Conference proceeding
Stochastic decomposition method for detection of epithelium dysplasia and inflammation using white light spectroscopy imaging
2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15, 5818
01 Jan 2006
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Abstract
In this paper, we present a stochastic decomposition method (SDM) that allows the detection of dysplasia in epithelial tissue using white-light spectroscopy imaging. The main goal is to extract the data from the decomposition which will lead to the construction of a feature parameter space corresponding to changes in the tissue morphology related to formation of dysplasia and inflammation. These parameters include the number and mean energy of coherent scatterers; deviation from Rayleigh scattering; residual error variance of the diffuse component; and normalized correlation coefficient. The tests are performed on tissue-mimicking phantom data and tissue data collected from mouse colon in vitro. The obtained results demonstrate effectiveness of the method in differentiating between tissue structures with different cell morphologies. The results are shown by fusing all the estimated parameter set together and also using each parameter separately. Combination of all the features results in an Az value higher than 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.
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Details
- Title
- Stochastic decomposition method for detection of epithelium dysplasia and inflammation using white light spectroscopy imaging
- Creators
- Ezgi Taslidere - Drexel Univ, Dept Elect & Comp Engn, Philadelphia, PA 19104 USAFernand S. Cohen - Drexel UniversityIEEE
- Publication Details
- 2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15, 5818
- Conference
- 2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, 28th
- Publisher
- IEEE
- Number of pages
- 2
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Identifiers
- 991019170503404721
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- Web of Science research areas
- Engineering, Biomedical