pôle SIS / Biological and Biomedical Signal and Image Processing

Pôle SIS (Signal, Images et Systèmes)

Pôle SIS (Signal, Images et Systèmes)

Biological and Biomedical Signal and Image Processing
Based on a solid background in modeling and signal processing, team members develop new signal processing methods adapted to medical demand in the field of audiology, cardiorespiratory coupling, electrocardiology.

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Signal and Image Processing

Biological and Biomedical Signal and Image Processing - NEWS

ANR-funded PhD Fellowship on biomedical signal processing available!

I- Biomedical Signal Processing The Biomedical Signal Processing activity is fully hosted by the Signal team where we share a common interest for signal processing tools. Based on a solid background in modeling and signal processing, the involved team members, Pr. H. Rix, Pr. O. Meste and Pr. V. Zarzoso, have succeeded in developing new signal processing methods adapted to medical demand. The modeling of physiological process is also under the scope of this activity. Below are described the biomedical applications studied during the period under consideration, along with the corresponding theoretical research. It is worth noticing that the team’s broad knowledge in this field is currently being focused on electrocardiography (ECG), fostered by a very promising and solid collaboration with the Cardiology Department of Monaco’s Princess Grace Hospital (CHPG).

Audiology
This research concerns the design and the best restitution strategy to be implemented in cochlear implants based on time-frequency analysis. Beamforming techniques with dual microphones have been studied in order to reduce noise and artifacts. The tinitus phenomenon has been studied in cooperation with the team of Pr. Y. CAZALS (Marseille) by analyzing the ensemble spontaneous activity alterations [IC-64S. Boudaoud, H. Rix, O. Meste, and Y. Cazals. Ensemble spontaneous activity alterations detected by CISA approach. In IEEE Engineering in Medicine and Biology Society (EMBS’07), Lyon, France, 2007. 4 pages.] with shape-variability approaches.

Cardiorespiratory coupling and heart rhythm during exercise
Jointly with Dr. S. Bermon (IM2S Monaco) and G. Blain (MCF Univ. Lille), the interaction between the autonomous nervous system and the heart rhythm has been analysed, with emphasis on the influence of the respiratory system. This relation has been accurately quantified during intense exercise where it has been shown that the heart rhythm is not only mechanically and neurally modulated by respiration but also by the locomotor activity[IJ-11G. Blain, O. Meste, A. Blain, and S. Bermon. Time-frequency analysis of heart rate variability reveals cardiolocomotor coupling during dynamic cycling exercise in humans. Am. J. Physiol., 296(5):H1651–9, 2009.
http://hal.archives-ouvertes.fr/hal-00430401/en/.]. Intense exercise provides a good experimental condition enriching the basal inputs of the cardiac system. This property has been advantageously exploited to develop a new relation linking the PR and the RR intervals[IJ-20A. Cabasson, O. Meste, G. Blain, and S. Bermon. Quantifying the PR interval pattern during dynamic exercise and recovery. IEEE Trans. Biomed. Eng., 56(11):2675–2683, 2009.], revealing for the first time a hysteresis behavior. This finding has been developed in addition to more theoretical studies in time delay estimation[IJ-19A. Cabasson and O. Meste. Time Delay Estimation: A New Insight Into the Woody’s Method. IEEE Signal Processing Letters, 15:573–576, 2008.
http://hal.archives-ouvertes.fr/hal-00357360/en/.].

Electrocardiology
Research into this very general field has focused on the analysis of signals recorded by small (standard 12 leads) or large (Body Surface Potential Maps : BSPM) sets of sensors located on the body surface. Standard 12 leads systems have been involved in P-wave shapes clustering for obstructive sleep apnoea detection [IJ-16S. Boudaoud, H. Rix, O. Meste, C. Heneghan, and C. O’Brien. Corrected integral shape averaging applied to obstructive sleep apnea detection from the electrocardiogram. EURASIP Journal on Advances in Signal Processing, Special issue on advances in Electrocardiograms signal processing and analysis, 2007. Article ID 32570, 12 pages.]. This lead system has also been the data generator of very intensive research on atrial fibrillation (AF), the most prevalent cardiac arrhythmia encountered in clinical practice. AF has been analyzed after the application of specific blind source separation (BSS) techniques for atrial signal extraction developed in the group [IJ-102R. Phlypo, V. Zarzoso, and I. Lemahieu. Source extraction by maximizing the variance in the conditional distribution tails. IEEE Transactions on Signal Processing, 58(1):305-316, 2010., IJ-101R. Phlypo, V. Zarzoso, and I. Lemahieu. Atrial activity estimation from atrial fibrillation ECGs by blind source extraction based on a conditional maximum likelihood approach. Medical & Biological Engineering & Computing, 48(5):483-488, 2010., IJ-124V. Zarzoso and P. Comon. Robust independent component analysis. IEEE Trans. Neural Networks, 21(2):248-261, 2010.]. A non-invasive quantitative characterization of the spatio-temporal organization of AF has been successfully achieved using BSPM recordings and data analysis techniques inspired on the BSS model [IJ-14P. Bonizzi, M. Guillem, A. Climent, J. Millet, V. Zarzoso, F. Castells, and O. Meste. Noninvasive assessment of the complexity and stationarity of the atrial wavefront patterns during atrial fibrillation. IEEE Trans. Biomed. Eng., To appear in 2010.]. The flexibility of the BSPM has been also illustrated by applying original shape analysis for the characterisation of the ECG spatial variability of patient suffering from myocardial infarction [Th-11S. Boudaoud. Analyse de la variabilité de forme des signaux : Applications aux signaux électrophysiologiques.
PhD thesis, Université de Nice-Sophia Antipolis, 2006., NJ-3M. Fereniec, R. Maniewski, G. Karpinski, G. Opolski, and H. Rix. High resolution multichannel measurement and analysis of cardiac repolarization. Biocybernetics and Biomedical Engineering, 28(3):61–69, 2008.].

