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IEEE Access
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publication venue for: Extended Blind End-Member and Abundance Extraction for Biomedical Imaging Applications
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R01CA200399, R01CA218739
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RDF description of Extended Blind End-Member and Abundance Extraction for Biomedical Imaging Applications - http://orbis.uaslp.mx/vivo/individual/scopus6077
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date: 2026-08-27T05:39:59
publisher: vivo
type: Document

Article
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Information Content Entity
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Extended Blind End-Member and Abundance Extraction for Biomedical Imaging Applications
doi: 10.1109/ACCESS.2019.2958985
related by: authorship_39069
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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. © 2019 IEEE.
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crisid: 6078
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estado: published
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has funding vehicle: Consejo Nacional de Ciencia y Tecnología, CONACYT: 254637
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has funding vehicle: R01CA200399, R01CA218739
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most specific type: Article
has publication venue: IEEE Access
volume: 7
page start: 178539
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origen: scopus
page end: 178552
date time value: article date 6078
has funding vehicle: Agencia Canaria de Investigación, Innovación y Sociedad de la Información, ACIISI: ProID2017010164
accesstype: All Open Access, Gold, Green
most specific type: Document
freetext keyword: Blind linear unmixing; constrained optimization; fluorescence lifetime imaging microscopy; hyperspectral imaging; optical coherence tomography Blind source separation; Constrained optimization; Cost functions; Extraction; Fluorescence imaging; Image segmentation; Iterative methods; Medical imaging; Optical data processing; Optical tomography; Quadratic programming; Spectroscopy; Biomedical imaging applications; Brain tissue classifications; Constrained quadratic optimization; Fluorescence lifetime imaging microscopy; Hyper-spectral images; Linear mixture modeling; Linear unmixing; State-of-the-art algorithms; Hyperspectral imaging
artnum: 8931797

Consejo Nacional de Ciencia y Tecnología, CONACYT: 254637
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funding vehicle for: Extended Blind End-Member and Abundance Extraction for Biomedical Imaging Applications

Agencia Canaria de Investigación, Innovación y Sociedad de la Información, ACIISI: ProID2017010164
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type: BFO_0000020
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funding vehicle for: Extended Blind End-Member and Abundance Extraction for Biomedical Imaging Applications