Early detection of oesophageal cancer through colour contrast enhancement for data augmentation

Conference paper

Gao, X., Taylor, S., Pang, W., Lu, X. and Braden, B. 2022. Early detection of oesophageal cancer through colour contrast enhancement for data augmentation. SPIE Medical Imaging: Computer-Aided Diagnosis. San Diego, USA 21 - 24 Feb 2022 Society of Photo-Optical Instrumentation Engineers. https://doi.org/10.1117/12.2611409
TypeConference paper
TitleEarly detection of oesophageal cancer through colour contrast enhancement for data augmentation
AuthorsGao, X., Taylor, S., Pang, W., Lu, X. and Braden, B.
Abstract

While white light imaging (WLI) of endoscopy has been set as the gold standard for screening and detecting oesophageal squamous cell cancer (SCC), the early signs of SCC are often missed (1 in 4) due to its subtle change of early onset of SCC. This study firstly enhances colour contrast of each of over 600 WLI images and their accompanying narrow band images (NBI) applying CIE colour appearance model CIECAM02. Then these augmented data together with the original images are employed to train a deep learning based system for classification of low grade dysplasia (LGD), SCC and high grade dysplasia (HGD). As a result, the averaged colour difference (ΔE) measured using CIEL*a*b* increased from 11.60 to 14.46 for WLI and from 17.52 to 32.53 for NBI in appearance between suspected regions and their normal neighbours. When training a deep learning system with added enhanced contrasted WLI images, the sensitivity, specific and accuracy for LGD increases by 10.87%, 4.95% and 6.76% respectively. When training with enhanced both WLI and NBI images, these measures for LGD increases by 14.83%, 4.89% and 7.97% respectively, the biggest increase among three classes of SCC, HGD and LGD. In average, the sensitivity, specificity and accuracy for these three classes are 88.26%, 94.44% and 92.63% respectively for classification of SCC, HGD and LGD, being comparable or exceeding existing published work.

KeywordsOesophageal cancer; data augmentation; colour contrast; deep learning
Research GroupArtificial Intelligence group
ConferenceSPIE Medical Imaging: Computer-Aided Diagnosis
Proceedings TitleProceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis
ISSN0277-786X
Electronic1996-756X
ISBN9781510649415
PublisherSociety of Photo-Optical Instrumentation Engineers
Publication dates
Print22 Feb 2022
Online04 Apr 2022
Publication process dates
Deposited21 Jan 2022
Accepted03 Dec 2021
Output statusPublished
Accepted author manuscript
Copyright Statement

Copyright 2022 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.
Xiaohong Gao, Stephen Taylor, Wei Pang, Xin Lu, Barbara Braden, "Early detection of oesophageal cancer through colour contrast enhancement for data augmentation," Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203303 (4 April 2022); https://doi.org/10.1117/12.2611409

