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Publications about EM algorithm
Result of the query in the list of publications :
2 Conference articles |
1 - Point-spread function retrieval for fluorescence microscopy.
P. Pankajakshan and L. Blanc-Féraud and Z. Kam and J. Zerubia. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), Publ. IEEE, Org. IEEE, Boston, USA, June 2009.
Keywords : fluorescence microscopy, point spread function, EM algorithm, Deconvolution.
Copyright : Copyright 2009 IEEE. Published in the 2009 International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2009), scheduled for June 28 - July 1, 2009 in Boston, Massachusetts, U.S.A. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact: Manager, Copyrights and Permissions / IEEE Service Center / 445 Hoes Lane / P.O. Box 1331 / Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966.
@INPROCEEDINGS{ppankajakshan09a,
|
| author |
= |
{Pankajakshan, P. and Blanc-Féraud, L. and Kam, Z. and Zerubia, J.}, |
| title |
= |
{Point-spread function retrieval for fluorescence microscopy}, |
| year |
= |
{2009}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, |
| publisher |
= |
{IEEE}, |
| organization |
= |
{IEEE}, |
| address |
= |
{Boston, USA}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/39/55/34/PDF/pankajakshan.pdf}, |
| keyword |
= |
{fluorescence microscopy, point spread function, EM algorithm, Deconvolution} |
| } |
Abstract :
In this paper we propose a method for retrieving the Point-Spread Function (PSF) of an imaging system given the observed images of fluorescent microspheres. Theoretically calculated PSFs often lack the experimental or microscope specific signatures while empirically obtained data are either over sized or (and) too noisy. The effect of noise and the influence of the microsphere size can be mitigated from the experimental data by using a Maximum Likelihood Expectation Maximization (MLEM) algorithm. The true experimental parameters can then be estimated by fitting the result to a model based on the scalar diffraction theory. The algorithm was tested on some simulated data and the results obtained validate the usefulness of the approach for retrieving the PSF from measured data. |
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2 - Parametric blind deconvolution for confocal laser scanning microscopy.
P. Pankajakshan and B. Zhang and L. Blanc-Féraud and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. 29th International Conference of IEEE EMBS (EMBC-07), pages 6531-6534, August 2007.
Keywords : Confocal microscopy, Blind Deconvolution, Poisson noise, Total variation, EM algorithm, Bayesian estimation.
Copyright : ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
@INPROCEEDINGS{Pankajakshan07a,
|
| author |
= |
{Pankajakshan, P. and Zhang, B. and Blanc-Féraud, L. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{Parametric blind deconvolution for confocal laser scanning microscopy}, |
| year |
= |
{2007}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. 29th International Conference of IEEE EMBS (EMBC-07)}, |
| pages |
= |
{6531-6534}, |
| pdf |
= |
{http://ieeexplore.ieee.org/iel5/4352184/4352185/04353856.pdf?tp=&isnumber=&arnumber=4353856}, |
| keyword |
= |
{Confocal microscopy, Blind Deconvolution, Poisson noise, Total variation, EM algorithm, Bayesian estimation} |
| } |
Abstract :
In this paper, we propose a method for the
iterative restoration of fluorescence Confocal Laser Scanning
Microscopic (CLSM) images and parametric estimation of the
acquisition system’s Point Spread Function (PSF). The CLSM is
an optical fluorescence microscope that scans a specimen in 3D
and uses a pinhole to reject most of the out-of-focus light. However,
the quality of the images suffers from two basic physical
limitations. 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. These images
can hence benefit from post-processing restoration methods
based on deconvolution. An efficient method for parametric
blind image deconvolution involves the simultaneous estimation
of the specimen 3D distribution of fluorescent sources and
the microscope PSF. By using a model for the microscope
image acquisition physical process, we reduce the number of
free parameters describing the PSF and introduce constraints.
The parameters of the PSF may vary during the course of
experimentation, and so they have to be estimated directly from
the observed data. A priori model of the specimen is further
applied to stabilize the alternate minimization algorithm and to
converge to the solutions. |
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Technical and Research Report |
1 - A Deterministic Annealing PMHT Algorithm with an Application to Particle Tracking.
A. Strandlie and J. Zerubia. Research Report 3711, Inria, June 1999.
Keywords : EM algorithm, Particle tracking.
