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Towards automated classification of clinical optical coherence tomography data of dense tissues

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Abstract

The native contrast of optical coherence tomography (OCT) data in dense tissues can pose a challenge for clinical decision making. Automated data evaluation is one way of enhancing the clinical utility of measurements. Methods for extracting information from structural OCT data are appraised here. A-scan analysis allows characterization of layer thickness and scattering parameters, whereas image analysis renders itself to segmentation, texture and speckle analysis. All fully automated approaches combine pre-processing, feature registration, data reduction, and classification. Pre-processing requires de-noising, feature recognition, normalization and refining. In the current literature, image exclusion criteria, initial parameters, or manual input are common requirements. The interest of the presented methods lies in the prospect of objective, quick, and/or post-acquisition processing. There is a potential to improve clinical decision making based on automated processing of OCT data.

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Acknowledgments

This work was funded by the Pump Prime Fund of Cranfield University, the Gloucestershire Hospitals NHS Foundation Trust, and receives funding from the Technology Strategy Board [Grant/Project title OMICRON]. The authors would like to thank Professor Hugh Barr and Ms. Joanne Hutchings for kindly providing the esophageal sample for Fig. 1 (ethics approval by Gloucestershire Local Research Ethics Committee), and Dr. Catherine Kendall and Mr. Martin Isabelle for proofreading this manuscript. Dr. Nicholas Stone holds a Career Scientist Fellowship, which is funded by the UK National Institute of Health Research.

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Authors and Affiliations

  1. Cranfield Health, Cranfield University at Silsoe, Silsoe, Bedfordshire, UK

    Florian Bazant-Hegemark

  2. Biophotonics Research Group, Gloucestershire Royal Hospital, Great Western Road, Gloucester, GL1 3NN, UK

    Florian Bazant-Hegemark & Nicholas Stone

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  1. Florian Bazant-Hegemark
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  2. Nicholas Stone
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Correspondence to Nicholas Stone.

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Bazant-Hegemark, F., Stone, N. Towards automated classification of clinical optical coherence tomography data of dense tissues. Lasers Med Sci 24, 627–638 (2009). https://doi.org/10.1007/s10103-008-0615-6

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