Near-infared hyperspectral imaging of teeth for dental caries detection

Zakian, Christian; Pretty, Iain A.; Ellwood, R.P.

doi:10.1117/1.3275480

Cited by 95 publications

(117 citation statements)

References 29 publications

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“…Recent advances in sensor instrumentation have facilitated the use of hyperspectral sensors for numerous scientific fields, including hyperspectral imaging for biomedical applications [18]. The combination of supercontinuum laser sources with hyperspectral sensors are of rising interest for numerous applications [19].…”

Section: Optical Setupmentioning

confidence: 99%

Spectral degree of linear polarization of light from healthy skin and melanoma

Ceolato¹,

Golzio²,

Riou³

et al. 2015

Opt. Express

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A non-invasive optical technique, based on a supercontinuum laser source and hyperspectral sensors, is established to measure the spectral degree of linear polarization (DOLP) in a broad spectral range from 525 nm to 1000 nm. Several biomaterials of interest, such as healthy and cancerous skins, are considered. The spectral DOLP of melanoma, from 5 mm to 9 mm diameter, are measured and analyzed. An increase of the spectral DOLP is reported for 100% of the melanoma samples compared to healthy skin samples. The spectral DOLP of a given melanoma appears to be correlated to the stage of its development: the larger the melanoma, the higher the DOLP. Such trend could be explained by a decrease of the surface roughness along the evolution of the disease. In addition, a significant spectral dependence of the DOLP is reported for melanoma samples as it exhibits a decrease in the near infrared from 750 nm to 1000 nm.

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Section: Optical Setupmentioning

confidence: 99%

Spectral degree of linear polarization of light from healthy skin and melanoma

Ceolato¹,

Golzio²,

Riou³

et al. 2015

Opt. Express

View full text Add to dashboard Cite

show abstract

“…5 Near-infrared (NIR) imaging has been shown to detect and quantify dental caries. 7,8 Also, both thermal and NIR reflectance imaging have been found to be suitable for the detection of remineralization of simulated caries lesions. 9 The use of optical coherence tomography (OCT) for caries detection and quantification has also shown promising results.…”

Section: Introductionmentioning

confidence: 99%

Pilot clinical study to assess caries lesion activity using quantitative light-induced fluorescence during dehydration

et al. 2017

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"Pilot clinical study to assess caries lesion activity using quantitative light-induced fluorescence during dehydration," J. Biomed. Opt. Abstract. This study aimed to evaluate the ability of quantitative light-induced fluorescence (QLF) to assess caries lesion activity using visual examination (VE) as the gold standard. Twenty-four visible white spot lesions on buccal surfaces were examined from 23 children, ages 9 to 14 years. At baseline, the surface was hydrated with water, and thereafter, it was dehydrated with continuous compressed air during image acquisition. QLF images were acquired at 0 (baseline), 5, and 15 s. QLF variables [QLF V : fluorescence loss (ΔF), lesion size (S), ΔQ: ΔF × S] was recorded. Changes-in-QLF V per second (ΔQLF V ) were determined: ΔQLF V ¼ ðQLF VN − QLF VBaseline Þ∕N, where N indicates dehydration time. One experienced dentist conducted VE independently using a dental unit's light, compressed air, and explorer. QLF V and ΔQLF V of the active group (n ¼ 11) were compared with those of the inactive group (n ¼ 13) using two-sample t -tests. As the surface was dehydrated, S and ΔQ values of the active group increased, whereas QLF V of the inactive group showed only a small change. ΔQLF V of the active group were larger than those of the inactive group; however, the difference did not reach statistical significance (p > 0.11). Within the limitations of this study, QLF data indicated increments for lesions designated as active and minimal change for lesions defined as inactive.

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“…The captured image from the hyperspectral camera is an intensity map of the examined sample as a function of the position and caries degree status, where the dark areas represent the affected areas of the teeth, while the brighter (high intensity) areas show the sound areas of the teeth [29]. Furthermore, such raw image needs to be handled by an image processing technique to classify the caries degree status embedded in the cavitated tooth.…”

Section: Discussionmentioning

confidence: 99%

Classification of human teeth caries using custom non-invasive optical imaging system

2017

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Purpose Caries status detection and classification in its early stages is an essential process for a precise, less pain caries removal procedure. The aim of this work was to reveal and classify different caries degree status via an optical imaging system interfaced with Pc, and controlled by an inspection algorithm using DSP 6 software. Methods This work presents a 635-nm He-Ne laser-tissue interaction mechanism for human teeth characterization. Spectroscopic measurement for both sound and caries teeth lesion area was used to estimate the averaged optical properties. One-layer Monte Carlo model is implemented for sound, and caries lesion separately to optimize the source detector position with respect to the sample. Captured images were further processed by the designed inspection algorithm based on a Hilbert transform edge detection technique. Results The proposed optical imaging system was able to discriminate between the examined 12 tooth samples into sound and caries according to optical properties calculated from the spectroscopy measurements, where the reflectance, transmittance, and absorbance of the sound tooth was T n = 68%, R n = 23%, and A n = 0.47 o.d., respectively. In contrast, for caries values, T c reached 30%, R c = 5.2%, and A c = 1.6 o.d. Caries degrees inside cavity lesion were classified by the designed inspection algorithm into sound, moderate, and severe degree. Conclusions This study reveals the ability of the real-time, non-invasive custom optical imaging system to differentiate between sound and caries lesion teeth, in addition to classifying caries degree from images processed by the 2-D Hilbert transform inspection algorithm.

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Near-infared hyperspectral imaging of teeth for dental caries detection

Cited by 95 publications

References 29 publications

Spectral degree of linear polarization of light from healthy skin and melanoma

Spectral degree of linear polarization of light from healthy skin and melanoma

Pilot clinical study to assess caries lesion activity using quantitative light-induced fluorescence during dehydration

Classification of human teeth caries using custom non-invasive optical imaging system

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