Paper Info
Title
An averaged intensity difference detection algorithm for identification of human gingival sulcus in optical coherence tomography images
Abstract
In the past decade, there has been an increase in the development of sensitive, high-resolution, non-invasive diagnostic methods for periodontic diseases. Optical coherence tomography (OCT) has attracted considerable attention in clinical settings. In this paper, a reliable, robust algorithm for the detection of gingival sulcus in 2D OCT cross-sectional images is proposed. Previously, the measurement of gingival sulcus in OCT images has been performed by manual identification using two-dimensional (2D) cross-sectional images. The automated detection of gingival sulcus continuity in 2D OCT images may help medical practitioners to assess important features of gingival tissues. The Sobel and canny operators have mainly been used for boundary and edge detection in OCT images. However, these algorithms are highly sensitive to noise and speckle in OCT images. To overcome these limitations, we propose an algorithm for the quantitative depth measurement of the human gingival sulcus, based on averaged intensity difference. In this paper, we utilized two commercially-available swept-source OCT systems operating at center wavelengths of 1310 and 1060 nm to image gingival sulcus of human samples in vivo. The images were processed using three algorithms: canny, Sobel, and averaged intensity difference.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2920146
IEEE ACCESS
Keywords
Field
DocType
Biomedical optical imaging,detection algorithms,optical coherence tomography
Optical reflection,Optical coherence tomography,Speckle pattern,Edge detection,Computer science,Gingival sulcus,Algorithm,Sobel operator,Interferometry,Measured depth
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Naresh Kumar Ravichandran111.83
Hoseong Cho200.34
Jaeyul lee300.34
Sangyeob Han401.35
Ruchire Eranga Wijesinghe555.19
Pil Un Kim623.75
Jaewon Song7113.68
Mansik Jeon856.12
Jeehyun Kim9138.21