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• W3211107601 abstract "Clinically interesting low-contrast dental and oral features can be challenging to detect. In visual observation and clinical photographs, identification of low-contrast features can be hard or even impossible. Imaging methods, e.g., X-ray and magnetic resonance imaging, provide more information but often require use of ionizing radiation, expensive equipment, and specialized personnel to operate the devices. A cost-effective, non-ionizing, contrast-enhancing imaging method that can be used at any dental clinic is in great demand. Here we show a dental and oral feature visibility-enhancement based on a portable spectral camera and computational filters derived from principal component analysis. By applying computational filters on oral and dental spectral images, selected features of clinical interest can be highlighted against their surroundings. Due to the lack of information available in standard color images, this visibility-enhancement technique can only be realized using spectral images. Oral and dental spectral imaging does not use ionizing radiation, and modern spectral cameras are small, portable, and can be used without specialized training. In this paper, spectral image-based visibility-enhancement is demonstrated for the following cases: gingival recession, calculus, gingivitis, root caries, secondary caries, Fordyce’s granules, leukoplakia, and pigmentous lesions. The results gained with spectral images and computational filters from principal component analysis are compared against regular color images and grayscale images computed with band-pass filters from our earlier work. The results are promising as the visibility and contrast of the features of interests are enhanced in all the studied cases. This study provides a starting point for future research and demonstrates the applicability of spectral imaging-based methods for practical use at dental clinics." @default.
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• W3211107601 date "2021-01-01" @default.
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• W3211107601 title "Computational Filters for Dental and Oral Lesion Visualization in Spectral Images" @default.
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• W3211107601 doi "https://doi.org/10.1109/access.2021.3121815" @default.
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