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1. Active contour based on approximate entropy: application to cell membrane segmentation in confocal microscopy

Author

Histace, Aymeric, Bonnefoye, Elizabeth, Garrido, Luis, Matuszewski, Bogdan, Murphy, Mark, ICI, ASTRE, Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Image Processing Group, Barcelona Perceptual Computing lab (BCNPCL), Universitat de Barcelona (UB)-Universitat de Barcelona (UB), Advanced Digital and Signal Image Processing Group (ADSIP), University of Central Lancashire [Preston] (UCLAN), Liverpool John Morres University - LJMU (UK), Scitepress, ECSON, TERAFS, and Histace, Aymeric

Subjects
Confocal Microscopy, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Approximate Entropy, Image Segmentation, Active Contours, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Segmentation of cellular structures is of primary interest in cell imaging for cell shape reconstruction and to provide crucial information about possible cell morphology changes during radiotherapy for instance. From the particular perspective of predictive oncology, this paper reports on a novel method for membrane segmentation from single channel actin tagged fluorescence confocal microscopy images, which remains a challenging task. Proposed method is based on the use of the Approximate Entropy formerly introduced by Pincus embedded within a Geodesic Active Contour approach. Approximate Entropy can be seen as an estimator of the regularity of a particular sequence of values and, consequently, can be used as an edge detector. In this prospective study, a preliminary study on Approximate Entropy as an edge detector function is first proposed with a particular focus on the robustness to noise, and some promising membrane segmentation results obtained on confocal microscopy images are also shown.

Published
2014

2. Statistical region based active contour using a fractional entropy descriptor: Application to nuclei cell segmentation in confocal microscopy images

Author

Histace, Aymeric, Meziou, Leila, Matuszewski, Bogdan, Precioso, Frédéric, Murphy, Mark, Carreiras, Franck, ICI, Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Advanced Digital and Signal Image Processing Group (ADSIP), University of Central Lancashire [Preston] (UCLAN), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe KEIA, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), General Engineering Research Institute (GERI), Liverpool John Moore University, Equipe de recherche sur les relations matrice extracellulaire-cellules (ERRMECe), Fédération INSTITUT DES MATÉRIAUX DE CERGY-PONTOISE (I-MAT), Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine, TERAFS project, and Histace, Aymeric

Subjects
[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer Science::Computer Vision and Pattern Recognition, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; We propose an unsupervised statistical region based active contour approach integrating an original fractional entropy measure for image segmentation with a particular application to single channel actin tagged fluorescence confocal microscopy image segmentation. Following description of statistical based active contour segmentation and the mathematical definition of the proposed fractional entropy descriptor, we demonstrate comparative segmentation results between the proposed approach and standard Shannon's entropy on synthetic and natural images. We also show that the proposed unsupervised statistical based approach, integrating the fractional entropy measure, leads to very satisfactory segmentation of the cell nuclei from which shape characterization can be calculated.

Published
2013

3. Alpha-divergences pour la segmentation d'images par contours actifs basés histogrammes : Application à l'analyse d'images médicales et biomédicales

Author

Meziou, Leila, Histace, Aymeric, Precioso, Frédéric, ICI, Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe KEIA, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), TERAFS, BIOMICMAC, and Histace, Aymeric

Subjects
contours actifs, Segmentation, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], alpha-divergences, méthodes variationnelles, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

33 pages, soumis à la revue "Traitement du Signal"; Cet article présente une méthode de segmentation par contours actifs basés histogramme intégrant comme mesure de similarité la famille particulière des alpha-divergences. L'intérêt principal de cette méthode réside (i) dans la flexibilité des alpha-divergences dont la métrique intrinsèque peut-être paramétrisée via la valeur de alpha et donc adaptée aux distributions statistiques des régions de l'image à segmenter ; et (ii) dans la capacité unificatrice de cette mesure statistique vis-à-vis des distances classiquement utilisées dans ce contexte (Kullback- Leibler, Hellinger...). Nous abordons l'étude de cette mesure statistique tout d'abord d'un point de vue supervisé pour lequel le processus itératif de segmentation se déduit de la minimisation de l'alpha -divergence entre la densité de probabilité courante et une référence définie manuellement. Puis nous nous focalisons sur le point de vue non supervisé qui permet de se dédouaner de l'étape de définition des références par le biais d'une maximisation de distance entre les densités de probabilités intérieure et extérieure au contour. Par ailleurs, nous proposons une démarche d'optimisation de l'évolution du paramètre alpha conjointe au processus d'extrémisation de la divergence, permettant d'adapter itérativement la divergence à la statistique des données considérées. Au niveau expérimental, nous proposons une étude comparée des différentes approches de segmentations : en premier lieu, sur des images synthétiques bruitées et texturées, puis, sur des images naturelles. Enfin, nous focalisons notre étude sur différentes applications issues des domaines biomédicaux (microscopie confocale cellulaire) et médicaux (radiographie X) dans le contexte de l'aide au diagnotic. Dans chacun des cas, une discussion sur l'apport des alpha-divergences est proposée.

