Optimal Consensus set for nD Fixed Width Annulus Fitting

International audienceThis paper presents a method for fitting a nD fixed width spherical shell to a given set of nD points in an image in the presence of noise by maximizing the number of inliers, namely the consensus set. We present an algorithm, that provides the optimal solution(s) within a time complexity O(N n+1 log N) for dimension n, N being the number of points. Our algorithm guarantees optimal solution(s) and has lower complexity than previous known methods

Facteurs predictifs de l’efficacite ablative de l’iode 131 dans les cancers differencies de la thyroïde

La prise en charge des cancers différenciés de la thyroïde (CDT) comporte souvent une radiothérapie métabolique à l’iode131 (IRA-thérapie). Le but de ce travail est d’évaluer le caractère prédictif des différents éléments anatomopathologiques, de la classification pTNM et de la stadification pronostique sur l’activité ablative requise d’iode131. Notre travail est une étude analytique rétrospective portant sur 275 cas de CDT ayant subit une thyroïdectomie totale. Tous ces patients ont eu une ou plusieurs activités ablatives. Nous avons cherché – au moyen d’une analyse statistique par test de Khi2 ou test Anova – toute corrélation entre les éléments de l’examen anatomopathologique de la tumeur, la classe pTNM, le stade pronostique correspondant d’une part  et l’efficacité de l’irathérapie ablative d’autre part. Dans notre série, une activité ablative plus élevée est nécessaire lorsque la taille du foyer tumoral dépasse les 6 cm (p=0,012), en cas de dépassement de la graisse péri thyroïdienne (

Digital hyperplane fitting

International audienceThis paper addresses the hyperplane fitting problem of discrete points in any dimension (i.e. in Z d). For that purpose, we consider a digital model of hyperplane, namely digital hyperplane, and present a combinatorial approach to find the optimal solution of the fitting problem. This method consists in computing all possible digital hyperplanes from a set S of n points, then an exhaustive search enables us to find the optimal hyperplane that best fits S. The method has, however, a high complexity of O(n d), and thus can not be applied for big datasets. To overcome this limitation, we propose another method relying on the Delaunay triangulation of S. By not generating and verifying all possible digital hyperplanes but only those from the elements of the triangula-tion, this leads to a lower complexity of O(n d 2 +1). Experiments in 2D, 3D and 4D are shown to illustrate the efficiency of the proposed method

3D Modeling for Multimodal Visualization of Medical Data

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Analytical Description of Digital Circles

In this paper we propose an analytical description of different kinds of digital circles that appear in the literature and especially in digital circle recognition algorithms

Two Plane-Probing Algorithms for the Computation of the Normal Vector to a Digital Plane

International audienceDigital planes are sets of integer points located between two parallel planes. We present a new algorithm that computes the normal vector of a digital plane given only a predicate " is a point x in the digital plane or not ". In opposition to classical recognition algorithm, this algorithm decides on-the-fly which points to test in order to output at the end the exact surface characteristics of the plane. We present two variants: the H-algorithm, which is purely local, and the R-algorithm which probes further along rays coming out from the local neighborhood tested by the H-algorithm. Both algorithms are shown to output the correct normal to the digital planes if the starting point is a lower leaning point. The worst-case time complexity is in O(ω) for the H-algorithm and O(ω log ω) for the R-algorithm, where ω is the arithmetic thickness of the digital plane. In practice, the H-algorithm often outputs a reduced basis of the digital plane while the R-algorithm always returns a reduced basis. Both variants perform much better than the theoretical bound, with an average behavior close to O(log ω). Finally we show how this algorithm can be used to analyze the geometry of arbitrary digital surfaces, by computing normals and identifying convex, concave or saddle parts of the surface. This paper is an extension of [16]