An Averaged Intensity Difference Detection Algorithm for Identification of Human Gingival Sulcus in Optical Coherence Tomography Images

Naresh Kumar Ravichandran; Hoseong Cho; Jaeyul Lee; Sangyeob Han; Ruchire Eranga Wijesinghe; Pilun Kim; Jae-Won Song; Mansik Jeon; Jeehyun Kim

doi:10.1109/ACCESS.2019.2920146

IEEE Access (Jan 2019)

An Averaged Intensity Difference Detection Algorithm for Identification of Human Gingival Sulcus in Optical Coherence Tomography Images

Naresh Kumar Ravichandran,
Hoseong Cho,
Jaeyul Lee,
Sangyeob Han,
Ruchire Eranga Wijesinghe,
Pilun Kim,
Jae-Won Song,
Mansik Jeon,
Jeehyun Kim

Affiliations

Naresh Kumar Ravichandran: School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea
Hoseong Cho: School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea
Jaeyul Lee: School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea
Sangyeob Han: School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea
Ruchire Eranga Wijesinghe: Department of Biomedical Engineering, College of Engineering, Kyungil University, Gyeongsangbuk-do, South Korea
Pilun Kim: Institute of Biomedical Engineering, Kyungpook National University, Daegu, South Korea
Jae-Won Song: School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea
Mansik Jeon: ORCiD; School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea
Jeehyun Kim: School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea

DOI: https://doi.org/10.1109/ACCESS.2019.2920146
Journal volume & issue: Vol. 7
pp. 73076 – 73084

Abstract

Read online

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.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

About the journal

Abstract

Keywords