Computational Filters for Dental and Oral Lesion Visualization in Spectral Images

Joni Hyttinen,Markku Hauta-Kasari,Heli Jasberg,Pauli Falt,Arja Kullaa

doi:10.1109/access.2021.3121815

Joni Hyttinen, Markku Hauta-Kasari + Show 3 more

Open Access

https://doi.org/10.1109/access.2021.3121815

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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.

Highlights

Caries, the most prevalent chronic endemic disease, and periodontitis are inflammatory diseases considered as a major cause of tooth loss
We applied Principal component analysis (PCA) on sets of spectra collected from spectral images based on manually segmented classes
Class pairs like Enamel–Calculus, Enamel–Root, Enamel– Attrition/Erosion, and Oral mucosa–Ulcer were used as the basis of the analysis

Summary

Introduction

The most prevalent chronic endemic disease, and periodontitis are inflammatory diseases considered as a major cause of tooth loss. The chronic nature of these diseases manifests from slow lesion progression. The associate editor coordinating the review of this manuscript and approving it for publication was Jingang Jiang. The standard diagnostic approach in clinical and radiological investigations of both caries and periodontal diseases rely on visual and morphological changes associated with these diseases, which makes early prediction challenging. The current diagnostic techniques for caries are unable to detect the lesions until they are relatively well advanced and involve one-third or more of the thickness of the enamel. The slow progression of caries lesions offers a window of opportunity for intervention to reverse the loss of minerals or arrest lesion progression

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