2 December 2014 Sequential weighted Wiener estimation for extraction of key tissue parameters in color imaging: a phantom study
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Abstract
Key tissue parameters, e.g., total hemoglobin concentration and tissue oxygenation, are important biomarkers in clinical diagnosis for various diseases. Although point measurement techniques based on diffuse reflectance spectroscopy can accurately recover these tissue parameters, they are not suitable for the examination of a large tissue region due to slow data acquisition. The previous imaging studies have shown that hemoglobin concentration and oxygenation can be estimated from color measurements with the assumption of known scattering properties, which is impractical in clinical applications. To overcome this limitation and speed-up image processing, we propose a method of sequential weighted Wiener estimation (WE) to quickly extract key tissue parameters, including total hemoglobin concentration (CtHb), hemoglobin oxygenation (StO2), scatterer density (α), and scattering power (β), from wide-band color measurements. This method takes advantage of the fact that each parameter is sensitive to the color measurements in a different way and attempts to maximize the contribution of those color measurements likely to generate correct results in WE. The method was evaluated on skin phantoms with varying CtHb, StO2, and scattering properties. The results demonstrate excellent agreement between the estimated tissue parameters and the corresponding reference values. Compared with traditional WE, the sequential weighted WE shows significant improvement in the estimation accuracy. This method could be used to monitor tissue parameters in an imaging setup in real time.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Shuo Chen, Xiaoqian Lin, Caigang Zhu, Quan Liu, "Sequential weighted Wiener estimation for extraction of key tissue parameters in color imaging: a phantom study," Journal of Biomedical Optics 19(12), 127001 (2 December 2014). https://doi.org/10.1117/1.JBO.19.12.127001 . Submission:
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