Objective and repeatable image quality assessment with Gaussian mixture models
Objective and repeatable image quality assessment with Gaussian mixture models
Quantifying image quality enables objective optimisation of imaging protocols. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measure visibility of features in relation to the image noise. Conventional SNR and CNR measurement is performed by user selection of regions in the image representing each material, which is not repeatable and impractical for large numbers of 3D datasets. Here, a semi-automated, objective and repeatable method of calculating SNR and CNR is presented which does not require user definition of regions-of-interest. This method utilises Gaussian mixture models to separate materials in the specimen based on the grey value distribution of the image. This tool is available as a graphical user interface for Fiji/ImageJ users, and as importable libraries for Python users under the GNU General Public License v3.0.
image quality, SNR, CNR, gaussian mixture model
Ho, Elaine Ming Li
7fa9df7f-4dbf-4be4-b03f-ff79012dd44b
Rossides, Charalambos
8ea65ff8-3ba0-46d3-9f88-18553aedfb7d
Katsamenis, Orestis L.
8553e7c3-d860-4b7a-a883-abf6c0c4b438
Lackie, Peter M.
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a
Schneider, Philipp
a810f925-4808-44e4-8a4a-a51586f9d7ad
26 February 2020
Ho, Elaine Ming Li
7fa9df7f-4dbf-4be4-b03f-ff79012dd44b
Rossides, Charalambos
8ea65ff8-3ba0-46d3-9f88-18553aedfb7d
Katsamenis, Orestis L.
8553e7c3-d860-4b7a-a883-abf6c0c4b438
Lackie, Peter M.
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a
Schneider, Philipp
a810f925-4808-44e4-8a4a-a51586f9d7ad
(2020)
Objective and repeatable image quality assessment with Gaussian mixture models.
Zenodo
doi:10.5281/zenodo.3688733
[Software]
Abstract
Quantifying image quality enables objective optimisation of imaging protocols. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measure visibility of features in relation to the image noise. Conventional SNR and CNR measurement is performed by user selection of regions in the image representing each material, which is not repeatable and impractical for large numbers of 3D datasets. Here, a semi-automated, objective and repeatable method of calculating SNR and CNR is presented which does not require user definition of regions-of-interest. This method utilises Gaussian mixture models to separate materials in the specimen based on the grey value distribution of the image. This tool is available as a graphical user interface for Fiji/ImageJ users, and as importable libraries for Python users under the GNU General Public License v3.0.
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Published date: 26 February 2020
Keywords:
image quality, SNR, CNR, gaussian mixture model
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Local EPrints ID: 448086
URI: http://eprints.soton.ac.uk/id/eprint/448086
PURE UUID: 5d66248d-db56-4ebd-b882-065d8a0e1770
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Date deposited: 01 Apr 2021 15:42
Last modified: 17 Mar 2024 03:34
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