ArticleIEEE access : practical innovations, open solutions2019
Extended Blind End-member and Abundance Extraction for Biomedical Imaging Applications.
Article in IEEE access : practical innovations, open solutions, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Hyperspectral imaging in neurosurgery: a review of systems, computational methods, and clinical applications.Journal of biomedical optics · 2025Review
- Comparative analysis of machine learning techniques on the BraTS dataset for brain tumor classification.Frontiers in oncology · 2025Article
- Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks.Healthcare technology letters · 2024Article
- Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection.NPJ precision oncology · 2023Article
- Live-cell fluorescence spectral imaging as a data science challenge.Biophysical reviews · 2022Review
- Blind deconvolution estimation by multi-exponential models and alternated least squares approximations: Free-form and sparse approach.PloS one · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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
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Registered trials
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