title: Fluorescence microscopy image noise reduction using a stochastically-connected random field model.
abstract: Fluorescence microscopy is an essential part of a biologist's toolkit, allowing assaying of many parameters like subcellular localization of proteins, changes in cytoskeletal dynamics, protein-protein interactions, and the concentration of specific cellular ions. A fundamental challenge with using fluorescence microscopy is the presence of noise. This study introduces a novel approach to reducing noise in fluorescence microscopy images. The noise reduction problem is posed as a Maximum A Posteriori estimation problem, and solved using a novel random field model called stochastically-connected random field (SRF), which combines random graph and field theory. Experimental results using synthetic and real fluorescence microscopy data show the proposed approach achieving strong noise reduction performance when compared to several other noise reduction algorithms, using quantitative metrics. The proposed SRF approach was able to achieve strong performance in terms of signal-to-noise ratio in the synthetic results, high signal to noise ratio and contrast to noise ratio in the real fluorescence microscopy data results, and was able to maintain cell structure and subtle details while reducing background and intra-cellular noise.
authors.affiliation: Vision and Image Processing (VIP) Research Group, Department of Systems Design Engineering, University of Waterloo, 200 University Avenue W, Waterloo, ON, N2L 3G1, Canada.
authors.forename: S A
authors.initials: SA
authors.lastname: Haider
authors.affiliation: Vision and Image Processing (VIP) Research Group, Department of Systems Design Engineering, University of Waterloo, 200 University Avenue W, Waterloo, ON, N2L 3G1, Canada.
authors.forename: A
authors.initials: A
authors.lastname: Cameron
authors.affiliation: Vision and Image Processing (VIP) Research Group, Department of Systems Design Engineering, University of Waterloo, 200 University Avenue W, Waterloo, ON, N2L 3G1, Canada.
authors.forename: P
authors.initials: P
authors.lastname: Siva
authors.affiliation: Vision and Image Processing (VIP) Research Group, Department of Systems Design Engineering, University of Waterloo, 200 University Avenue W, Waterloo, ON, N2L 3G1, Canada.
authors.forename: D
authors.initials: D
authors.lastname: Lui
authors.affiliation: Vision and Image Processing (VIP) Research Group, Department of Systems Design Engineering, University of Waterloo, 200 University Avenue W, Waterloo, ON, N2L 3G1, Canada.
authors.forename: M J
authors.initials: MJ
authors.lastname: Shafiee
authors.affiliation: Vision and Image Processing (VIP) Research Group, Department of Systems Design Engineering, University of Waterloo, 200 University Avenue W, Waterloo, ON, N2L 3G1, Canada.
authors.forename: A
authors.initials: A
authors.lastname: Boroomand
authors.affiliation: Department of Medical Biophysics, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada.
authors.forename: N
authors.initials: N
authors.lastname: Haider
authors.affiliation: Vision and Image Processing (VIP) Research Group, Department of Systems Design Engineering, University of Waterloo, 200 University Avenue W, Waterloo, ON, N2L 3G1, Canada.
authors.forename: A
authors.initials: A
authors.lastname: Wong
mesh_terms: D007091:Image Processing, Computer-Assisted; D008856:Microscopy, Fluorescence; D008962:Models, Theoretical; D059629:Signal-To-Noise Ratio; D013269:Stochastic Processes