Bayesian Approach for Automatic Joint Parameter Estimation in 3D Image Reconstruction from Multi-Focus Microscope

Seunghwan Yoo, Pablo Ruiz, Xiang Huang, Kuan He, Xiaolei Wang, Itay Gdor, Alan Selewa, Matthew Daddysman, Nicola J Ferrier, Mark Hereld, Norbert F. Scherer, Oliver Strides Cossairt, Aggelos K Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a Bayesian approach for 3D image reconstruction of an extended object imaged with multi-focus microscopy (MFM). MFM simultaneously captures multiple sub-images of different focal planes to provide 3D information of the sample. The naive method to reconstruct the object is to stack the sub-images along the z -axis, but the result suffers from poor resolution in the z -axis. The maximum a posteriori framework provides a way to reconstruct a 3D image according to its observation model and prior knowledge. It jointly estimates the 3D image and the model parameters. Experimental results with synthetic and real experimental data show that it enables the high-quality 3D reconstruction of an extended object from MFM.
Original languageEnglish (US)
Title of host publication2018 25th IEEE International Conference on Image Processing (ICIP)
PublisherIEEE
ISBN (Electronic)978-1479970612
DOIs
StatePublished - 2018

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microscopy
parameter estimation
method
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parameter

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Yoo, S., Ruiz, P., Huang, X., He, K., Wang, X., Gdor, I., ... Katsaggelos, A. K. (2018). Bayesian Approach for Automatic Joint Parameter Estimation in 3D Image Reconstruction from Multi-Focus Microscope. In 2018 25th IEEE International Conference on Image Processing (ICIP) IEEE. https://doi.org/10.1109/ICIP.2018.8451309
Yoo, Seunghwan ; Ruiz, Pablo ; Huang, Xiang ; He, Kuan ; Wang, Xiaolei ; Gdor, Itay ; Selewa, Alan ; Daddysman, Matthew ; Ferrier, Nicola J ; Hereld, Mark ; Scherer, Norbert F. ; Cossairt, Oliver Strides ; Katsaggelos, Aggelos K. / Bayesian Approach for Automatic Joint Parameter Estimation in 3D Image Reconstruction from Multi-Focus Microscope. 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018.
@inproceedings{101d93222e45425e84501aa707afa716,
title = "Bayesian Approach for Automatic Joint Parameter Estimation in 3D Image Reconstruction from Multi-Focus Microscope",
abstract = "We present a Bayesian approach for 3D image reconstruction of an extended object imaged with multi-focus microscopy (MFM). MFM simultaneously captures multiple sub-images of different focal planes to provide 3D information of the sample. The naive method to reconstruct the object is to stack the sub-images along the z -axis, but the result suffers from poor resolution in the z -axis. The maximum a posteriori framework provides a way to reconstruct a 3D image according to its observation model and prior knowledge. It jointly estimates the 3D image and the model parameters. Experimental results with synthetic and real experimental data show that it enables the high-quality 3D reconstruction of an extended object from MFM.",
author = "Seunghwan Yoo and Pablo Ruiz and Xiang Huang and Kuan He and Xiaolei Wang and Itay Gdor and Alan Selewa and Matthew Daddysman and Ferrier, {Nicola J} and Mark Hereld and Scherer, {Norbert F.} and Cossairt, {Oliver Strides} and Katsaggelos, {Aggelos K}",
year = "2018",
doi = "10.1109/ICIP.2018.8451309",
language = "English (US)",
booktitle = "2018 25th IEEE International Conference on Image Processing (ICIP)",
publisher = "IEEE",

}

Yoo, S, Ruiz, P, Huang, X, He, K, Wang, X, Gdor, I, Selewa, A, Daddysman, M, Ferrier, NJ, Hereld, M, Scherer, NF, Cossairt, OS & Katsaggelos, AK 2018, Bayesian Approach for Automatic Joint Parameter Estimation in 3D Image Reconstruction from Multi-Focus Microscope. in 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE. https://doi.org/10.1109/ICIP.2018.8451309

Bayesian Approach for Automatic Joint Parameter Estimation in 3D Image Reconstruction from Multi-Focus Microscope. / Yoo, Seunghwan; Ruiz, Pablo; Huang, Xiang; He, Kuan; Wang, Xiaolei; Gdor, Itay; Selewa, Alan; Daddysman, Matthew; Ferrier, Nicola J; Hereld, Mark; Scherer, Norbert F.; Cossairt, Oliver Strides; Katsaggelos, Aggelos K.

2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Bayesian Approach for Automatic Joint Parameter Estimation in 3D Image Reconstruction from Multi-Focus Microscope

AU - Yoo, Seunghwan

AU - Ruiz, Pablo

AU - Huang, Xiang

AU - He, Kuan

AU - Wang, Xiaolei

AU - Gdor, Itay

AU - Selewa, Alan

AU - Daddysman, Matthew

AU - Ferrier, Nicola J

AU - Hereld, Mark

AU - Scherer, Norbert F.

AU - Cossairt, Oliver Strides

AU - Katsaggelos, Aggelos K

PY - 2018

Y1 - 2018

N2 - We present a Bayesian approach for 3D image reconstruction of an extended object imaged with multi-focus microscopy (MFM). MFM simultaneously captures multiple sub-images of different focal planes to provide 3D information of the sample. The naive method to reconstruct the object is to stack the sub-images along the z -axis, but the result suffers from poor resolution in the z -axis. The maximum a posteriori framework provides a way to reconstruct a 3D image according to its observation model and prior knowledge. It jointly estimates the 3D image and the model parameters. Experimental results with synthetic and real experimental data show that it enables the high-quality 3D reconstruction of an extended object from MFM.

AB - We present a Bayesian approach for 3D image reconstruction of an extended object imaged with multi-focus microscopy (MFM). MFM simultaneously captures multiple sub-images of different focal planes to provide 3D information of the sample. The naive method to reconstruct the object is to stack the sub-images along the z -axis, but the result suffers from poor resolution in the z -axis. The maximum a posteriori framework provides a way to reconstruct a 3D image according to its observation model and prior knowledge. It jointly estimates the 3D image and the model parameters. Experimental results with synthetic and real experimental data show that it enables the high-quality 3D reconstruction of an extended object from MFM.

U2 - 10.1109/ICIP.2018.8451309

DO - 10.1109/ICIP.2018.8451309

M3 - Conference contribution

BT - 2018 25th IEEE International Conference on Image Processing (ICIP)

PB - IEEE

ER -

Yoo S, Ruiz P, Huang X, He K, Wang X, Gdor I et al. Bayesian Approach for Automatic Joint Parameter Estimation in 3D Image Reconstruction from Multi-Focus Microscope. In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE. 2018 https://doi.org/10.1109/ICIP.2018.8451309