Thierry Géraud

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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Deep neural networks for aberrations compensation in digital holographic imaging of the retina

By Julie Rivet, Guillaume Tochon, Serge Meimon, Michel Pâques, Thierry Géraud, Michael Atlan

2018-10-25

In Proceedings of the SPIE conference on adaptive optics and wavefront control for biological systems v

Abstract

In computational imaging by digital holography, lateral resolution of retinal images is limited to about 20 microns by the aberrations of the eye. To overcome this limitation, the aberrations have to be canceled. Digital aberration compensation can be performed by post-processing of full-field digital holograms. Aberration compensation was demonstrated from wavefront measurement by reconstruction of digital holograms in subapertures, and by measurement of a guide star hologram. Yet, these wavefront measurement methods have limited accuracy in practice. For holographic tomography of the human retina, image reconstruction was demonstrated by iterative digital aberration compensation, by minimization of the local entropy of speckle-averaged tomographic volumes. However image-based aberration compensation is time-consuming, preventing real-time image rendering. We are investigating a new digital aberration compensation scheme with a deep neural network to circumvent the limitations of these aberrations correction methods. To train the network, 28.000 anonymized images of eye fundus from patients of the 15-20 hospital in Paris have been collected, and synthetic interferograms have been reconstructed digitally by simulating the propagation of eye fundus images recorded with standard cameras. With a U-Net architecture, we demonstrate defocus correction of these complex-valued synthetic interferograms. Other aberration orders will be corrected with the same method, to improve lateral resolution up to the diffraction limit in digital holographic imaging of the retina.

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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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Document detection in videos captured by smartphones using a saliency-based method

By Minh Ôn Vũ Ngọc, Jonathan Fabrizio, Thierry Géraud

2018-09-20

In International conference on document analysis and recognition workshops (ICDARW)

Abstract

Smartphones are now widely used to digitizepaper documents. Document detection is the first importantstep of the digitization process. Whereas many methods extractlines from contours as candidates for the document boundary, we present in this paper a region-based approach. A key feature of our method is that it relies on visual saliency, using a recent distance existing in mathematical morphology. We show that the performance of our method is competitive with state-of-the-art methods on the ICDAR Smartdoc 2015 Competition dataset.

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Real-time document detection in smartphone videos

By Élodie Puybareau, Thierry Géraud

2018-05-10

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

Abstract

Smartphones are more and more used to capture photos of any kind of important documents in many different situations, yielding to new image processing needs. One of these is the ability of detecting documents in real time on smartphones’ video stream while being robust to classical defects such as low contrast, fuzzy images, flares, shadows, etc. This feature is interesting to help the user to capture his document in the best conditions and to guide this capture (evaluating appropriate distance, centering and tilt). In this paper we propose a solution to detect in real time documents taking very few assumptions concerning their contents and background. This method is based on morphological operators which contrasts with classical line detectors or gradient based thresholds. The use of such invariant operators makes our method robust to the defects encountered in video stream and suitable for real time document detection on smartphones.

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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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Segmentation des hyperintensités de la matière blanche en quelques secondes à l’aide d’un réseau de neurones convolutif et de transfert d’apprentissage

By Élodie Puybareau, Yongchao Xu, Joseph Chazalon, Isabelle Bloch, Thierry Géraud

2018-05-04

In Actes du congrès reconnaissance des formes, image, apprentissage et perception (RFIAP), session spéciale “deep learning, deep in france”

Abstract

Dans cet article, nous proposons une méthode automatique et rapide pour segmenter les hyper-intensités de la matière blanche (WMH) dans des images IRM cérébrales 3D, en utilisant un réseau de neurones entièrement convolutif (FCN) et du transfert d’apprentissage. Ce FCN est le réseau neuronal du Visual Geometry Group (VGG) pré-entraîné sur la base ImageNet pour la classification des images naturelles, et affiné avec l’ensemble des données d’entraînement du concours MICCAI WMH. Nous considérons trois images pour chaque coupe du volume à segmenter, provenant des acquisitions en T1, en FLAIR, et le résultat d’un opérateur morphologique appliqué sur le FLAIR, le top-hat, qui met en évidence les petites structures de forte intensité. Ces trois images 2D sont assemblées pour former une image 2D-3 canaux interprétée comme une image en couleurs, ensuite passée au FCN pour obtenir la segmentation 2D de la coupe correspondante. Nous traitons ainsi toutes les coupes pour former la segmentation de sortie 3D. Avec une telle technique, la segmentation de WMH sur un volume cérébral 3D prend environ 10 secondes, pré-traitement compris. Notre technique a été classée 6e sur 20 participants au concours MICCAI WMH.

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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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Parallel computation of component trees on distributed memory machines

By Markus Götz, Gabriele Cavallaro, Thierry Géraud, Matthias Book, Morris Riedel

2018-04-02

In IEEE Transactions on Parallel and Distributed Systems

Abstract

Component trees are region-based representations that encode the inclusion relationship of the threshold sets of an image. These representations are one of the most promising strategies for the analysis and the interpretation of spatial information of complex scenes as they allow the simple and efficient implementation of connected filters. This work proposes a new efficient hybrid algorithm for the parallel computation of two particular component trees—the max- and min-tree—in shared and distributed memory environments. For the node-local computation a modified version of the flooding-based algorithm of Salembier is employed. A novel tuple-based merging scheme allows to merge the acquired partial images into a globally correct view. Using the proposed approach a speed-up of up to 44.88 using 128 processing cores on eight-bit gray-scale images could be achieved. This is more than a five-fold increase over the state-of-the-art shared-memory algorithm, while also requiring only one-thirty-second of the memory.

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The challenge of cerebral magnetic resonance imaging in neonates: A new method using mathematical morphology for the segmentation of structures including diffuse excessive high signal intensities

By Yongchao Xu, Baptiste Morel, Sonia Dahdouh, Élodie Puybareau, Alessio Virzì, Hélène Urien, Thierry Géraud, Catherine Adamsbaum, Isabelle Bloch

2018-03-22

In Medical Image Analysis

Abstract

Preterm birth is a multifactorial condition associated with increased morbidity and mortality. Diffuse excessive high signal intensity (DEHSI) has been recently described on T2-weighted MR sequences in this population and thought to be associated with neuropathologies. To date, no robust and reproducible method to assess the presence of white matter hyperintensities has been developed, perhaps explaining the current controversy over their prognostic value. The aim of this paper is to propose a new semi-automated framework to detect DEHSI on neonatal brain MR images having a particular pattern due to the physiological lack of complete myelination of the white matter. A novel method for semi- automatic segmentation of neonatal brain structures and DEHSI, based on mathematical morphology and on max-tree representations of the images is thus described. It is a mandatory first step to identify and clinically assess homogeneous cohorts of neonates for DEHSI and/or volume of any other segmented structures. Implemented in a user-friendly interface, the method makes it straightforward to select relevant markers of structures to be segmented, and if needed, apply eventually manual corrections. This method responds to the increasing need for providing medical experts with semi-automatic tools for image analysis, and overcomes the limitations of visual analysis alone, prone to subjectivity and variability. Experimental results demonstrate that the method is accurate, with excellent reproducibility and with very few manual corrections needed. Although the method was intended initially for images acquired at 1.5T, which corresponds to usual clinical practice, preliminary results on images acquired at 3T suggest that the proposed approach can be generalized.

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