J Periodontal Implant Sci.  2017 Feb;47(1):41-50. 10.5051/jpis.2017.47.1.41.

Automatic detection of tooth cracks in optical coherence tomography images

• Kim JM
• Kang SR
• Yi WJ

• KMID: 2368886
• DOI: http://doi.org/10.5051/jpis.2017.47.1.41

Abstract

PURPOSE
The aims of the present study were to compare the image quality and visibility of tooth cracks between conventional methods and swept-source optical coherence tomography (SS-OCT) and to develop an automatic detection technique for tooth cracks by SS-OCT imaging.
METHODS
We evaluated SS-OCT with a near-infrared wavelength centered at 1,310 nm over a spectral bandwidth of 100 nm at a rate of 50 kHz as a new diagnostic tool for the detection of tooth cracks. The reliability of the SS-OCT images was verified by comparing the crack lines with those detected using conventional methods. After performing preprocessing of the obtained SS-OCT images to emphasize cracks, an algorithm was developed and verified to detect tooth cracks automatically.
RESULTS
The detection capability of SS-OCT was superior or comparable to that of trans-illumination, which did not discriminate among the cracks according to depth. Other conventional methods for the detection of tooth cracks did not sense initial cracks with a width of less than 100 μm. However, SS-OCT detected cracks of all sizes, ranging from craze lines to split teeth, and the crack lines were automatically detected in images using the Hough transform.
CONCLUSIONS
We were able to distinguish structural cracks, craze lines, and split lines in tooth cracks using SS-OCT images, and to automatically detect the position of various cracks in the OCT images. Therefore, the detection capability of SS-OCT images provides a useful diagnostic tool for cracked tooth syndrome.

Keyword

Computer-assisted image interpretation; Cracked tooth syndrome; Tooth fractures; Optical coherence tomography

MeSH Terms

Cracked Tooth Syndrome
Image Interpretation, Computer-Assisted
Tomography, Optical Coherence*
Tooth Fractures
Tooth*

Figure

• 

  Figure 1 The 4 imaging techniques used in the study. (A) Trans-illumination. A strong light is shone on one side of a tooth and the tooth image is acquired on the other side to identify cracks. (B) An intraoral X-ray is used to acquire a 2-dimensional image of a tooth using radiation. (C) CBCT is used to reconstruct a 3-dimensional image through image processing after obtaining radiographs from various angles. (D) OCT is used to acquire a cross-sectional image of a tooth using an infrared ray of 1,310 nm. CBCT, cone-beam computed tomography; OCT, optical coherence tomography; CCD, charge-coupled detector.

• 

  Figure 2 Crack-induced tooth sample. (A) Black arrow indicates the crack. (B) Cracks are difficult to observe with the naked eye.

• 

  Figure 3 Results of 4 imaging techniques. The red line indicates the cross-section of CBCT and the OCT scan line. Red and blue arrows indicate crack lines. The blue circle is a false-positive crack line. E, D, and the DEJ are clearly distinguished in the OCT images. (A, E, I) Trans-illumination images. (B, F, J) Intraoral radiography images. (C, G, K) CBCT images. (D, H, L) OCT images. CBCT, cone-beam computed tomography; E, enamel; D, dentine; DEJ, dentine-enamel junction; OCT, optical coherence tomography.

• 

  Figure 4 (A, D, G, J, M, and P) Single OCT images showing high noise. (B, E, H, K, N, and Q) OCT images were acquired 100 times, indicating that the noise was improved by image averaging. (C, F, I, L, O, and R) After averaging, crack lines were detected with the Hough transform. OCT, optical coherence tomography.

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