Variational attenuation correction in two-view confocal microscopy

BackgroundAbsorption and refraction induced signal attenuation can seriously hinder the extraction of quantitative information from confocal microscopic data. This signal attenuation can be estimated and corrected by algorithms that use physical image formation models. Especially in thick heterogeneous samples, current single view based models are unable to solve the underdetermined problem of estimating the attenuation-free intensities.ResultsWe present a variational approach to estimate both, the real intensities and the spatially variant attenuation from two views of the same sample from opposite sides. Assuming noise-free measurements throughout the whole volume and pure absorption, this would in theory allow a perfect reconstruction without further assumptions. To cope with real world data, our approach respects photon noise, estimates apparent bleaching between the two recordings, and constrains the attenuation field to be smooth and sparse to avoid spurious attenuation estimates in regions lacking valid measurements.ConclusionsWe quantify the reconstruction quality on simulated data and compare it to the state-of-the art two-view approach and commonly used one-factor-per-slice approaches like the exponential decay model. Additionally we show its real-world applicability on model organisms from zoology (zebrafish) and botany (Arabidopsis). The results from these experiments show that the proposed approach improves the quantification of confocal microscopic data of thick specimen.

[1]  O. A L-Ko F A H I,et al.  Attenuation correction in confocal laser microscopes : a novel two-view approach , 2003 .

[2]  Thomas Brox,et al.  ViBE-Z: a framework for 3D virtual colocalization analysis in zebrafish larval brains , 2012, Nature Methods.

[3]  J. Pawley,et al.  Handbook of Biological Confocal Microscopy , 1990, Springer US.

[4]  Thomas Brox,et al.  Von Pixeln zu Regionen: partielle Differentialgleichungen in der Bildanalyse , 2005 .

[5]  Dinu Coltuc,et al.  Automated compensation of light attenuation in confocal microscopy by exact histogram specification , 2010, Microscopy research and technique.

[6]  Stefan W. Hell,et al.  Aberrations in confocal and multi-photon fluorescence microscopy induced by refractive index mismatch , 2006 .

[7]  M. Capek,et al.  Methods for compensation of the light attenuation with depth of images captured by a confocal microscope , 2006, Microscopy research and technique.

[8]  Jorge Nocedal,et al.  Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization , 1997, TOMS.

[9]  T. Wilson,et al.  Aberration correction for confocal imaging in refractive‐index‐mismatched media , 1998 .

[10]  Frans C. A. Groen,et al.  Absorption and scattering correction in fluorescence confocal microscopy , 1991 .

[11]  Y Q Guan,et al.  Adaptive correction technique for 3D reconstruction of fluorescence microscopy images , 2008, Microscopy research and technique.

[12]  Brakenhoff,et al.  Fluorescence photobleaching‐based shading correction for fluorescence microscopy , 1998 .

[13]  Margret Keuper,et al.  Variational attenuation correction of two-view confocal microscopic recordings , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[14]  Graeme P. Penney,et al.  Estimating and resolving uncertainty in cardiac respiratory motion modelling , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[15]  B. F. Logan,et al.  The Fourier reconstruction of a head section , 1974 .

[16]  Karen O. Egiazarian,et al.  Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data , 2008, IEEE Transactions on Image Processing.