Pretorius, AJ, Zhou, Y and Ruddle, RA (2015) Visual parameter optimisation for biomedical image processing. BMC Bioinformatics, 16 (S11). S9. ISSN 1471-2105
Abstract
Background: Biomedical image processing methods require users to optimise input parameters to ensure high-quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output.
Results: We present a visualisation method that transforms users’ ability to understand algorithm behaviour by integrating input and output and supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm.
Conclusions: The visualisation method presented here provides users with a capability to combine multiple inputs and outputs in biomedical image processing that is not provided by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 Pretorius et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited |
Keywords: | visualisation; parameter optimisation; image analysis; image processing; biology; biomedicine; histology; design study |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Jun 2015 14:58 |
Last Modified: | 23 Jun 2023 21:48 |
Published Version: | http://dx.doi.org/10.1186/1471-2105-16-S11-S9 |
Status: | Published |
Publisher: | BioMed Central |
Refereed: | Yes |
Identification Number: | 10.1186/1471-2105-16-S11-S9 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:86634 |