Dr John Chiverton

Research Interests: Biomedical image and signal processing and analysis, imaging and computer vision.

3D Image Processing of Nanoscale Fibres in XCT Images

Nano-metre based fibres are an important component in many materials, playing an essential role and providing improvements in many functional aspects such as mechanical properties and biocompatibility. The physical properties of the fibres in a material such as the geometric arrangement and distribution and physical properties such as size can have an impact on the role of the fibres in these different materials. Volumetric X-ray Computed Tomography (XCT) is fast becoming an important tool in the investigation of these properties of fibres in 3D. However the effect of XCT imaging resolution is often a difficult to understand limitation when imaging materials with sub-micron based fibres.

Another imaging modality with better sub-micron imaging resolution is Scanning Electron Microscopy (SEM), but which is limited to 2D estimates. Furthermore, the estimation of material fibre properties can be affected by differences in methods, partly due to a lack of specifically tailored fully automatic 3D image processing methods.

This study therefore sought to investigate a number of questions in this regard: Can fully automatic 3D image processing techniques be used to provide reliable estimates of the geometric properties and dimensions of fibre properties from XCT imaging data? Can estimates provided by fully automatic 3D image processing techniques be seen to be comparable to a gold standard 2D estimate from SEM?

Volume rendering of electro-spun fibres as used in our work:

JMicroscopy, 2018 https://doi.org/10.1111/jmi.12719

entropy modelling of 3D orientation and distance in steel fiber micro-tomography data

This work is concerned with the modelling and analysis of the orientation and distance between steel fibers in Xray Micro-Tomography (XCT) data. The advantage of combining both orientation and separation in a model is that it helps provide a detailed understanding of how the steel fibers are arranged, which is easy to compare. The developed models are designed to summarise the randomness of the orientation distribution of the steel fibers both locally and across an entire volume based on multiscale entropy. Theoretical modelling, simulation and application to real imaging data are shown here. The theoretical modelling of multiscale entropy for orientation includes a proof showing the final form of the multiscale taken over a linear range of scales. A series of image processing operations are also included to overcome interslice connectivity issues to help derive the statistical descriptions of the orientation distributions of the steel fibers. The results demonstrate that multiscale entropy provides unique insights into both simulated and real imaging data of steel fiber reinforced concrete.

Entropy estimation from steel fibre core samples:

Work from our IEEE TIP 2017 paper: https://doi.org/10.1109/TIP.2017.2722234

OVERVIEW

My research has focused on medical imaging and computer vision topics, specifically:

Partial Volume Modelling
Probabilistic modelling of imaging data
Active Contours using Level Sets
Image processing of structural and functional medical imaging data
Robust Shape Modelling
Computer vision for traffic safety

Links

Research Gate: https://www.researchgate.net/profile/John_Chiverton

University of Portsmouth page: http://www.port.ac.uk/school-of-engineering/staff/dr-john-chiverton.html

University of Portsmouth Research Portal: https://researchportal.port.ac.uk/portal/en/persons/john-chiverton(0fd3892d-1c0a-4e84-82fb-f7f1cf28e911).html

Google Scholar: https://scholar.google.co.uk/citations?user=UinAs4UAAAAJ&hl=en

Github page: https://github.com/chivertj

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