ICE: a statistical approach to identifying constituents of biomedical hyperspectral images

Mark Berman; Aloke Phatak; Ryan Lagerstrom

doi:10.1117/12.600291

23 March 2005 ICE: a statistical approach to identifying constituents of biomedical hyperspectral images

Mark Berman, Aloke Phatak, Ryan Lagerstrom

Proceedings Volume 5694, Spectral Imaging: Instrumentation, Applications, and Analysis III; (2005) https://doi.org/10.1117/12.600291
Event: SPIE BiOS, 2005, San Jose, CA, United States

Abstract

A problem of considerable interest in the hyperspectral and chemical imaging communities in recent years has been the automated identification and mapping of the constituent materials ("endmembers") present in a hyperspectral image. Several of the more important endmember-finding algorithms are discussed and some of their shortcomings highlighted. A relatively new algorithm, ICE, which attempts to address these shortcomings, is introduced. Although ICE was originally developed for exploration applications of airborne hyperspectral data, its performance on two biomedical data sets is investigated. Possible future research directions are outlined.

Citation Download Citation

Mark Berman, Aloke Phatak, and Ryan Lagerstrom "ICE: a statistical approach to identifying constituents of biomedical hyperspectral images", Proc. SPIE 5694, Spectral Imaging: Instrumentation, Applications, and Analysis III, (23 March 2005); https://doi.org/10.1117/12.600291

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