Journal article
Stochastic decomposition method for modeling the scattered signal reflected of mucosal tissues
Journal of biomedical optics, v 13(5), pp 054039-0540314
Sep 2008
PMID: 19021419
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The aim of this work is to draw the attention of the biophotonics community to a stochastic decomposition method (SDM) to potentially model 2-D scans of light scattering data from epithelium mucosa tissue. The emphasis in this work is on the proposed model and its theoretical pinning and foundation. Unlike previous works that analyze scattering signal at one spot as a function of wavelength or angle, our method statistically analyzes 2-D scans of light scattering data over an area. This allows for the extraction of texture parameters that correlate with changes in tissue morphology, and physical characteristics such as changes in absorption and scattering characteristics secondary to disease, information that could not be revealed otherwise. The method is tested on simulations, phantom data, and on a limited preliminary in-vitro animal experiment to track mucosal tissue inflammation over time, using the area Az under receiver operating characteristics (ROC) curve as a performance measure. Combination of all the features results in an Az value up to 1 for the simulated data, and Az > 0.927 for the phantom data. For the tissue data, the best performances for differentiation between pairs of various levels of inflammation are 0.859, 0.983, and 0.999.
Metrics
Details
- Title
- Stochastic decomposition method for modeling the scattered signal reflected of mucosal tissues
- Creators
- Fernand S Cohen - Drexel University, Electrical and Computer Engineering Department, Philadelphia, Pennsylvania 19104, USAEzgi TaslidereDilip S HariSreekant Murthy
- Publication Details
- Journal of biomedical optics, v 13(5), pp 054039-0540314
- Publisher
- Society of Photo-Optical Instrumentation Engineers (SPIE); United States
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000261764900048
- Scopus ID
- 2-s2.0-60849124550
- Other Identifier
- 991014878406704721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Biochemical Research Methods
- Optics
- Radiology, Nuclear Medicine & Medical Imaging