@TECHREPORT{jz99c,
|
| author |
= |
{Strandlie, A. and Zerubia, J.}, |
| title |
= |
{A Deterministic Annealing PMHT Algorithm with an Application to Particle Tracking}, |
| year |
= |
{1999}, |
| month |
= |
{June}, |
| institution |
= |
{Inria}, |
| type |
= |
{Research Report}, |
| number |
= |
{3711}, |
| url |
= |
{https://hal.inria.fr/inria-00072957}, |
| pdf |
= |
{https://hal.inria.fr/file/index/docid/72957/filename/RR-3711.pdf}, |
| ps |
= |
{https://hal.inria.fr/docs/00/07/29/57/PS/RR-3711.ps}, |
| keyword |
= |
{EM algorithm, Particle tracking} |
| } |
Résumé :
Nous considérons l'algorithme PMHT pour suivre la trajectoire de particules dans des détecteurs utilisés en physique des hautes énergies. Cet algorithme a récemment été développé pour suivre des cibles multiples dans un environneme- nt encombré. Il est fondé sur l'estimateur du maximum de vraisemblance, et s'appuie sur un algorithme de type EM. L'algorithme résultant correspond à l'utilisation en parallèle de plusieurs filtres de Kalman itératifs couplés. Il est proche de l'algorithme EA, mais il est de plus capable de prendre en compte le bruit associé au processus, comme par exemple la diffusion de Coulomb multiple. Dans ce rapport, nous présentons les propriétés classiques d'un tel algorithme et proposons une généralisation incluant un recuit déterministe. Nous proposons également plusieurs modificati- ons améliorant les performances de cet algorithme. En particulier, nous avons modifié les probabilités reliant les événements élémentaires aux trajectoires afin d'obtenir une compétition entre ces événements dans une même couche du détecteur. Enfin, nous présentons des résultats obtenus sur des simulations réalisées à partir du détecteur ATLAS (TRT). Nous considérons l'algorithme PMHT pour suivre la trajectoire de particules dans des détecteurs utilisés en physique des hautes énergies. Cet algorithme a récemment été développé pour suivre des cibles multiples dans un environneme- nt encombré. Il est fondé sur l'estimateur du maximum de vraisemblance, et s'appuie sur un algorithme de type EM. L'algorithme résultant correspond à l'utilisation en parallèle de plusieurs filtres de Kalman itératifs couplés. Il est proche de l'algorithme EA, mais il est de plus capable de prendre en compte le bruit associé au processus, comme par exemple la diffusion de Coulomb multiple. Dans ce rapport, nous présentons les propriétés classiques d'un tel algorithme et proposons une généralisation incluant un recuit déterministe. Nous proposons également plusieurs modificati- ons améliorant les performances de cet algorithme. En particulier, nous avons modifié les probabilités reliant les événements élémentaires aux trajectoires afin d'obtenir une compétition entre ces événements dans une même couche du détecteur. Enfin, nous présentons des résultats obtenus sur des simulations réalisées à partir du détecteur ATLAS (TRT). |
Abstract :
We introduce the Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm for particle tracking in high-energy physics detectors. This algorithm has been developed recently for tracking multiple targets in clutter, and it is based on maximum likelihood estimation by aid of the EM algorithm. The resulting algorithm basically consists of running several iterated and coupled Kalman filters and smoothers in parallel. It is similar to the Elastic Arms algorithm, but it possesses the additional feature of being able to take process noise into account, as for instance multiple Coulomb scattering. Herein, we review its basic properties and derive a generalized version of the algorithm by including a deterministic annealing scheme. Further developments of the algorithm in order to improve the performance are also discussed. In particular, we propose to modify the hit-to-track assignment probabilities in order to obtain competition between hits in the same detector layer. Finally, we present results of an implementat- ion of the algorithm on simulated tracks from the ATLAS Inner Detector Transition Radiation Tracker (TRT). We introduce the Probabilistic Multi-Hypot- hesis Tracking (PMHT) algorithm for particle tracking in high-energy physics detectors. This algorithm has been developed recently for tracking multiple targets in clutter, and it is based on maximum likelihood estimation by aid of the EM algorithm. The resulting algorithm basically consists of running several iterated and coupled Kalman filters and smoothers in parallel. It is similar to the Elastic Arms algorithm, but it possesses the additional feature of being able to take process noise into account, as for instance multiple Coulomb scattering. Herein, we review its basic properties and derive a generalized version of the algorithm by including a deterministic annealing scheme. Further developments of the algorithm in order to improve the performance are also discussed. In particular, we propose to modify the hit-to-track assignment probabilities in order to obtain competition between hits in the same detector layer. Finally, we present results of an implementation of the algorithm on simulated tracks from the ATLAS Inner Detector Transition Radiation Tracker (TRT). |
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