Détection de spots avec sélection d'échelle automatique et seuillage adaptatif en microscopie de fluorescence

En imagerie cellulaire, et plus particulierement en microscopie de fluorescence, la premiere phase de beaucoup d’analyses consiste a detecter des elements intra-cellulaires d’interet, comme des proteines ou des vesicules. De nombreuses methodes de detection ont ete developpees dans ce contexte, mais elles reclament souvent un parametrage assez fin et ne peuvent pas toujours s’adapter a l’echelle des elements a detecter. Nous proposons une methode incluant une selection d’echelle automatique precise et un seuillage localement adapte. La methode fournit de plus le support spatial effectif des elements detectes, qui ne sont pas necessairement circulaires. Des experimentations quantitatives sur images simulees montrent les avantages et les meilleures performances de notre methode. Des resultats sur sequences reelles confirment son interet.

[1]  Michael Unser,et al.  Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics , 2005, IEEE Transactions on Image Processing.

[2]  Lucas J. van Vliet,et al.  Recursive implementation of the Gaussian filter , 1995, Signal Process..

[3]  Didier Demigny,et al.  Fast Recursive Implementation of the Gaussian Filter , 2001, VLSI-SOC.

[4]  Patrick Bouthemy,et al.  SLT-LoG: A vesicle segmentation method with automatic scale selection and local thresholding applied to TIRF microscopy , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[5]  Charles Kervrann,et al.  A Rab11A/Myosin Vb/Rab11‐FIP2 Complex Frames Two Late Recycling Steps of Langerin from the ERC to the Plasma Membrane , 2012, Traffic.

[6]  Mohamed-Jalal Fadili,et al.  Multiscale Variance-Stabilizing Transform for Mixed-Poisson-Gaussian Processes and its Applications in Bioimaging , 2007, 2007 IEEE International Conference on Image Processing.

[7]  R. Vale,et al.  Total Internal Reflection Fluorescence Microscopy. , 2015, Cold Spring Harbor protocols.

[8]  Rachid Deriche,et al.  Fast algorithms for low-level vision , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[9]  Erik Meijering,et al.  Methods for cell and particle tracking. , 2012, Methods in enzymology.

[10]  Jean-Christophe Olivo-Marin,et al.  Extraction of spots in biological images using multiscale products , 2002, Pattern Recognit..

[11]  Patrick Bouthemy,et al.  Patch-Based Nonlocal Functional for Denoising Fluorescence Microscopy Image Sequences , 2010, IEEE Transactions on Medical Imaging.

[12]  Richard I. Hartley,et al.  A new approach for spot detection in total internal reflection fluorescence microscopy , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[13]  D. Bright,et al.  Two‐dimensional top hat filter for extracting spots and spheres from digital images , 1987 .

[14]  Charles Kervrann,et al.  Sélection d'échelle automatique précise et seuillage localement adapté pour la segmentation de vésicules en microscopie TIRF , 2014 .

[15]  Bart M. ter Haar Romeny,et al.  Front-End Vision and Multi-Scale Image Analysis , 2003, Computational Imaging and Vision.

[16]  Fionn Murtagh,et al.  Image Processing and Data Analysis - The Multiscale Approach , 1998 .

[17]  Rachid Deriche.  Fast Algorithms for Low-Level Vision , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Tony Lindeberg,et al.  Scale-Space Theory in Computer Vision , 1993, Lecture Notes in Computer Science.

[19]  Luc Vincent,et al.  Morphological grayscale reconstruction in image analysis: applications and efficient algorithms , 1993, IEEE Trans. Image Process..

[20]  Wiro J. Niessen,et al.  Quantitative Comparison of Spot Detection Methods in Fluorescence Microscopy , 2010, IEEE Transactions on Medical Imaging.

[21]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .