abstract

• This work is part of a continuous effort to achieve characterization of tissue from auto-fluorescence measurements. One particular problem is the estimation of the number of components in a sample from multi-spectral Fluorescence Lifetime Imaging Data (m-FLIM). The proposed method is based on a two-step iterative procedure, where first a blind end-member and abundance extraction algorithm is employed, followed by an evaluation of the resulting end-members by solving an optimal approximation problem. A threshold method is employed to evaluate if the extracted end-members are nonredundant. The validation of the proposal is performed by 3 m-FLIM data sets from post-mortem human coronary artery samples, where the results obtained matched the qualitative description provided by histopathology slides. © 2013 IEEE.

authors

• Arce Santana Edgar Román
• Campos Delgado Daniel Ulises
• Gutierrez-Navarro O.
• Jo J.A.
• Méndez García Martin Oswaldo

publication date

• 2013-01-01

funding provided via

• Consejo Nacional de Ciencia y Tecnología, CONACYT: 2012/034  Grant

published in

• Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing  Proceedings

keywords

• Autofluorescence; Dimension estimation; Hyperspectral unmixing; Linear unmixing Approximation algorithms; Biology; Fluorescence imaging; Iterative methods; Remote sensing; Spectroscopy; Autofluorescence; Dimension estimation; Extraction algorithms; Fluorescence measurements; Human coronary arteries; Hyperspectral unmixing; Linear unmixing; Optimal approximation; Image processing

Digital Object Identifier (DOI)

• 10.1109/WHISPERS.2013.8080599

start page

end page

volume

• 2013-June