Published
2012

4. 3D Confocal Microscopy data analysis using level-set segmentation with alpha-divergence similarity measure

Author

Meziou, Leila, Histace, Aymeric, Precioso, Frédéric, Matuszewski, Bogdan, Carreiras, Franck, Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe KEIA, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Advanced Digital and Signal Image Processing Group (ADSIP), University of Central Lancashire [Preston] (UCLAN), Equipe de recherche sur les relations matrice extracellulaire-cellules (ERRMECe), Fédération INSTITUT DES MATÉRIAUX DE CERGY-PONTOISE (I-MAT), Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine, TERAFS, and Histace, Aymeric

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Segmentation of cellular structures is of primary interest in cell imaging for a 3D reconstruction of cell shape. Such an analysis provides crucial information about cell morphology and is instrumental in understanding of biological processes leading to development of a particular pathology. The work presented in this paper reports on a novel method for segmentation of cellular structures (nuclei and cell boundaries) from 3D single channel actin tagged fluorescence confocal microscopy images. The proposed segmentation method uses histogram-based image similarity measure in a level-set active-contour framework. The novelty of the method is in application of the alpha-divergence distance measure which can be seen as a generalization of classic Kullback-Leibler and Chi 2 measures. The resulting alpha-divergence level-set formulation leads to a single front evolution formula for both nuclei and cell boundaries segmentation, with no requirements for any enhancement or preprocessing of acquired cell images (a monolayer of human cells (PNT2) culture).

Published
2012

5. Fractional Entropy Based Active Contour Segmentation of Cell Nuclei in Actin-Tagged Confocal Microscopy Images

Author

Meziou, Leila, Histace, Aymeric, Precioso, Frederic, Matuszewski, Bogdan J, Carreiras, Franck, Histace, Aymeric, ICI, Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe KEIA, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Advanced Digital and Signal Image Processing Group (ADSIP), University of Central Lancashire [Preston] (UCLAN), Equipe de recherche sur les relations matrice extracellulaire-cellules (ERRMECe), Fédération INSTITUT DES MATÉRIAUX DE CERGY-PONTOISE (I-MAT), Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine-Université de Cergy Pontoise (UCP), Université Paris-Seine-Université Paris-Seine, EPSRC (UK), ENSEA (France), and TERAFS project

Subjects
I300, H200, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer Science::Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

Best Student Paper Award; International audience; In the framework of cell structure characterization for predictive oncology, we pro- pose in this paper an unsupervised statistical region based active contour approach in- tegrating an original fractional entropy measure for single channel actin tagged fluo- rescence confocal microscopy image segmentation. Following description of statistical based active contour segmentation and the mathematical definition of the proposed frac- tional entropy descriptor, we demonstrate comparative segmentation results between the proposed approach and standard Shannon's entropy obtained for nuclei segmentation. We show that the unsupervised proposed statistical based approach integrating the frac- tional entropy measure leads to very satisfactory segmentation of the cell nuclei from which shape characterization can be subsequently used for the therapy progress assess- ment.

