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2 March 2006 Improved diagnostics using polarization imaging and artificial neural networks
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In recent years there has been an increasing interest in studying the propagation of polarized light in randomly scattering media. This paper presents a novel approach for cell and tissue imaging by using full Stokes imaging and for its improved diagnostics by using artificial neural networks (ANNs). Phantom experiments have been conducted using a prototyped Stokes polarization imaging device. Several types of phantoms, consisting of polystyrene latex spheres in various diameters, were prepared to simulate different conditions of epidermal layer of skin. Several sets of four images that contain not only the intensity, but also the polarization information were taken for analysis. Wavelet transforms are first applied to the Stokes components for initial feature analysis and extraction. Artificial neural networks (ANNs) are then used to extract diagnostic features for improved classification and prediction. The experimental results show that the classification performance using Stokes images is significantly improved over that using the intensity image only.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Uwe Klimach, Hongzhi Zhao, Qiushui Chen, Yingyin Kevin Zou, Yue Wang, and Jianhua Xuan "Improved diagnostics using polarization imaging and artificial neural networks", Proc. SPIE 6142, Medical Imaging 2006: Physics of Medical Imaging, 614244 (2 March 2006);

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