Can we see epithelium tissue structure below the surface using an optical probe?
- Fernand S. CohenAffiliated withDepartment of Electrical and Computer Engineering, Drexel University
- , Ezgi TaslidereAffiliated withDepartment of Electrical and Computer Engineering, Drexel University Email author
- , Sreekant MurthyAffiliated withDivision of Gastroenterology and Hepatology, College of Medicine, Drexel University
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This paper answers the question of whether it is possible to detect changes below the surface in epithelium layered structures using a Stochastic Decomposition Method (SDM) that models the scattered light reflected from the layered structure over an area (2-D scan) illuminated by an optical sensor (fibre) emitting light at either one wavelength or with white light. Our technique correlates the differential changes in the reflected tissue texture with the morphological and physical changes that occur in the tissue occurring inside the structure. This work has great potential for detecting changes in mucosal structures and may lead to enhanced endoscopy when the disease is developing to the outside of the mucosal structure and hence becoming hidden during colonoscopy or endoscopic examination. Tests are performed on layered tissue phantoms, and the results obtained show great effectiveness of the model and method in picking up changes in the morphology of the layered tissue phantoms occurring below the surface. We also establish the robustness of the model to changes in viewing depth by testing it on phantoms viewed at different depths. We show that the model is robust to within a 4-mm-deep viewing range.
KeywordsBiological signal processing Optical imaging Pattern recognition Optical signal processing Stochastic analysis Spectral analysis Computer-aided diagnosis Modelling biomedical systems
- Can we see epithelium tissue structure below the surface using an optical probe?
Medical & Biological Engineering & Computing
Volume 49, Issue 1 , pp 85-96
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- Biological signal processing
- Optical imaging
- Pattern recognition
- Optical signal processing
- Stochastic analysis
- Spectral analysis
- Computer-aided diagnosis
- Modelling biomedical systems
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