generally ignored aspect of the solution. In the construction of a reference system for the human brain, the Bayesian approach is well suited to the task of modeling variation in morphology. Statistical information about morphological variability, accumulated over p..

Matching Structural Images of the Human Brain Using Statistical and Geometrical Image Features

The efficacy of using intensity edges, curvature of iso-intensity contours, and tissue classified data for image matching are examined. The image matching problem is formulated in such a way that the different features are handled uniformly, allowing the same code to be used in each instance. The results using both simulated and real brain images indicate that each feature affected an improvement in the correspondence after matching with it. 1. INTRODUCTION Image registration has recently emerged as an important area of research in medical image processing. It facilitates the integration or fusion of images taken of the same subject but from different modalities. This in turn offers better diagnostic capability, improves surgical and therapy planning and evaluation, facilitates localization of function and the study of its cerebral organization, and enhances the information from modalities which when considered alone are difficult to interpret because of their inherently poor resolut..