Theoretical research
As mentioned above, the Biomedical Signal Processing activity does not only focus on providing the best solutions to meet the medical or physiological demand but also on designing novel general-purpose signal processing tools. This complementary activity has yielded theoretical results in the fields of :

II- Biological images by Confocal Laser Scanning Microscopy (CLSM) Since a few years, more and more imagery systems are available for biological specimen imaging. Among them, Confocal Laser Scanning Microscopy (CLSM) is a powerful system allowing visualizing 3D life biological specimen at resolution of hundreds of nanometers. The diffraction-limited nature of the optical system, and the reduced amount of light detected by the photomultiplier, cause blur and photon counting noise respectively. Modelling and estimating these degradations are important (1) in order to build numerical image restoration and (2) to take into account these degradations for detecting thin structures in the cell.

We have obtained results on image deconvolution methods in presence of Poisson noise by developing regularized algorithms using TV regularization [IJ-43N. Dey, L. Blanc-Féraud, C. Zimmer, Z. Kam, P. Roux, J. Olivo-Marin, and J. Zerubia. Richardson-Lucy algorithm with total variation regularization for 3d confocal microscope deconvolution. Microscopy Research Technique, 69:260–266, 2006.]. In fluorescence microscopy, often the degradation varies with the imaging conditions. Blind restoration approaches tackle this (much more difficult and realistic) situation where the degradation is unknown [BC-8L. Blanc-Féraud, L. Mugnier, and A. Jalobeanu. Blind image deconvolution. In L. . J.W. series DSIP, Ed. ISTE and N. Y. Sons, editors, Inverse Problems in Vision and 3D Tomography, pages 97–121. 2010.]. Blind deconvolution is an ill-posed underdetermined problem. For thin specimen imaging, an alternate minimization (AM) approach has been proposed within a Bayesian framework, restoring the lost frequencies beyond the diffraction limit by using regularization on the object and a constraint on the point spread function (PSF) [IC-257P. Pankajakshan, B. Zhang, L. Blanc-Féraud, Z. Kam, J. Olivo-Marin, and J. Zerubia. Blind deconvolution for diffraction-limited fluorescence microscopy. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), pages 740–743, Paris, France, 2008., IJ-89P. Pankajakshan, B. Zhang, L. Blanc-Féraud, Z. Kam, J. Olivo-Marin, and J. Zerubia. Blind deconvoltion for thin layered confocal imaging. Applied Optics, 48(22):4437–4448, 2009.]. This work was done partly through the P2R Franco-Israeli project (2005-2009), in collaboration with Pasteur Institute, Weizmann Institute (Israel), Technion (Israel), and partly during a PhD funded by a CORDI Fellowship (INRIA), and now through ANR Defis DIAMOND on "Deconvolution of Augmented Images in Multi-Dimensional Optical Microscopy" started on December 2009.

As indicated in the previous paragraph thin structure detection is important for biologists (as for example filament (actin filaments in the cell) or points (nucleus or viral particles)). These elements are under the resolution limit of the microscope and can be modeled as structures of codimension higher or equal to two in 3D images. We have proposed new approaches to detect these thin structures, taking into account the blur, and using smooth approximations and Gamma-convergence of functional for point detection [IC-146D. Graziani, L. Blanc-Féraud, and G. Aubert. A new variational method to detect points in biological images. In IEEE International Symposium on Biomedical Imaging (ISBI’09), Boston, USA, 2009. 4 pages., Re-2D. Graziani, L. Blanc-Féraud, and G. Aubert. A formal gamma-convergence approach for the detection of points in 2-d images. Research Report 7038, INRIA, 2009.
http://hal.archives-ouvertes.fr/inria-00418526.], or by using vector fields to model these thin structures and using Ginzburg- Landau functional for completion [IC-28A. Baudour, G. Aubert, and L. Blanc-Féraud. Detection and completion of filaments: A vector field and pde approach. In SSVM 2007, LNCS 4485 proceedings, pages 451–460, 2007., NC-4A. Baudour, G. Aubert, and L. Blanc-Féraud. Détection et complétion de filaments: une approche variationelle et vectorielle. In Colloque Groupe de Recherche et d’Etudes du Traitement du Signal et des Images (GRETSI), Troyes, France, 2007. 4 pages., Th-4A. Baudour. Détection de Filaments dans des images 2D et 3D; modélisation, étude mathématique et algorithmes.
PhD thesis, Université de Nice Sophia Antipolis, 2009.]. This work has been done in collaboration with Dieudonné laboratory and through the ANR Blanche DETECFINE (2006 - 2010), in collaboration with Pasteur Institute and Sagem DS.

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