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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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