Research Papers: Imaging

Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch

[+] Author Affiliations
Paritosh Pande, Sebina Shrestha, Jesung Park, Michael J. Serafino, Brian E. Applegate, Javier A. Jo

Texas A&M University, Department of Biomedical Engineering, College Station, Texas 77843, United States

Irma Gimenez-Conti, Jimi Brandon

University of Texas M.D. Anderson Cancer Center, Department of Carcinogenesis, Smithville, Texas 78957, United States

Yi-Shing Cheng

Texas A&M University Health Science Center—Baylor College of Dentistry, Department of Diagnostic Sciences, 3302 Gaston Avenue, Dallas, Texas 75246, United States

J. Biomed. Opt. 19(8), 086022 (Aug 27, 2014). doi:10.1117/1.JBO.19.8.086022
History: Received May 7, 2014; Revised August 7, 2014; Accepted August 8, 2014
Text Size: A A A

Abstract.  Most studies evaluating the potential of optical coherence tomography (OCT) for the diagnosis of oral cancer are based on visual assessment of OCT B-scans by trained experts. Human interpretation of the large pool of data acquired by modern high-speed OCT systems, however, can be cumbersome and extremely time consuming. Development of image analysis methods for automated and quantitative OCT image analysis could therefore facilitate the evaluation of such a large volume of data. We report automated algorithms for quantifying structural features that are associated with the malignant transformation of the oral epithelium based on image processing of OCT data. The features extracted from the OCT images were used to design a statistical classification model to perform the automated tissue diagnosis. The sensitivity and specificity of distinguishing malignant lesions from benign lesions were found to be 90.2% and 76.3%, respectively. The results of the study demonstrate the feasibility of using quantitative image analysis algorithms for extracting morphological features from OCT images to perform the automated diagnosis of oral malignancies in a hamster cheek pouch model.

Figures in this Article
© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Paritosh Pande ; Sebina Shrestha ; Jesung Park ; Michael J. Serafino ; Irma Gimenez-Conti, et al.
"Automated classification of optical coherence tomography images for the diagnosis of oral malignancy in the hamster cheek pouch", J. Biomed. Opt. 19(8), 086022 (Aug 27, 2014). ; http://dx.doi.org/10.1117/1.JBO.19.8.086022


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.