Abstract

1. O 2 O 1 Total number of active molecules O 3 PhotoblinkingModel(βON>βOFF) Photobleaching Model ; Denoising of Fluorescent Confocal Microscopy images affected by the Photoblinking/Photobleaching effects Isabel Rodrigues and J.Miguel Sanches Institute for Systems and Robotics / Instituto Superior Técnico Lisboa, Portugal Abstract Fluorescence confocal microscopy images present a low signal to noise ratio and a time intensity decay due to the so called photoblinking and photobleaching effects. These effects, together with the Poisson multiplicative noise that corrupts the images, make long time biological observation processes very difficult. In this work a Bayesian denoising algorithm for Poisson data is presented where two different a priori distributions in the time and space domains are used to regularize the solution. These distributions, used to describe the spatio-temporal correlation among neighboring pixels, allow to greatly improve the SNR of the denoised solution, mainly, in the last images of a sequence. An intensity decay model for the photoblinking and photobleaching effects is presented and theoretical foundations are given. Monte Carlo experiments and validation with other models, using synthetic data, are presented to characterize the performance of the algorithm. Also an example with a real data sequence is included to illustrate its application. Experimental Results Model Validation: Monte Carlo experiment Problem Formulation Photobleaching/Photoblinking model Three main states of the fluorescence molecules: ON-state - able to fluoresce and be observed OFF-state - not able to fluoresce and not visible Permanently-OFF-state - permanently OFF. Comparison with state-of-the-art models Dynamics of the number of molecules at the ON-state, (directly related with the Intensity of the image), from (1-3): Experimental rates of decay Solution: Real data results • Data exhibiting a severe type of signal-dependent noise, assumed to obey a Poisson distribution: • Blur is neglected. • Independence of the observations assumed. • Bayesian framework: MAP estimate. • X as Markov Random Field (MRF) - Gibbs distribution for X. • Anisotropic prior terms. • log-Euclidean –TV edge preserving priors in space and in time. The energy function Convex optimization through variable change: Conclusions In this work a denoising algorithm with an embedded Photobleaching/ Photoblinking dynamic model for the Fluorescence Confocal Image intensity decay is presented. A Monte-Carlo experiment and validation with other state-of-the-art methods show the superior performance of the proposed model. RecPad2010 - 16th edition of the Portuguese Conference on Pattern Recognition, UTAD University, Vila Real city, October 29th