Paper Info
Title
Using density invariant graph Laplacian to resolve unobservable parameters for three-dimensional optical bio-imaging
Abstract
We explore the graph Laplacian eigenmap for the application of three-dimensional (3D) optical bioimaging. By using the density invariant graph Laplacian, each high-dimensional sampling (e.g. an image) could be represented by a low-dimensional description. These descriptions not only preserve key features of raw images but also estimate unobservable parameters for 3D imaging. In this paper, we apply this method for two 3D optical microscopies under following scenarios: (i) 3D optical tomography with projections of unknown orientation. (ii) 3D deconvolution microscopy with a disordered focal stack. To prove the robustness of the method, we use images from real biological systems and experimental measurements. In both cases, our results show that the density invariant graph Laplacian is able to overcome practical issues such as limited number of measurement, unstable environment, misalignment and experimental noise.
Year
DOI
Venue
2014
10.1109/ICASSP.2014.6853872
ICASSP
Keywords
Field
DocType
optical tomography,high-dimensional sampling,raw images,3d optical microscopies,disordered focal stack,3d deconvolution microscopy,biological techniques,density invariant graph laplacian,low-dimensional description,optical microscopy,unobservable parameters,3d optical bioimaging,three-dimensional optical bioimaging,experimental measurements,dimension reduction,3d optical tomography,real biological systems,manifold learning,3d bio-imaging,image reconstruction,microscopy,tomography,optical imaging
Laplacian matrix,Pattern recognition,Computer science,Deconvolution,Robustness (computer science),Artificial intelligence,Sampling (statistics),Invariant (mathematics),Microscopy,Optical tomography,Unobservable
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
9
2
Name
Order
Citations
PageRank
Chien-Hung Lu100.34
Pei Yuan Wu2163.96