Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/69248
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.
URI: http://hdl.handle.net/10553/69248
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2958985
Source: IEEE Access [ISSN 2169-3536], v. 7, p. 178539 - 178552
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