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Objective and repeatable image quality assessment with Gaussian mixture models

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.
Zenodo
Lackie, Peter M.
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a
Katsamenis, Orestis
8553e7c3-d860-4b7a-a883-abf6c0c4b438
Ho, Elaine Ming Li
7fa9df7f-4dbf-4be4-b03f-ff79012dd44b
Rossides, Charalambos
0a9d478d-4417-4841-83f5-d059172b3f9d
Schneider, Philipp
a810f925-4808-44e4-8a4a-a51586f9d7ad
Lackie, Peter M.
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a
Katsamenis, Orestis
8553e7c3-d860-4b7a-a883-abf6c0c4b438
Ho, Elaine Ming Li
7fa9df7f-4dbf-4be4-b03f-ff79012dd44b
Rossides, Charalambos
0a9d478d-4417-4841-83f5-d059172b3f9d
Schneider, Philipp
a810f925-4808-44e4-8a4a-a51586f9d7ad

(2020) Objective and repeatable image quality assessment with Gaussian mixture models. Zenodo doi:10.5281/zenodo.3688732 [Dataset]

Record type: Dataset

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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More information

Published date: 26 February 2020

Identifiers

Local EPrints ID: 472153
URI: http://eprints.soton.ac.uk/id/eprint/472153
PURE UUID: f55c9ed5-a126-4705-9729-7a18ebacc134
ORCID for Peter M. Lackie: ORCID iD orcid.org/0000-0001-7138-3764
ORCID for Orestis Katsamenis: ORCID iD orcid.org/0000-0003-4367-4147
ORCID for Philipp Schneider: ORCID iD orcid.org/0000-0001-7499-3576

Catalogue record

Date deposited: 28 Nov 2022 17:58
Last modified: 15 Jul 2023 01:43

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Contributors

Contributor: Peter M. Lackie ORCID iD
Contributor: Orestis Katsamenis ORCID iD
Contributor: Elaine Ming Li Ho
Contributor: Charalambos Rossides
Contributor: Philipp Schneider ORCID iD

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