Please use this identifier to cite or link to this item:
Title: Extended Blind End-Member and Abundance Extraction for Biomedical Imaging Applications
Authors: Campos-Delgado, Daniel U.
Gutierrez-Navarro, Omar
Rico-Jimenez, Jose J.
Duran-Sierra, Elvis
Fabelo Gómez, Himar Antonio 
Ortega Sarmiento, Samuel 
Marrero Callicó, Gustavo Iván 
Jo, Javier A.
UNESCO Clasification: 3314 Tecnología médica
Keywords: Blind linear unmixing
Constrained optimization
Fluorescence lifetime imaging microscopy
Hyperspectral imaging
Optical coherence tomography
Issue Date: 2019
Project: Identificación Hiperespectral de Tumores Cerebrales (Ithaca) 
Journal: IEEE Access 
Abstract: In some applications of biomedical imaging, a linear mixture model can represent the constitutive elements (end-members) and their contributions (abundances) per pixel of the image. In this work, the extended blind end-member and abundance extraction (EBEAE) methodology is mathematically formulated to address the blind linear unmixing (BLU) problem subject to positivity constraints in optical measurements. The EBEAE algorithm is based on a constrained quadratic optimization and an alternated least-squares strategy to jointly estimate end-members and their abundances. In our proposal, a local approach is used to estimate the abundances of each end-member by maximizing their entropy, and a global technique is adopted to iteratively identify the end-members by reducing the similarity among them. All the cost functions are normalized, and four initialization approaches are suggested for the end-members matrix. Synthetic datasets are used first for the EBEAE validation at different noise types and levels, and its performance is compared to state-of-the-art algorithms in BLU. In a second stage, three experimental biomedical imaging applications are addressed with EBEAE: M-FLIM for chemometric analysis in oral cavity samples, OCT for macrophages identification in post-mortem artery samples, and hyper-spectral images for in-vivo brain tissue classification and tumor identification. In our evaluations, EBEAE was able to provide a quantitative analysis of the samples with none or minimal a priori information.
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2958985
Source: IEEE Access [ISSN 2169-3536], v. 7, p. 178539 - 178552
Appears in Collections:Artículos
Adobe PDF (19,67 MB)
Show full item record


checked on May 19, 2024


checked on Feb 25, 2024

Page view(s)

checked on Dec 30, 2023


checked on Dec 30, 2023

Google ScholarTM




Export metadata

Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.