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

Su-Long Nyeo, Rafat R. Ansari

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationOphthalmic Technologies XX
Publication statusPublished - 2010 Dec 1
EventOphthalmic Technologies XX - San Francisco, CA, United States
Duration: 2010 Jan 232010 Jan 25

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


OtherOphthalmic Technologies XX
Country/TerritoryUnited States
CitySan Francisco, CA

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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