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Proceedings Paper

Spectral unmixing algorithm for distributed endmembers with applications to biomedical imaging
Author(s): Sabbir A. Rahman
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Paper Abstract

Spectral unmixing algorithms tend to make the simplifying assumptions that each type of material in a spectral library may be represented by a single reference spectrum and that the mixing process is linear. While these assumptions are convenient in that they allow techniques of linear algebra to be used, they lack realism as each material type in a spectral image will in general emit a distribution of spectra while the mixing itself need not be linear. We describe a 'common sense' spectral unmixing algorithm for the general case where endmembers are described by arbitrary D-dimensional probability distribution and the mixing can be non-linear. As an application we outline an unsupervised procedure for deriving the fractional material content of every pixel in an image and identifying anomalies given no a priori knowledge. Accurate endmember distribution are obtained by first masking out impure pixels using locally normalized Sobel and Laplacian filters and then performing single-link hierarchical clustering on the pure pixels which remain. The most probable endmember decomposition for a given target spectrum is found by selecting an appropriate set of endmembers based on the target's immediate neighborhood, and performing a constrained maximum likelihood search over the space of fractional abundances. We also explain how the procedure may be applied to subpixel and anomaly detection. To illustrate our ideas the techniques described are applied to biomedical images throughout.

Paper Details

Date Published: 21 April 1999
PDF: 12 pages
Proc. SPIE 3603, Systems and Technologies for Clinical Diagnostics and Drug Discovery II, (21 April 1999); doi: 10.1117/12.346735
Show Author Affiliations
Sabbir A. Rahman, Raytheon Optical Systems, Inc. (United Kingdom)


Published in SPIE Proceedings Vol. 3603:
Systems and Technologies for Clinical Diagnostics and Drug Discovery II
Gerald E. Cohn; John C. Owicki, Editor(s)

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