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  4. Delineation of an oral cancer lesion with swept-source optical coherence tomography
 
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Delineation of an oral cancer lesion with swept-source optical coherence tomography

Journal
Journal of Biomedical Optics
Journal Volume
13
Journal Issue
4
Date Issued
2008
Author(s)
Tsai M.-T.
HSIANG-CHIEH LEE  
Lu C.-W.
Wang Y.-M.
Lee C.-K.
Yang C.C.
CHUN-PIN CHIANG  
DOI
10.1117/1.2960632
URI
https://www.scopus.com/inward/record.uri?eid=2-s2.0-58149376555&doi=10.1117%2f1.2960632&partnerID=40&md5=6e880770f2cb7935c7d8c57cd07da88c
https://scholars.lib.ntu.edu.tw/handle/123456789/517393
Abstract
We demonstrate the ex vivo imaging of an oral cancerous sample with a swept-source optical coherence tomography (SS-OCT) system. With the axial resolution of 8μm in free space and system sensitivity of 108dB, we can well distinguish the normal and abnormal tissue portions in a sample. In particular, we analyze the lateral variation of A-scan profiles to show two parameters of SS-OCT signal for delineating an oral cancer lesion. One of the parameters is the decay constant in the exponential fitting of the SS-OCT signal intensity along depth. This decay constant decreases as the A-scan point moves laterally across the margin of a lesion. The other parameter is the standard deviation of the SS-OCT signal intensity fluctuation in an A-scan. This parameter increases significantly when the A-scan point is moved across the transition region between the normal and abnormal portions. Such parameters are useful for determining the margins of oral cancer. ? 2008 Society of Photo-Optical Instrumentation Engineers.
SDGs

[SDGs]SDG3

Other Subjects
Abnormal tissues; Axial resolutions; biomedical optics; Coherent optical systems; Decay constants; Ex vivo imaging; Exponential fitting; Free space; Lateral variations; Oral cancer; Point moves; Signal intensities; Standard deviation; Swept source; System sensitivity; Transition regions; Two parameter; Optical systems; Tissue; Tomography; Diseases; algorithm; article; artificial intelligence; automated pattern recognition; computer assisted diagnosis; human; image enhancement; instrumentation; methodology; mouth tumor; optical coherence tomography; pathology; reproducibility; sensitivity and specificity; Algorithms; Artificial Intelligence; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Mouth Neoplasms; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Tomography, Optical Coherence

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