Web address (URL)https://spie.org/medical-imaging/presentation/Early-detection-of-oesophageal-cancer-through-colour-contrast-enhancement-for/12033-17
Digital Object Identifier (DOI)https://doi.org/10.1117/12.2611409
Scopus EID2-s2.0-85132843152
Web of Science identifierWOS:000838048600002
LanguageEnglish
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Gao, X. and Clark, J. 2008. A fast approach to segmentation of PET brain images for extraction of features. Gao, X., Loomes, M., Comley, R., Muller, H. and Luo, S. (ed.) International Conference on Medical Imaging and Informatics (MIMI 2007). Beijing, China 14 - 16 Aug 2007 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-540-79490-5_25
Prototype system for semantic retrieval of neurological PET images
Batty, S., Clark, J., Fryer, T. and Gao, X. 2008. Prototype system for semantic retrieval of neurological PET images. Gao, X., Muller, H., Loomes, M., Comley, R. and Luo, S. (ed.) International Conference on Medical Imaging and Informatics (MIMI 2007). Beijing, China 14 - 16 Aug 2007 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-540-79490-5_23
Colour vision model-based approach for segmentation of traffic signs
Gao, X., Hong, K., Passmore, P., Podladchikova, L. and Shaposhnikov, D. 2008. Colour vision model-based approach for segmentation of traffic signs. EURASIP Journal on Image and Video Processing. 2008. https://doi.org/10.1155/2008/386705
Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach
Anishenko, S., Osimov, V., Shaposhnikov, D., Podladchikova, L., Comley, R. and Gao, X. 2008. Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach. Puuronen, S., Pechenizkiy, M., Tsymbal, A. and Lee, D. (ed.) 21st IEEE International Symposium on Computer-Based Medical Systems. Jyvaskyla, Finland 17 - 19 Jun 2008 IEEE Computer Society. pp. 50-52 https://doi.org/10.1109/CBMS.2008.19
Detection of head motions using a vision model
Gao, X., Shaposhnikov, D., Podladchikova, L., Batty, S. and Clark, J. 2007. Detection of head motions using a vision model. Bashshur, R. (ed.) 3rd IASTED International Conference on Telehealth. Montreal, QC, Canada 31 May - 01 Jun 2007 Anaheim, CA Acta Press. pp. 167-171
Recognition of traffic signs based on their colour and shape features extracted using human vision models
Gao, X., Podladchikova, L., Shaposhnikov, D., Hong, K. and Shevtsova, N. 2006. Recognition of traffic signs based on their colour and shape features extracted using human vision models. Journal of Visual Communication and Image Representation. 17 (4), pp. 675-685. https://doi.org/10.1016/j.jvcir.2005.10.003
Colour management in telemedicine
Gao, X. 2004. Colour management in telemedicine. Hamza, M. (ed.) 7th IASTED International Conference on Computer Graphics and Imaging. Kauai, Hawaii, United States 16 - 18 Aug 2004 Anaheim, CA Acta Press. pp. 361-364
Application of vision models to traffic sign recognition
Gao, X., Shaposhnikov, D. and Podladchikova, L. 2004. Application of vision models to traffic sign recognition. Kaynak, O., Alpaydin, E., Oja, E. and Xu, L. (ed.) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Istanbul, Turkey 26 - 29 Sep 2003 Berlin, Heidelberg Springer. https://doi.org/10.1007/3-540-44989-2_131
Towards content-based retrieval for wallpaper images
Qian, Y., Tully, C., Hendon, Z. and Gao, X. 2003. Towards content-based retrieval for wallpaper images. 7th IASTED International Conference on Computer Graphics and Imaging. Kauai, Hawaii, United States 16 - 18 Aug 2004 pp. 305-309
Vision models based identification of traffic signs
Gao, X., Podladchikova, L., Shaposhnikov, D., Shevtsova, N., Hong, K., Batty, S., Golovan, A. and Gusakova, V. 2002. Vision models based identification of traffic signs. 1st European Conference on Colour Graphics, Imaging, and Vision. University of Poitiers, France 02 - 05 Apr 2002 Society for Imaging Science and Technology.
Content based retrieval of lesioned brain images
Batty, S., Blandford, A., Clark, J., Fryer, T. and Gao, X. 2002. Content based retrieval of lesioned brain images. Siegel, E. and Huang, H. (ed.) SPIE Medical Imaging 2002. San Diego, California, United States 23 - 28 Feb 2002 Bellingham Society of Photo-optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.466997
A method of vessel tracking for vessel diameter measurement on retinal images
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2001. A method of vessel tracking for vessel diameter measurement on retinal images. 2001 International Conference on Image Processing. Thessaloniki, Greece 07 - 10 Oct 2001 IEEE.
Computer algorithms for the automated measurement of retinal arteriolar diameters
Chapman, N., Witt, N., Gao, X., Bharath, A., Stanton, A., Thom, S. and Hughes, A. 2001. Computer algorithms for the automated measurement of retinal arteriolar diameters. British Journal of Ophthalmology. 85 (1), pp. 74-79. https://doi.org/10.1136/bjo.85.1.74
Quantification and characterization of arteries in retinal images
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2000. Quantification and characterization of arteries in retinal images. Computer Methods and Programs in Biomedicine. 63 (2), pp. 133-146. https://doi.org/10.1016/S0169-2607(00)00082-1