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Published on: October 27, 2020

Quadratic blind linear unmixing: A graphical user interface for tissue characterization.

O Gutierrez-Navarro¹, D U Campos-Delgado¹, E R Arce-Santana¹

¹Facultad de Ciencias, Universidad Autonoma de San Luis Potosi, SLP, Mexico.

Computer Methods and Programs in Biomedicine

|November 22, 2015

Summary

This study introduces new software for spectral unmixing, a method to identify components within complex samples. The tool offers efficient blind end-member and abundance extraction (BEAE) and quadratic blind linear unmixing (QBLU) for multi-spectral fluorescence lifetime imaging microscopy (m-FLIM) data.

Keywords:

Chemometrics Endogenous fluorescence Graphical user interface Linear spectral unmixing Quadratic optimization

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Published on: September 22, 2013

Area of Science:

Spectroscopy and Imaging
Biophysics
Computational Biology

Background:

Spectral unmixing decomposes complex sample data into constituent components and their abundances.
Prior research focused on blind unmixing for multi-spectral fluorescence lifetime imaging microscopy (m-FLIM) datasets using linear and quadratic models.
Existing methods can be limited by the number of components or end-members.

Purpose of the Study:

To present an interactive software tool for spectral unmixing.
To implement blind end-member and abundance extraction (BEAE) and quadratic blind linear unmixing (QBLU) algorithms.
To provide a flexible solution for spectral data decomposition with or without prior knowledge of component numbers.

Main Methods:

Development of interactive software in Matlab implementing BEAE and QBLU algorithms.
Software allows estimation of end-members and abundances when the number of components is known.
Software provides a blind solution to estimate component number, end-members, and abundances when no prior knowledge is available.

Main Results:

The software successfully performs spectral unmixing for multi/hyper-spectral data.
Validated performance across diverse biological samples: ex-vivo human coronary arteries, human breast cancer cells, and in-vivo hamster oral mucosa.
Demonstrated efficiency and ease-of-use for spectral data decomposition.

Conclusions:

The developed software offers a powerful and accessible tool for spectral unmixing.
It provides both guided and completely blind solutions for component and abundance extraction.
The software is freely available, facilitating advancements in multi/hyper-spectral data analysis.