Published
2012

6. Segmentation of Cellular Structures in Actin Tagged Fluorescence Confocal Microscopy Images

Author

Bogdan J. Matuszewski, Aymeric Histace, David R. Burton, Thomas E Marchant, Mark F. Murphy, Christopher J Moore, Frédéric Precioso, Advanced Digital and Signal Image Processing Group (ADSIP), University of Central Lancashire [Preston] (UCLAN), General Engineering Research Institute (GERI), Liverpool John Moore University, The Christie NHS Foundation Trust, University of Manchester [Manchester], Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Machine Learning and Information Retrieval (MALIRE), Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), and TERAFS project

Subjects
Physics, Active contour model, business.industry, 0206 medical engineering, 02 engineering and technology, Image segmentation, Iterative reconstruction, Cell morphology, 020601 biomedical engineering, law.invention, Level set, Confocal microscopy, law, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Microscopy, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Segmentation, Computer vision, Artificial intelligence, Biological system, business
Abstract

The paper reports on a novel method for reconstruction of cellular features including cell nuclei and cellular boundaries from actin tagged fluorescence confocal microscopy images. Such reconstruction can provide spatial context for subsequent quantitative analysis of changes to actin organisation and cell morphology in both controlled and stressed cell cultures. The proposed method is fully automatic and is formulated within active contour multiphase level set framework. The derived level set evolution PDEs combine previously proposed curvature and advection flows with propagation flow defined by specially designed set of geodesic distance maps. Additionally the proposed PDEs include additional components to impose known inclusion/exclusion topological constraints between cellular structures. The paper gives an overview of the proposed methodology as well as reports on initial results obtained for monolayer of human prostate cells (PNT2) culture visualised using acting tagged fluorescence confocal microscopy.

Published
2011

7. Confocal Microscopy Segmentation Using Active Contour Based on Alpha-Divergence

Author

Meziou, Leila, Histace, Aymeric, Precioso, Frédéric, Matuszewski, Bogdan, Murphy, Mark, Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Laboratoire d'Imagerie Paramétrique (LIP), Université Pierre et Marie Curie - Paris 6 (UPMC)-IFR58-Centre National de la Recherche Scientifique (CNRS), Advanced Digital and Signal Image Processing Group (ADSIP), University of Central Lancashire [Preston] (UCLAN), General Engineering Research Institute (GERI), Liverpool John Moore University, BIO MICMAC, TERAFS, and Histace, Aymeric

Subjects
[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Computer Science::Computer Vision and Pattern Recognition, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
Abstract

International audience; This paper describes a novel method for active contour segmentation based on foreground/background alpha-divergence histogram distance measure. In recent years a number of variational segmentation techniques have been proposed for a region based active contour segmentation utilising different distance measures between probability density functions (pdf) describing foreground and background regions. The most common techniques use Ki2, Hellinger/Bhattacharya distances or Kullback-Leibler divergence. In this paper, it is proposed to generalize these methods by using the alpha-divergences distance function. This distance function depending on the selected value of its parameter encompasses mentioned above classical distances. The paper defines a partial differential equation, associated with alpha-divergence variational criterion, that governs the iterative deformations of the active contour. The experimental results on a synthetic data demonstrate that the proposed method outperforms previously proposed histogram based methods in terms of segmentation accuracy and robustness with respect to type and level of noise. The potential of the proposed technique for segmentation of cellular structures in fluorescence confocal microscopy data is also illustrated.

Published
2011

8. Statistical Shape Model of Legendre Moments with Active Contour Evolution for Shape Detection and Segmentation

Author

Aymeric Histace, Frédéric Precioso, Yan Zhang, Bogdan J. Matuszewski, Histace, Aymeric, Springer, Advanced Digital and Signal Image Processing Group (ADSIP), University of Central Lancashire [Preston] (UCLAN), Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Machine Learning and Information Retrieval (MALIRE), Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), and TERAFS

Subjects
Active contour model, business.industry, Feature vector, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, 02 engineering and technology, Image segmentation, 030218 nuclear medicine & medical imaging, Active appearance model, 03 medical and health sciences, 0302 clinical medicine, Heat kernel signature, Point distribution model, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Active shape model, Computer Science::Computer Vision and Pattern Recognition, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, ComputingMethodologies_COMPUTERGRAPHICS, Mathematics, Shape analysis (digital geometry)
Abstract

International audience; This paper describes a novel method for shape detection and image segmentation. The proposed method combines statistical shape models and active contours implemented in a level set framework. The shape detection is achieved by minimizing the Gibbs energy of the posterior probability function. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. The proposed energy is minimized by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results are also presented to show that the proposed method has very robust performances for images with a large amount of noise.

Published
2011

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