Logo image
Tissue characterization and detection of dysplasia using scattered light
Conference proceeding

Tissue characterization and detection of dysplasia using scattered light

Fernand S. Cohen, Ezgi Taslidere, Dilip S. Hari and IEEE
2006 3RD IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: MACRO TO NANO, VOLS 1-3, v 3, pp 590-593
01 Jan 2006

Abstract

Imaging Science & Photographic Technology Life Sciences & Biomedicine Radiology, Nuclear Medicine & Medical Imaging Science & Technology Technology
In this paper, the structural parameters of dysplasia formation in the epithelial tissue are estimated using a stochastic decomposition algorithm (SDM) by means of scattered light. We extract texture parameters obtained from the decomposition that capture the signature of dysplasia formation. These parameters include the number and mean energy of coherent scatterers; deviation from Rayleigh scattering; average energy of diffuse scatterers; and normalized correlation coefficient. The tests are performed on simulations, and tissue-mimicking phantom data. The simulations are based on the light scattered from the cells with varying parameters such as, index of refraction, number of cells, and size of cells. The obtained results demonstrate the proof-of-concept in being able to differentiate between tissue structures that give rise to changes in cell morphology as well as other physical properties such as change in index of refraction. Fusing all the estimated parameter set together results in the differentiation performance (Az value) up to 1(perfect detection) for simulated data, and Az > 0.927 for the phantom data.

Metrics

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Web of Science research areas
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Logo image