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Semi-automated Intensity-based Image quality assessment with Gaussian Mixture Models

Semi-automated Intensity-based Image quality assessment with Gaussian Mixture Models
Semi-automated Intensity-based Image quality assessment with Gaussian Mixture Models
Protocol development for X-ray micro-computed tomography (microCT) requires optimisation of several factors at once. Quantifying microCT image quality enables objective selection of the most suitable protocol for the user requirements. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) are image quality metrics describing the clarity of a feature in relation to the image noise. SNR and CNR are typically determined by comparing intensities of user-defined regions containing the feature of interest and the background. However, this method is tedious and impractical when comparing between many 3D image datasets. Reiter et al. proposed a region of interest (ROI)-independent method of determining CNR from image intensity distributions of microCT datasets (1). MicroCT intensities are proportional to material- dependent X-ray attenuation, so it is assumed that the overall image intensity for multi-material specimens is the sum of individual Gaussian distributions for each material. Reiter et al. calculate CNR from the means and variances of Gaussians fitted for each material, isolated from the overall image intensity distribution. However, their method is limited to well-separated Gaussians, which is often not applicable in microCT for biological specimens with overlapping image intensity distributions. Here we present a semi-automated tool for determining SNR and CNR from microCT data using Gaussian Mixture Models (GMMs). GMMs estimate the mean, variance and weight of a user-specified number of Gaussian components from the image intensity distribution. These properties are then used to calculate SNR and CNR between any combination of the Gaussian components. This tool was implemented using the Python scikit-learn library (2) and has a custom ImageJ user interface. Validation was performed in silico using a phantom image with pre-defined Gaussian distributions of image intensity for three materials. The phantom image used was a microCT scan of a paraffin-embedded murine colon from a custom-built microCT setup (Nikon Metrology, UK). Materials were segmented by thresholding and image intensities from known distributions of similar specimens were assigned to each pixel, according to the materials represented. The SNR and CNR calculated by GMMs was found to be within 2.5% of the phantom image values. In conclusion, fitting GMMs to the intensity distribution offers a repeatable and objective measurement of image quality for 3D microCT datasets, even from low contrast images with overlapping distributions. Furthermore, SNR and CNR between several material components can be calculated at once without additional user selection of ROIs for each material.
Micro-computed tomography, Image quality, X-ray, Gaussian mixture model
Ho, Elaine Ming Li
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Rossides, Charalambos
8ea65ff8-3ba0-46d3-9f88-18553aedfb7d
Pender, Sylvia
62528b03-ec42-41bb-80fe-48454c2c5242
Katsamenis, Orestis L.
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Lackie, Peter
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Schneider, Philipp
a810f925-4808-44e4-8a4a-a51586f9d7ad
Ho, Elaine Ming Li
7fa9df7f-4dbf-4be4-b03f-ff79012dd44b
Rossides, Charalambos
8ea65ff8-3ba0-46d3-9f88-18553aedfb7d
Pender, Sylvia
62528b03-ec42-41bb-80fe-48454c2c5242
Katsamenis, Orestis L.
8553e7c3-d860-4b7a-a883-abf6c0c4b438
Lackie, Peter
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a
Schneider, Philipp
a810f925-4808-44e4-8a4a-a51586f9d7ad

Ho, Elaine Ming Li, Rossides, Charalambos, Pender, Sylvia, Katsamenis, Orestis L., Lackie, Peter and Schneider, Philipp (2020) Semi-automated Intensity-based Image quality assessment with Gaussian Mixture Models. The 4th Network of European Bioimage Analysts (NEUBIAS) Conference & Symposium 2020, France, Bordeaux, France. 29 Feb - 06 Mar 2020.

Record type: Conference or Workshop Item (Poster)

Abstract

Protocol development for X-ray micro-computed tomography (microCT) requires optimisation of several factors at once. Quantifying microCT image quality enables objective selection of the most suitable protocol for the user requirements. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) are image quality metrics describing the clarity of a feature in relation to the image noise. SNR and CNR are typically determined by comparing intensities of user-defined regions containing the feature of interest and the background. However, this method is tedious and impractical when comparing between many 3D image datasets. Reiter et al. proposed a region of interest (ROI)-independent method of determining CNR from image intensity distributions of microCT datasets (1). MicroCT intensities are proportional to material- dependent X-ray attenuation, so it is assumed that the overall image intensity for multi-material specimens is the sum of individual Gaussian distributions for each material. Reiter et al. calculate CNR from the means and variances of Gaussians fitted for each material, isolated from the overall image intensity distribution. However, their method is limited to well-separated Gaussians, which is often not applicable in microCT for biological specimens with overlapping image intensity distributions. Here we present a semi-automated tool for determining SNR and CNR from microCT data using Gaussian Mixture Models (GMMs). GMMs estimate the mean, variance and weight of a user-specified number of Gaussian components from the image intensity distribution. These properties are then used to calculate SNR and CNR between any combination of the Gaussian components. This tool was implemented using the Python scikit-learn library (2) and has a custom ImageJ user interface. Validation was performed in silico using a phantom image with pre-defined Gaussian distributions of image intensity for three materials. The phantom image used was a microCT scan of a paraffin-embedded murine colon from a custom-built microCT setup (Nikon Metrology, UK). Materials were segmented by thresholding and image intensities from known distributions of similar specimens were assigned to each pixel, according to the materials represented. The SNR and CNR calculated by GMMs was found to be within 2.5% of the phantom image values. In conclusion, fitting GMMs to the intensity distribution offers a repeatable and objective measurement of image quality for 3D microCT datasets, even from low contrast images with overlapping distributions. Furthermore, SNR and CNR between several material components can be calculated at once without additional user selection of ROIs for each material.

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Published date: 3 March 2020
Venue - Dates: The 4th Network of European Bioimage Analysts (NEUBIAS) Conference & Symposium 2020, France, Bordeaux, France, 2020-02-29 - 2020-03-06
Keywords: Micro-computed tomography, Image quality, X-ray, Gaussian mixture model

Identifiers

Local EPrints ID: 448072
URI: http://eprints.soton.ac.uk/id/eprint/448072
PURE UUID: 260394dd-221b-4ab1-b6bf-7e58e37bec4f
ORCID for Charalambos Rossides: ORCID iD orcid.org/0000-0002-7547-0256
ORCID for Sylvia Pender: ORCID iD orcid.org/0000-0001-6332-0333
ORCID for Orestis L. Katsamenis: ORCID iD orcid.org/0000-0003-4367-4147
ORCID for Peter Lackie: ORCID iD orcid.org/0000-0001-7138-3764
ORCID for Philipp Schneider: ORCID iD orcid.org/0000-0001-7499-3576

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Date deposited: 01 Apr 2021 15:41
Last modified: 22 Nov 2021 03:15

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