Recovery of macular pigment spectrum in vivo using hyperspectral image analysis

Amani A. Fawzi, Noah Lee, Jennifer H. Acton, Andrew F. Laine, R. Theodore Smith*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

27 Scopus citations


We investigated the feasibility of a novel method for hyperspectral mapping of macular pigment (MP) in vivo. Six healthy subjects were recruited for noninvasive imaging using a snapshot hyperspectral system. The three-dimensional full spatial-spectral data cube was analyzed using non-negative matrix factorization (NMF), wherein the data was decomposed to give spectral signatures and spatial distribution, in search for the MP absorbance spectrum. The NMF was initialized with the in vitro MP spectrum and rank 4 spectral signature decomposition was used to recover the MP spectrum and optical density in vivo. The recovered MP spectra showed two peaks in the blue spectrum, characteristic of MP, giving a detailed in vivo demonstration of these absorbance peaks. The peak MP optical densities ranged from 0.08 to 0.22 (mean 0.15 +/-0.05) and became spatially negligible at diameters 1100 to 1760 μm (4 to 6 deg) in the normal subjects. This objective method was able to exploit prior knowledge (the in vitro MP spectrum) in order to extract an accurate in vivo spectral analysis and full MP spatial profile, while separating the MP spectra from other ocular absorbers. Snapshot hyperspectral imaging in combination with advanced mathematical analysis provides a simple cost-effective approach for MP mapping in vivo.

Original languageEnglish (US)
Article number106008
JournalJournal of Biomedical Optics
Issue number10
StatePublished - Oct 2011


  • hyperspectral imaging
  • image analysis
  • ophthalmology

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Biomedical Engineering


Dive into the research topics of 'Recovery of macular pigment spectrum in vivo using hyperspectral image analysis'. Together they form a unique fingerprint.

Cite this