Variational and Shape Prior-based Level Set Model for Image Segmentation

International audienceA new image segmentation model based on level sets approach is presented herein. We deal with radiographic medical images where boundaries are not salient, and objects of interest have the same gray level as other structures in the image. Thus, an a priori information about the shape we look for is integrated in the level set evolution for good segmentation results. The proposed model also accounts a penalization term that forces the level set to be close to a signed distance function (SDF), which then avoids the re-initialization procedure. In addition, a variant and complete Mumford-Shah model is used in our functional; the added Hausdorff measure helps to better handle zones where boundaries are occluded or not salient. Finally, a weighted area term is added to the functional to make the level set drive rapidly to object's boundaries. The segmentation model is formulated in a variational framework, which, thanks to calculus of variations, yields to partial differential equations (PDEs) to guide the level set evolution. Results obtained on both synthetic and digital radiographs reconstruction (DRR) show that the proposed model improves on existing prior and non-prior shape based image segmentation

Modèles AM-FM et Approche par Équations aux Dérivées Partielles de la Décomposition Modale Empirique pour l'Analyse des Signaux et des Images

This work is addressed to signal and image analysis based on the empirical mode decomposition (EMD) and AM-FM models. In the first part of the thesis, a theoretical study of the 1D and 2D EMD is proposed. To do that, upper and lower envelopes are locally modeled with continuous operators. The local mean of envelopes is then computed in a more convenient way, such that the 1D and 2D sifting iterations are now well approximated by the resolutions of well posed partial differential equations (PDE). In addition, we provide theoretical justifications and carry out analytical characterizations of 1D and 2D empirical modes. This theoretical work enlightens important aspects of the EMD that were stated only in an intuitive way or based on controlled numerical simulations. In that way, we provide mathematical contributions on the method, which is originally defined just as an algorithm, and for which the main criticism remains the lack of a theoretical framework. Finally, new EMD algorithms are proposed and associated PDE are numerically solved, both in 1D and 2D. Our PDE-based approaches are illustrated with various signal and image examples. The second part deals with AM-FM image modeling. AM-FM models decompose images into components. In one hand, we have the local texture contrast (AM). On the other, we have the image geometry (FM). We first propose some improvements in wideband image demodulation techniques. Secondly, image demodulation based on 2D higher order differential energy operators (2D HODEO) are explored and better image demodulation algorithms are introduced. A practical application to sonar images' segmentation is also proposed. Our approaches are illustrated with various images and results are compared to the DESA (Discrete Energy Separation Algorithm) and the analytical image-based approach.Le travail de thèse traite de l'analyse des signaux et des images par décomposition modale empirique (EMD) et par modèles AM-FM. Dans la première partie de cette thèse, nous apportons des cadres théoriques à l'EMD 1D et 2D. Nous approchons localement les enveloppes supérieures et inférieures, dans le processus de tamisage de l'EMD, par des opérateurs continus. Par suite, nous formulons différemment la moyenne locale et prouvons que les itérations du tamisage 1D et 2D peuvent être approchées par des équations aux dérivées partielles (EDP) bien posées. Nous apportons des justifications théoriques et proposons des caractérisations analytiques des modes empiriques 1D et 2D. Ce travail a permis d'éclaircir de nombreux points et notions relatifs à l'EMD, et définis en 1D comme en 2D, que de manière très intuitive ou sur la base de simulations numériques contrôlées. Nous apportons de la sorte des contributions théoriques à l'EMD 1D et 2D, initialement définie par un algorithme et dont la principale limite est le manque de cadre théorique. Enfin, nous proposons de nouveaux algorithmes EMD 1D et 2D, et résolvons numériquement les EDP proposées en 1D et 2D. Nous illustrons nos approches par EDP sur de nombreux signaux et images. Dans la seconde partie, nous étudions les modèles AM-FM pour l'analyse d'images. Ces modèles se basent sur une décomposition des images en composantes regroupant les niveaux de gris des parties texturées (AM), d'une part, et une partie contenant la géométrie de l'image (FM), d'autre part. Nous proposons d'abord une amélioration de la démodulation d'images large bande. Dans un deuxième temps, nous explorons la démodulation d'images avec les opérateurs de Teager-Kaiser d'ordres supérieurs (HODEO 2D), en proposant de meilleurs algorithmes de démodulation, basés sur les HODEO 2D. Nous proposons ensuite une application à la segmentation d'images sonar et illustrons nos approches sur de nombreuses images. Les résultats sont comparés à ceux obtenus avec l'algorithme DESA (Discrete Energy Separation Algorithm) et l'approche par image analytique

Spatially adaptive PDEs for robust image sharpening

ISBN: 978-146732533-2International audienceWe present here discrete and continuous partial differential equation (PDE) -based methods for image enhancement/ sharpening. Using more robust and spatially adaptive PDEs, multiscale morphological operators that account image features are introduced, and then, used to provide a discrete enhancement operator, thanks to the former Kramer and Bruckner filter. A novel PDE associated to the introduced enhancement operator is established in 2D. Both the discrete and PDE-based sharpening filters are illustrated on synthetic, binary and real images

