Edwin Carlinet

Introducing multivariate connected openings and closings

By Edwin Carlinet, Thierry Géraud

2019-03-13

In Mathematical morphology and its application to signal and image processing – proceedings of the 14th international symposium on mathematical morphology (ISMM)

Abstract

The component trees provide a high-level, hierarchical, and contrast invariant representations of images, suitable for many image processing tasks. Yet their definition is ill-formed on multivariate data, e.g., color images, multi-modality images, multi-band images, and so on. Common workarounds such as marginal processing, or imposing a total order on data are not satisfactory and yield many problems, such as artifacts, loss of invariances, etc. In this paper, inspired by the way the Multivariate Tree of Shapes (MToS) has been defined, we propose a definition for a Multivariate min-tree or max-tree. We do not impose an arbitrary total ordering on values; we use only the inclusion relationship between components. As a straightforward consequence, we thus have a new class of multivariate connected openings and closings.

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Spherical fluorescent particle segmentation and tracking in 3D confocal microscopy

By Élodie Puybareau, Edwin Carlinet, Alessandro Benfenati, Hugues Talbot

2019-03-13

In Mathematical morphology and its application to signal and image processing – proceedings of the 14th international symposium on mathematical morphology (ISMM)

Abstract

Spherical fluorescent particle are micrometer-scale spherical beads used in various areas of physics, chemistry or biology as markers associated with local physical media. They are useful for example in fluid dynamics to characterize flows, diffusion coefficients, viscosity or temperature; they are used in cells dynamics to estimate mechanical strain and stress at the micrometer scale. In order to estimate these physical measurements, tracking these particles is necessary. Numerous approaches and existing packages, both open-source and proprietary are available to achieve tracking with a high degree of precision in 2D. However, little such software is available to achieve tracking in 3D. One major difficulty is that 3D confocal microscopy acquisition is not typically fast enough to assume that the beads are stationary during the whole 3D scan. As a result, beads may move between planar scans. Classical approaches to 3D segmentation may yield objects are not spherical. In this article, we propose a 3D bead segmentation that deals with this situation.

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Intervertebral disc segmentation using mathematical morphology—A CNN-free approach

By Edwin Carlinet, Thierry Géraud

2018-11-26

In Proceedings of the 5th MICCAI workshop & challenge on computational methods and clinical applications for spine imaging (CSI)

Abstract

In the context of the challenge of “automatic InterVertebral Disc (IVD) localization and segmentation from 3D multi-modality MR images” that took place at MICCAI 2018, we have proposed a segmentation method based on simple image processing operators. Most of these operators come from the mathematical morphology framework. Driven by some prior knowledge on IVDs (basic information about their shape and the distance between them), and on their contrast in the different modalities, we were able to segment correctly almost every IVD. The most interesting feature of our method is to rely on the morphological structure called the Three of Shapes, which is another way to represent the image contents. This structure arranges all the connected components of an image obtained by thresholding into a tree, where each node represents a particular region. Such structure is actually powerful and versatile for pattern recognition tasks in medical imaging.

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An image processing library in modern C++: Getting simplicity and efficiency with generic programming

By Michaël Roynard, Edwin Carlinet, Thierry Géraud

2018-10-25

In Proceedings of the 2nd workshop on reproducible research in pattern recognition (RRPR 2018)

Abstract

As there are as many clients as many usages of an Image Processing library, each one may expect different services from it. Some clients may look for efficient and production-quality algorithms, some may look for a large tool set, while others may look for extensibility and genericity to inter-operate with their own code base… but in most cases, they want a simple-to-use and stable product. For a C++ Image Processing library designer, it is difficult to conciliate genericity, efficiency and simplicity at the same time. Modern C++ (post 2011) brings new features for library developers that will help designing a software solution combining those three points. In this paper, we develop a method using these facilities to abstract the library components and augment the genericity of the algorithms. Furthermore, this method is not specific to image processing; it can be applied to any C++ scientific library.

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Left atrial segmentation in a few seconds using fully convolutional network and transfer learning

By Élodie Puybareau, Zhou Zhao, Younes Khoudli, Edwin Carlinet, Yongchao Xu, Jérôme Lacotte, Thierry Géraud

2018-10-25

In Proceedings of the workshop on statistical atlases and computational modelling of the heart (STACOM 2018), in conjunction with MICCAI

Abstract

In this paper, we propose a fast automatic method that segments left atrial cavity from 3D GE-MRIs without any manual assistance, using a fully convolutional network (FCN) and transfer learning. This FCN is the base network of VGG-16, pre-trained on ImageNet for natural image classification, and fine tuned with the training dataset of the MICCAI 2018 Atrial Segmentation Challenge. It relies on the “pseudo-3D” method published at ICIP 2017, which allows for segmenting objects from 2D color images which contain 3D information of MRI volumes. For each $n^{\text{th}}$ slice of the volume to segment, we consider three images, corresponding to the $(n-1)^{\text{th}}$, $n^{\text{th}}$, and $(n+1)^{\text{th}}$ slices of the original volume. These three gray-level 2D images are assembled to form a 2D RGB color image (one image per channel). This image is the input of the FCN to obtain a 2D segmentation of the $n^{\text{th}}$ slice. We process all slices, then stack the results to form the 3D output segmentation. With such a technique, the segmentation of the left atrial cavity on a 3D volume takes only a few seconds. We obtain a Dice score of 0.92 both on the training set in our experiments before the challenge, and on the test set of the challenge.

