Bayesian image recovery for dendritic structures under low
signal-to-noise conditions
IEEE Transactions on Image Processing 18: 471-482.
Experimental research seeking to quantify neuronal structure
constantly contends with restrictions on image resolution and
variability. In particular, experimentalists often need to analyze
images with very low signal-to-noise ratio (SNR). In many experiments
dye toxicity scales with the light intensity; this leads
experimentalists to reduce image SNR in order to preserve the
viability of the specimen. In this work we present a Bayesian approach
for estimating the neuronal shape given low-SNR observations. This
Bayesian framework has two major advantages. First, the method
effectively incorporates known facts about 1) the image formation
process, including blur and the Poisson nature of image noise at low
intensities, and 2) dendritic shape, including the fact that dendrites
are simply-connected geometric structures with smooth boundaries.
Second, we may employ standard Markov chain Monte Carlo (MCMC)
techniques for quantifying the posterior uncertainty in our estimate
of the dendritic shape. We describe an efficient computational
implementation of these methods and demonstrate the algorithm's
performance on simulated noisy two-photon laser-scanning microscopy
images.
Preprint | Sample code
(zipped Matlab code plus a sample image;
500K) | Liam
Paninski's research page