Modèles AM-FM et approche par équations aux dérivées partielles de la décomposition modale empirique pour l'analyse des signaux et des images

Le travail de thèse traite de l'analyse des signaux et des images par décomposition modale empirique (EMD) et par modèles AM-FM. Dans la première partie de cette thèse, nous apportons des cadres théoriques à l'EMD 1D et 2D. Nous approchons localement les enveloppes supérieures et inférieures, dans le processus de tamisage de l'EMD, par des opérateurs continus. Par suite, nous formulons différemment la moyenne locale et prouvons que les itérations du tamisage 1D et 2D peuvent être approchées par des équations aux dérivées partielles (EDP) bien posées. Nous apportons des justifications théoriques et proposons des caractérisations analytiques des modes empiriques 1D et 2D. Ce travail a permis d'éclaircir de nombreux points et notions relatifs à l'EMD, et définis en 1D comme en 2D, que de manière très intuitive ou sur la base de simulations numériques contrôlées. Nous apportons de la sorte des contributions théoriques à l'EMD 1D et 2D, initialement définie par un algorithme et dont la principale limite est le manque de cadre théorique. Enfin, nous proposons de nouveaux algorithmes EMD 1D et 2D, et résolvons numériquement les EDP proposées en 1D et 2D. Nous illustrons nos approches par EDP sur de nombreux signaux et images. Dans la seconde partie, nous étudions les modèles AM-FM pour l'analyse d'images. Ces modèles se basent sur une décomposition des images en composantes regroupant les niveaux de gris des parties texturées (AM), d'une part, et une partie contenant la géométrie de l'image (FM), d'autre part. Nous proposons d'abord une amélioration de la démodulation d'images large bande. Dans un deuxième temps, nous explorons la démodulation d'images avec les opérateurs de Teager- Kaiser d'ordres supérieurs (HODEO 2D), en proposant de meilleurs algorithmes de démodulation, basés sur les HODEO 2D. Nous proposons ensuite une application à la segmentation d'images sonar et illustrons nos approches sur de nombreuses images. Les résultats sont comparés à ceux obtenus avec l'algorithme DESA (Discrete Energy Separation Algorithm) et l'approche par image analytique.This work is addressed to signal and image analysis based on the empirical mode decomposition (EMD) and AM-FM models. In the first part of the thesis, a theoretical study of the 1D and 2D EMD is proposed. To do that, upper and lower envelopes are locally modeled with continuous operators. The local mean of envelopes is then computed in a more convenient way, such that the 1D and 2D sifting iterations are now well approximated by the resolutions of well posed partial differential equations (PDE). In addition, we provide theoretical justifications and carry out analytical characterizations of 1D and 2D empirical modes. This theoretical work enlightens important aspects of the EMD that were stated only in an intuitive way or based on controlled numerical simulations. In that way, we provide mathematical contributions on the method, which is originally defined just as an algorithm, and for which the main criticism remains the lack of a theoretical framework. Finally, new EMD algorithms are proposed and associated PDE are numerically solved, both in 1D and 2D. Our PDEbased approaches are illustrated with various signal and image examples. The second part deals with AM-FM image modeling. AM-FM models decompose images into components. In one hand, we have the local texture contrast (AM). On the other, we have the image geometry (FM). We first propose some improvements in wideband image demodulation techniques. Secondly, image demodulation based on 2D higher order differential energy operators (2D HODEO) are explored and better image demodulation algorithms are introduced. A practical application to sonar images' segmentation is also proposed. Our approaches are illustrated with various images and results are compared to the DESA (Discrete Energy Separation Algorithm) and the analytical image-based approach. Keywords: Empirical Mode Decomposition, partial differential equations, harmonic analysis, Laplacian operator, amplitude modulation, frequency modulation, Teager-Kaiser energy operators, Hilbert transform.RENNES1-BU Sciences Philo (352382102) / SudocSudocFranceF

Bi-planar image segmentation based on variational geometrical active contours with shape priors

International audienceThis work proposes an image segmentation model based on active contours. For a better handling of regions where anatomical structures are poorly contrasted and/or missing, we propose to incorporate a priori shape information in a variational formulation. Based on a level set approach, the proposed functional is composed of four terms. The first one makes the level set keep the important signed distance function property, which is necessary to guarantee the good level set evolution. Doing so results in avoiding the classical re-initialization process, contrary to most existing works where a partial differential equation is used instead. The second energy term contains the a priori information about admissible shapes of the target object, the latter being integrated in the level set evolution. An energy that drives rapidly the level set towards objects of interest is defined in the third term. A last term is defined on prior shapes thanks to a complete and modified Mumford-Shah model. The segmentation model is derived by solving the Euler-Lagrange equations associated to the functional minimization. Efficiency and robustness of our segmentation model are validated on synthetic images, digitally reconstructed images, and real image radiographs. Quantitative evaluations of segmentation results are also provided, which also show the importance of prior shapes in the context of image segmentation

