Entropy analysis of OCT signal for automatic tissue characterization

Document Type

Conference Proceeding

Publication Date

7-18-2016

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.

Identifier

84982095358 (Scopus)

ISBN

[9781628419542]

Publication Title

Proceedings of SPIE the International Society for Optical Engineering

External Full Text Location

https://doi.org/10.1117/12.2212587

e-ISSN

1996756X

ISSN

0277786X

Volume

9720

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