MaIterativeSolvingLightfieldMicroscopy

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Thesis: Evaluation of iterative solving methods for the statistical reconstruction of lightfield microscopy data

Thesis by:
Advisor: Tobias Lasser
Supervision by: Anca Stefanoiu

Abstract

Light field microscopy is a scanless techniques for high speed 3D imaging of fluorescent specimens. A conventional microscope can be turned into a light field microscope by placing a microlens array in front of the camera allowing for a full spatio-angular capture of the light field in a single snapshot. The recorded information can be used to volumetrically reconstruct the imaged sample. Once the forward light transfer is determined based on the optical system response, the reconstruction process is an inverse problem. In fluorescence microscopy, besides the read-out noise, Poisson noise is present due to the low photon count. Hence a Poisson-Gaussian mixture model would be an appropriate approach for likelihood-based statistical reconstruction. Various iteration schemes may result from different likelihoods coupled with regularization.

Tasks

The aim of this thesis is the implementation and evaluation of iterative solving schemes for the likelihood-based statistical reconstruction of lightfield microscopy data.

Requirements

Scientific Programming, C++

Contact


ProjectForm
Title: Evaluation of iterative solving methods for the statistical reconstruction of lightfield microscopy data
Abstract: Light field microscopy is a scanless techniques for high speed 3D imaging of fluorescent specimens. A conventional microscope can be turned into a light field microscope by placing a microlens array in front of the camera allowing for a full spatio-angular capture of the light field in a single snapshot. The recorded information can be used to volumetrically reconstruct the imaged sample. Once the forward light transfer is determined based on the optical system response, the reconstruction process is an inverse problem. In fluorescence microscopy, besides the read-out noise, Poisson noise is present due to the low photon count. Hence a Poisson-Gaussian mixture model would be an appropriate approach for likelihood-based statistical reconstruction. Various iteration schemes may result from different likelihoods coupled with regularization.
Student:  
Director: Tobias Lasser
Supervisor: Anca Stefanoiu
Type: DA/MA/BA
Area: Medical Imaging
Status: open
Start: 2017/11/15
Finish:  
Thesis (optional):  
Picture:  


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