| Paper: | WP-P1.11 |
| Session: | Implementation of Image and Video Processing Systems II / Biomedical Imaging |
| Time: | Wednesday, September 19, 14:30 - 17:10 |
| Presentation: |
Poster
|
| Title: |
MULTISCALE VARIANCE-STABILIZING TRANSFORM FOR MIXED-POISSON-GAUSSIAN PROCESSES AND ITS APPLICATIONS IN BIOIMAGING |
| Authors: |
Bo Zhang; URA CNRS 2582 | | |
| | Jalal Fadili; GREYC UMR CNRS 6072 | | |
| | Jean-Luc Starck; DAPNIA/SEDI-SAP CEA-Saclay | | |
| | Jean-Christophe Olivo-Marin; URA CNRS 2582 | | |
| Abstract: |
Fluorescence microscopy images are contaminated by photon and readout noises, and hence can be described by Mixed-Poisson-Gaussian (MPG) processes. In this paper, a new variance stabilizing transform (VST) is designed to convert a filtered MPG process into a near Gaussian process with a constant variance. This VST is then combined with the isotropic undecimated wavelet transform leading to a multiscale VST (MS-VST). We demonstrate the usefulness of MS-VST for image denoising and spot detection in fluorescence microscopy. In the first case, we detect significant Gaussianized wavelet coefficients under the control of a false discovery rate. A sparsity-driven iterative scheme is proposed to properly reconstruct the final estimate. In the second case, we show that the MS-VST can also lead to a fluorescent-spot detector, where the false positive rate of the detection in pure noise can be controlled. Experiments show that the MS-VST approach outperforms the generalized Anscombe transform in denoising, and that the detection scheme allows efficient spot extraction from complex background. |
