journal article·2020-10-16Open access

Oral and Dental Spectral Image Database—ODSI-DB

Joni Hyttinen, Pauli Fält, Heli Jäsberg, Arja Kullaa, Markku Hauta-Kasari

Applied Sciences· Volume 10· Issue 20

Abstract

The most common imaging methods used in dentistry are X-ray imaging and RGB color photography. However, both imaging methods provide only a limited amount of information on the wavelength-dependent optical properties of the hard and soft tissues in the mouth. Spectral imaging, on the other hand, provides significantly more information on the medically relevant dental and oral features (e.g. caries, calculus, and gingivitis). Due to this, we constructed a spectral imaging setup and acquired 316 oral and dental reflectance spectral images, 215 of which are annotated by medical experts, of 30 human test subjects. Spectral images of the subjects’ faces and other areas of interest were captured, along with other medically relevant information (e.g., pulse and blood pressure). We collected these oral, dental, and face spectral images, their annotations and metadata into a publicly available database that we describe in this paper. This oral and dental spectral image database (ODSI-DB) provides a vast amount of data that can be used for developing, e.g., pattern recognition and machine vision applications for dentistry.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Optical Imaging and Spectroscopy Techniques · Dental Radiography and Imaging · Dental Research and COVID-19

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Computing, University of Eastern Finland, Yliopistokatu 2, P.O. Box 111, 80101 Joensuu, Finland

Institute of Dentistry, University of Eastern Finland, Yliopistonranta 1 C, P.O. Box 1627, 70211 Kuopio, Finland

Funding

Business Finland and European Regional Development Fund (ERDF)

References (25)

Show source-provided references
1. Digital Dental X-ray Database for Caries Screening10.1007/s13319-016-0096-5 ↗
2. Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives10.1016/j.eswa.2018.04.001 ↗
3. Dental diagnosis from X-Ray images: An expert system based on fuzzy computing10.1016/j.bspc.2017.07.005 ↗
4. Cui, Z., Li, C., and Wang, W. (2019, January 16–20). ToothNet: Automatic Tooth Instance Segmentation and Identification From Cone Beam CT Images. Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00653 ↗
5. Spectral imaging: Principles and applications10.1002/cyto.a.20311 ↗
6. Review of spectral imaging technology in biomedical engineering: Achievements and challenges10.1117/1.JBO.18.10.100502 ↗
7. Mansouri, A., el Moataz, A., Nouboud, F., and Mammass, D. (2018). Spectral Image Enhancement for the Visualization of Dental Lesions. Image and Signal Processing, Springer International Publishing. Lecture Notes in Computer Science.10.1007/978-3-319-94211-7 ↗
8. Mansouri, A., el Moataz, A., Nouboud, F., and Mammass, D. (2018). Contrast Enhancement of Dental Lesions by Light Source Optimisation. Image and Signal Processing, Springer International Publishing. Lecture Notes in Computer Science.10.1007/978-3-319-94211-7 ↗
9. Optical implementation of partially negative filters using a spectrally tunable light source, and its application to contrast enhanced oral and dental imaging10.1364/OE.27.034022 ↗
10. Bauer, J.R., Thomas, J.B., Hardeberg, J.Y., and Verdaasdonk, R.M. (2019). An Evaluation Framework for Spectral Filter Array Cameras to Optimize Skin Diagnosis. Sensors, 19.10.3390/s19214805 ↗
11. Boiko, O., Hyttinen, J., Fält, P., Jäsberg, H., Mirhashemi, A., Kullaa, A., and Hauta-Kasari, M. (2019, January 21–25). Deep Learning for Dental Hyperspectral Image Analysis. Proceedings of the 27th Color and Imaging Conference Final Program and Proceedings, Paris, France.10.2352/issn.2169-2629.2019.27.53 ↗
12. Spectral imaging of the retina10.1038/eye.2010.222 ↗
13. Pourreza-Shahri, R., Saki, F., Kehtarnavaz, N., Leboulluec, P., and Liu, H. (2013, January 15–18). Classification of ex-vivo breast cancer positive margins measured by hyperspectral imaging. Proceedings of the 2013 IEEE International Conference on Image Processing, Melbourne, Victoria, Australia.10.1109/ICIP.2013.6738289 ↗
14. Tongue Tumor Detection in Medical Hyperspectral Images10.3390/s120100162 ↗
15. Fabelo, H., Ortega, S., Ravi, D., Kiran, B.R., Sosa, C., Bulters, D., Callicó, G.M., Bulstrode, H., Szolna, A., and Piñeiro, J.F. (2018). Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations. PLoS ONE, 13.10.1371/journal.pone.0193721 ↗
16. In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection10.1109/ACCESS.2019.2904788 ↗
17. Quantitative analysis of multi-spectral fundus images10.1016/j.media.2006.05.007 ↗
18. The OpenCV Library
19. Registration of Challenging Image Pairs: Initialization, Estimation, and Decision10.1109/TPAMI.2007.1116 ↗
20. Behmann, J., Acebron, K., Emin, D., Bennertz, S., Matsubara, S., Thomas, S., Bohnenkamp, D., Kuska, M.T., Jussila, J., and Salo, H. (2018). Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection. Sensors, 18.10.3390/s18020441 ↗
21. (2020, August 24). Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Available online: https://creativecommons.org/licenses/by-nc-sa/4.0/.
22. Beeckman, J., Neyts, K., and Vanbrabant, P.J.M. (2011). Liquid-crystal photonic applications. Opt. Eng., 50.10.1117/1.3565046 ↗
23. (2020, August 19). Thorlabs OSL2BIR-Product Page with Specifications. Available online: https://www.thorlabs.com/thorproduct.cfm?partnumber=OSL2BIR.
24. The evolution of human skin coloration10.1006/jhev.2000.0403 ↗
25. The Absorption Spectra of Hemoglobin and Its Derivatives in the Visible and Near Infra-Red Regions10.1016/S0021-9258(18)72329-6 ↗