Raumonen, P;
Tarvainen, T;
(2018)
Segmentation of vessel structures from photoacoustic images with reliability assessment.
Biomedical Optics Express
, 9
(7)
pp. 2887-2904.
10.1364/BOE.9.002887.
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Abstract
Photoacoustic imaging enables the imaging of soft biological tissue with combined optical contrast and ultrasound resolution. One of the targets of interest is tissue vasculature. However, the photoacoustic images may not directly provide the information on, for example, vasculature structure. Therefore, the images are improved by reducing noise and artefacts, and processed for better visualisation of the target of interest. In this work, we present a new segmentation method of photoacoustic images that also straightforwardly produces assessments of its reliability. The segmentation depends on parameters which have a natural tendency to increase the reliability as the parameter values monotonically change. The reliability is assessed by counting classifications of image voxels with different parameter values. The resulting segmentation with reliability offers new ways and tools to analyse photoacoustic images and new possibilities for utilising them as anatomical priors in further computations. Our MATLAB implementation of the method is available as an open-source software package [P. Raumonen, Matlab, 2018].
| Type: | Article |
|---|---|
| Title: | Segmentation of vessel structures from photoacoustic images with reliability assessment |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1364/BOE.9.002887 |
| Publisher version: | https://doi.org/10.1364/BOE.9.002887 |
| Language: | English |
| Additional information: | This is the published version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10053587 |
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