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The tree of shapes turned into a max-tree: A simple and efficient linear algorithm

By Edwin Carlinet, Thierry Géraud, Sébastien Crozet

2018-05-10

In Proceedings of the 24th IEEE international conference on image processing (ICIP)

Abstract

The Tree of Shapes (ToS) is a morphological tree-based representation of an image translating the inclusion of its level lines. It features many invariances to image changes, which makes it well-suited for a lot of applications in image processing and pattern recognition. In this paper, we propose a way of turning this algorithm into a Max-Tree computation. The latter has been widely studied, and many efficient algorithms (including parallel ones) have been developed. Furthermore, we develop a specific optimization to speed-up the common 2D case. It follows a simple and efficient algorithm, running in linear time with a low memory footprint, that outperforms other current algorithms. For Reproducible Research purpose, we distribute our code as free software.

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Un algorithme de complexité linéaire pour le calcul de l’arbre des formes

By Edwin Carlinet, Sébastien Crozet, Thierry Géraud

2018-05-04

In Actes du congrès reconnaissance des formes, image, apprentissage et perception (RFIAP)

Abstract

L’arbre des formes (AdF) est une représentation morpho- logique hiérarchique de l’image qui traduit l’inclusion des ses lignes de niveaux. Il se caractérise par son invariance à certains changement de l’image, ce qui fait de lui un outil idéal pour le développement d’applications de reconnaissance des formes. Dans cet article, nous proposons une méthode pour transformer sa construction en un calcul de Max-tree. Ce dernier a été largement étudié au cours des dernières années et des algorithmes efficaces (dont certains parallèles) existent déjà. Nous proposons également une optimisation qui permet d’accélérer son calcul dans le cas classique des images 2D. Il en découle un algorithme simple, efficace, s’exécutant linéairement en fonction du nombre de pixels, avec une faible empreinte mémoire, et qui surpasse les algorithmes à l’état de l’art.

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La pseudo-distance du dahu

By Edwin Carlinet, Yongchao Xu, Nicolas Boutry, Thierry Géraud

2017-03-21

In Actes d’ORASIS

Abstract

La distance de la barrière minimum est définie comme le plus petit intervalle de l’ensemble des niveaux de gris le long d’un chemin entre deux points dans une image. Pour cela, on considère que l’image est un graphe à valeurs sur les sommets. Cependant, cette définition ne correspond pas à l’interprétation d’une image comme étant une carte d’élévation, c’est-à-dire, un paysage continu d’une manière ou d’une autre. En se plaçant dans le cadre des fonctions multivoques, nous présentons une nouvelle définition pour cette distance. Cette définition, compatible avec l’interprétation paysagère, est dénuée de problèmes topologiques bien qu’en restant dans un monde discret. Nous montrons que la distance proposée est reliée à la structure morphologique d’arbre des formes, qui permet de surcroît un calcul rapide et exact de cette distance. Cela se démarque de sa définition classique, pour laquelle le seul calcul rapide n’est qu’approximatif.

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Introducing the Dahu pseudo-distance

By Thierry Géraud, Yongchao Xu, Edwin Carlinet, Nicolas Boutry

2017-02-23

In Mathematical morphology and its application to signal and image processing – proceedings of the 13th international symposium on mathematical morphology (ISMM)

Abstract

The minimum barrier (MB) distance is defined as the minimal interval of gray-level values in an image along a path between two points, where the image is considered as a vertex-valued graph. Yet this definition does not fit with the interpretation of an image as an elevation map, i.e. a somehow continuous landscape. In this paper, based on the discrete set-valued continuity setting, we present a new discrete definition for this distance, which is compatible with this interpretation, while being free from digital topology issues. Amazingly, we show that the proposed distance is related to the morphological tree of shapes, which in addition allows for a fast and exact computation of this distance. That contrasts with the classical definition of the MB distance, where its fast computation is only an approximation.

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Region-based classification of remote sensing images with the morphological tree of shapes

By Gabriele Cavallaro, Mauro Dalla Mura, Edwin Carlinet, Thierry Géraud, Nicola Falco, Jón Atli Benediktsson

2016-04-12

In Proceedings of the IEEE international geoscience and remote sensing symposium (IGARSS)

Abstract

Satellite image classification is a key task used in remote sensing for the automatic interpretation of a large amount of information. Today there exist many types of classification algorithms using advanced image processing methods enhancing the classification accuracy rate. One of the best state-of-the-art methods which improves significantly the classification of complex scenes relies on Self-Dual Attribute Profiles (SDAPs). In this approach, the underlying representation of an image is the Tree of Shapes, which encodes the inclusion of connected components of the image. The SDAP computes for each pixel a vector of attributes providing a local multiscale representation of the information and hence leading to a fine description of the local structures of the image. Instead of performing a pixel-wise classification on features extracted from the Tree of Shapes, it is proposed to directly classify its nodes. Extending a specific interactive segmentation algorithm enables it to deal with the multi-class classification problem. The method does not involve any statistical learning and it is based entirely on morphological information related to the tree. Consequently, a very simple and effective region-based classifier relying on basic attributes is presented.

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