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Proceedings Paper

Entropy analysis of OCT signal for automatic tissue characterization
Author(s): Yahui Wang; Yi Qiu; Farzana Zaki; Yiqing Xu; Basil Hubbi; Kevin D. Belfield; Xuan Liu
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Paper Abstract

Optical coherence tomography (OCT) signal can provide microscopic characterization of biological tissue and assist clinical decision making in real-time. However, raw OCT data is noisy and complicated. It is challenging to extract information that is directly related to the pathological status of tissue through visual inspection on huge volume of OCT signal streaming from the high speed OCT engine. Therefore, it is critical to discover concise, comprehensible information from massive OCT data through novel strategies for signal analysis. In this study, we perform Shannon entropy analysis on OCT signal for automatic tissue characterization, which can be applied in intraoperative tumor margin delineation for surgical excision of cancer. The principle of this technique is based on the fact that normal tissue is usually more structured with higher entropy value, compared to pathological tissue such as cancer tissue. In this study, we develop high-speed software based on graphic processing units (GPU) for real-time entropy analysis of OCT signal.

Paper Details

Date Published: 7 March 2016
PDF: 7 pages
Proc. SPIE 9720, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management, 972018 (7 March 2016); doi: 10.1117/12.2212587
Show Author Affiliations
Yahui Wang, New Jersey Institute of Technology (United States)
Yi Qiu, New Jersey Institute of Technology (United States)
Farzana Zaki, New Jersey Institute of Technology (United States)
Yiqing Xu, New Jersey Institute of Technology (United States)
Basil Hubbi, New Jersey Medical School (United States)
Kevin D. Belfield, New Jersey Institute of Technology (United States)
Xuan Liu, New Jersey Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9720:
High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management
Kevin K. Tsia; Keisuke Goda, Editor(s)

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