MULTISCALE IMAGE ANALYSIS BASED ON ROBUST AND ADAPTIVE MORPHOLOGICAL SCALE-SPACES

Mathematical morphology is a powerful tool for image analysis; however, classical morphological operators suffer from lacks of robustness against noise, and also intrinsic image features are not accounted at all in the process. We propose in this work a new and different way to overcome such limits, by introducing both robustness and locally adaptability in morphological operators, which are now defined in a manner such that intrinsic image features are accounted. Dealing with partial differential equations (PDEs) for generalized Cauchy problems, we show that proposed PDEs are equivalent to impose robustness and adaptability of corresponding sup-inf operators, to structuring functions. Accurate numerical schemes are also provided to solve proposed PDEs, and experiments conducted for both synthetic and real images, show the efficiency and robustness of our approach

Emergence of Crimean–Congo Hemorrhagic Fever Virus in Eastern Senegal in 2022

Crimean–Congo hemorrhagic fever (CCHF), the most widespread tick-borne viral human infection, poses a threat to global health. In this study, clinical samples collected through national surveillance systems were screened for acute CCHF virus (CCHFV) infection using RT-PCR and for exposure using ELISA. For any CCHF-positive sample, livestock and tick samples were also collected in the neighborhood of the confirmed case and tested using ELISA and RT-PCR, respectively. Genome sequencing and phylogenetic analyses were also performed on samples with positive RT-PCR results. In Eastern Senegal, two human cases and one Hyalomma tick positive for CCHF were identified and a seroprevalence in livestock ranging from 9.33% to 45.26% was detected. Phylogenetic analyses revealed that the human strain belonged to genotype I based on the available L segment. However, the tick strain showed a reassortant profile, with the L and M segments belonging to genotype I and the S segment belonging to genotype III. Our data also showed that our strains clustered with strains isolated in different countries, including Mauritania. Therefore, our findings confirmed the high genetic variability inside the CCHF genotypes and their introduction to Senegal from other countries. They also indicate an increasing CCHF threat in Senegal and emphasize the need to reinforce surveillance using a one-health approach

Human and Livestock Surveillance Revealed the Circulation of Rift Valley Fever Virus in Agnam, Northern Senegal, 2021

The mosquito-borne disease caused by the Rift Valley Fever Virus (RVFV) is a viral hemorrhagic fever that affects humans and animals. In 1987, RVFV emerged in Mauritania, which caused the first RVFV outbreak in West Africa. This outbreak was shortly followed by reported cases in humans and livestock in Senegal. Animal trade practices with neighboring Mauritania suggest northern regions of Senegal are at high risk for RVF. In this study, we aim to conduct a molecular and serological survey of RVFV in humans and livestock in Agnam (northeastern Senegal) by RT-PCR (reverse transcription real-time polymerase chain reaction) and ELISA (Enzyme-Linked Immunosorbent Assay), respectively. Of the two hundred fifty-five human sera, one (0.39%) tested RVFV IgM positive, while fifty-three (20.78%) tested positive for RVFV IgG. For animal monitoring, out of 30 sheep recorded and sampled over the study period, 20 (66.67%) showed seroconversion to RVFV IgG antibodies, notably during the rainy season. The presence of antibodies increased significantly with age in both groups (p < 0.05), as the force of RVF infection (FOI), increased by 16.05% per year for humans and by 80.4% per month for livestock sheep. This study supports the usefulness of setting up a One Health survey for RVF management

Crimean&ndash;Congo Hemorrhagic Fever Virus Survey in Humans, Ticks, and Livestock in Agnam (Northeastern Senegal) from February 2021 to March 2022

Crimean&ndash;Congo hemorrhagic fever virus (CCHFV) is widespread in Asia, Europe, and Africa. In Senegal, sporadic cases of CCHFV have been reported since 1960. Bordering Mauritania in northeastern Senegal, Agnam is an arid area in the region of Matam where CCHFV is endemic, which harbors a pastoralist community. Given the drought conditions of Agnam, inhabitants are in constant movement with their animals in search of pasture, which brings them into contact with pathogens such as arboviruses. To identify CCHFV in this area, we established a One Health site in order to analyze animal livestock, ticks and human samples collected over a one-year period by qRT-PCR and ELISA. Our analysis showed one (1/364) patient carried anti-CCHFV IgM and thirty-seven carried anti-CCHFV IgG (37/364). In livestock, anti-CCHFV IgG was detected in 13 (38.24%) of 34 sentinel sheep. The risk of CCHFV infection increased significatively with age in humans (p-value = 0.00117) and sheep (p-value = 1.18 &times; 10&minus;11). Additional risk factors for CCHFV infection in sheep were dry seasons (p-value = 0.004) and time of exposure (p-value = 0.007). Furthermore, we detected a total of three samples with CCHFV RNA within Rhipicephalus evertsi evertsi and Rhipicephalus guilhoni tick species. Our results highlighted the usefulness of a One Health survey of CCHFV in pastoral communities at risk of arboviruses