Abstract
In the preceding paper [1], PCA-NN was successfully introduced to deduce the optical properties of semi-infinite tissue model from spatially resolved diffuse reflectance. However, tissue often has a layered structure. Therefore, a new hierarchical PCA-NN (HPCA-NN) algorithm was presented in this paper for extracting the optical properties of multi-layer tissue model from the spatially resolved reflectance. For simplicity, we concentrated on the two-layer model that simulated a skin layer with thickness of 5 mm and the semi-infinite underlying muscle layer. The results showed that the method can achieve high predictive accuracy with the rms errors (RMSEs) < 1% for the top-layer optical properties and the RMSEs < 5% for the bottom-layer optical properties. All the results were based on Monte Carlo simulations.
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References
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Chen, Y., Lin, L., Li, G., Gao, J., Yu, Q. (2004). Hierarchical PCA-NN for Retrieving the Optical Properties of Two-Layer Tissue Model. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_129
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DOI: https://doi.org/10.1007/978-3-540-28647-9_129
Publisher Name: Springer, Berlin, Heidelberg
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