Gap Filling of 3-D Microvascular Networks by Tensor Voting

We present a new algorithm which merges discontinuities in 3-D images of tubular structures presenting undesirable gaps. The application of the proposed method is mainly associated to large 3-D images of microvascular networks. In order to recover the real network topology, we need to fill the gaps between the closest discontinuous vessels. The algorithm presented in this paper aims at achieving this goal. This algorithm is based on the skeletonization of the segmented network followed by a tensor voting method. It permits to merge the most common kinds of discontinuities found in microvascular networks. It is robust, easy to use, and relatively fast. The microvascular network images were obtained using synchrotron tomography imaging at the European Synchrotron Radiation Facility. These images exhibit samples of intracortical networks. Representative results are illustrated

Gibbs point field models for extraction problems in image analysis

International audienceThis paper is a review of some probabilistic methods, based on Gibbs fields theory, applied to solve image analysis tasks. We present the mathematical background and show different applications

Building detection in a single remotely sensed image with a point process of rectangles

International audienceIn this paper we introduce a probabilistic approach of building extraction in remotely sensed images. To cope with data heterogeneity we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature based modules. A global optimization process attempts to find the optimal configuration of buildings, considering simultaneously the observed data, prior knowledge, and interactions between the neighboring building parts. The proposed method is evaluated on various aerial image sets containing more than 500 buildings, and the results are matched against two state-of-the-art techniques

A 3D segmentation algorithm for ellipsoidal shapes. Application to nuclei extraction.

ISBN 978-989-8565-41-9International audienceWe propose some improvements of the Multiple Birth and Cut algorithm (MBC) in order to extract nuclei in 2D and 3D images. This algorithm based on marked point processes was proposed recently in (Gamal Eldin et al., 2012). We introduce a new contrast invariant energy that is robust to degradations encountered in fluorescence microscopy (e.g. local radiometry attenuations). Another contribution of this paper is a fast algorithm to determine whether two ellipses (2D) or ellipsoids (3D) intersect. Finally, we propose a new heuristic that strongly improves the convergence rates. The algorithm alternates between two birth steps. The first one consists in generating objects uniformly at random and the second one consists in perturbing the current configuration locally. Performance of this modified birth step is evaluated and examples on various image types show the wide applicability of the method in the field of bio-imaging

Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics

International audienceIn this paper we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. The accuracy is ensured by a Bayesian object model verification, meanwhile the computational cost is significantly decreased by a non-uniform stochastic object birth process, which proposes relevant objects with higher probability based on low-level image features

Improved RJMCMC point process sampler for object detection on images by simulated annealing

We first recall Geyer and Møller algorithm that allows to sample point processes using a Markov chain. We also recall Green's framework that allows to build samplers on general state spaces by imposing reversibility of the designed Markov chain.Since in our image processing applications, we are interested by sampling highly spatially correlated and non-invariant point processes, we adapt these ideas to improve the exploration ability of the algorithm. In particular, we keep the ability of generating points with non-uniform distributions, and design an updating scheme that allows to generate points in some neighborhood of other points. We first design updating schemes under Green's framework to keep (.) reversibility of the Markov chain and then show that stability properties are not loosed. Using a drift condition we prove that the Markov chain is geometrically ergodic and Harris recurrent.We finally show on experimental results that these kinds of updates are usefull and propose other improvements

An adaptive simulated annealing cooling schedule for object detection in images

In our image processing applications, we use a simulated annealing procedure to find configurations of geometric shapes that fit the best an image. This type of algorithm allows finding one of the global minima of an arbitrary function provided that the cooling schedule is logarithmic with the time. Since this type of cooling schedules is very slow, geometrical cooling schemes are used in practice. Geometrical schemes are however subject to some disadvantages that we discuss in this report. To overcome these disadvantages, we propose an adaptive cooling scheme. This heuristic is based on the analysis of the cooling scheme behavior in practice. In particular, we observe the presence of critical temperatures. To deal with these critical temperatures, we propose a cooling scheme that decelerates when such a temperature is detected, and accelerates otherwise. We present results on a real problem taken from our image processing applications

Optimization Techniques for Energy Minimization Problem in a Marked Point Process Application to Forestry

We use marked point processes to detect an unknown number of trees from high resolution aerial images. This approach turns to be an energy minimization problem, where the energy contains a prior term which takes into account the geometrical properties of the objects, and a data term to match these objects onto the image. This stochastic process is simulated via a Reversible Jump Markov Chain Monte Carlo procedure, which embeds a Simulated Annealing scheme to extract the best configuration of objects. We compare in this paper different cooling schedules of the Simulated Annealing algorithm which could provide some good minimization in a short time. We also study some adaptive proposition kernels

Brain Tumor Vascular Network Segmentation from Micro-Tomography

Micro-tomography produces high resolution images of bio- logical structures such as vascular networks. In this paper, we present a new approach for segmenting vascular network into pathological and normal regions from considering their micro-vessel 3D structure only. We define and use a condi- tional random field for segmenting the output of a watershed algorithm. The tumoral and normal classes are thus character- ized by their respective distribution of watershed region size interpreted as local vascular territories