Please use this identifier to cite or link to this item: https://accedacris.ulpgc.es/handle/10553/136580
Title: Robust Blind Linear Unmixing for Correlated Multimodal Images in Medical Applications
Authors: Campos Delgado, Daniel Ulises 
Mendoza-Chavarria, Juan Nicolas
Jo, Javier A.
León, Raquel 
Fabelo, Himar 
Callico, Gustavo Marrero 
UNESCO Clasification: 3314 Tecnología médica
Issue Date: 2024
Conference: IEEE URUCON 2024
Abstract: In this work, we introduce a new algorithm for robust blind linear unmixing focused in correlated multimodal images (CMI) with emphasis on medical applications. Our proposal is based on a linear model between end-members and abundances, and additive Gaussian and sparse noise components. The end-members represent spectral, time or morphological information, and their abundances denote their spatial contributions in the CMIs. Our formulation for blind linear umixing presents an iterative optimization scheme that uses a convex cost function, and jointly estimates end-members and their abundances, and a sparse noise component. The iterative methodology relies on cyclic coordinate descent optimization, constrained quadratic estimation and L1-regularization. In our evaluation, we consider synthetic datasets of biological tissue absorbance by hyperspectral imaging, and multi-spectral fluorescence life-time imaging. Our proposal was compared with five algorithms from the literature in linear unmixing, and it was able to provide the best compromise between estimation performance and complexity.
URI: https://accedacris.ulpgc.es/handle/10553/136580
ISBN: 9798350355383
DOI: 10.1109/URUCON63440.2024.10850209
Source: IEEE Urucon 2024, Montevideo
Appears in Collections:Actas de congresos
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