Analysis of dynamic light scattering data with sparse Bayesian learning for the study of cataractogenesis

Su-Long Nyeo; Rafat R. Ansari

doi:10.1117/12.846644

2 March 2010 Analysis of dynamic light scattering data with sparse Bayesian learning for the study of cataractogenesis

Su-Long Nyeo, Rafat R. Ansari

Proceedings Volume 7550, Ophthalmic Technologies XX; 75501R (2010) https://doi.org/10.1117/12.846644
Event: SPIE BiOS, 2010, San Francisco, California, United States

Abstract

Dynamic light scattering (DLS) experimental data is statistical in nature and therefore requires a probabilistic analysis tool. The probabilistic sparse Bayesian learning (SBL) algorithm is introduced for analyzing DLS data from ocular lenses. The algorithm is used to reconstruct the most-relevant size distribution of the α-crystallins and their aggregates. The performance of the algorithm is evaluated by analyzing simulated data from a known distribution and experimental DLS data from the ocular lenses of several mammals.

Citation Download Citation

Su-Long Nyeo and Rafat R. Ansari "Analysis of dynamic light scattering data with sparse Bayesian learning for the study of cataractogenesis", Proc. SPIE 7550, Ophthalmic Technologies XX, 75501R (2 March 2010); https://doi.org/10.1117/12.846644

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