Evidence map›Paper›PMID 26589467›Full record

ArticleComputer methods and programs in biomedicine2016

Quadratic blind linear unmixing: A graphical user interface for tissue characterization.

O Gutierrez-Navarro, D U Campos-Delgado, E R Arce-Santana, Javier A Jo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computer methods and programs in biomedicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Extended Blind End-member and Abundance Extraction for Biomedical Imaging Applications.IEEE access : practical innovations, open solutions · 2019
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

O Gutierrez-NavarroFacultad de Ciencias, Universidad Autonoma de San Luis Potosi, SLP, Mexico.
D U Campos-DelgadoFacultad de Ciencias, Universidad Autonoma de San Luis Potosi, SLP, Mexico. Electronic address: ducd@fciencias.uaslp.mx.
E R Arce-SantanaFacultad de Ciencias, Universidad Autonoma de San Luis Potosi, SLP, Mexico.
Javier A JoDepartment of Biomedical Engineering, Texas A&M University, College Station, TX, USA.
Autonomous University of San Luis Potosí · MXTexas A&M University · US

Funding

Morphological and Molecular Imaging System for in vivo Atherosclerosis ResearchR01HL111361 · NHLBI · TEXAS ENGINEERING EXPERIMENT STATION · PI APPLEGATE, BRIAN E. · 2012 to 2016
$1.7M
Multiresolution FLIM and Reflectance Confocal Microscopy to Detect DysplasiaR01CA138653 · NCI · TEXAS ENGINEERING EXPERIMENT STATION · PI MAITLAND, KRISTEN C. · 2010 to 2014
$1.5M
Structured illumination multispectral fluorescence lifetime microendoscopy to detect oral premalignanciesR03CA191860 · NCI · TEXAS ENGINEERING EXPERIMENT STATION · PI MALIK, BILAL · 2015 to 2016
$140k
NCI NIH HHS R01 CA138653NCI NIH HHS R01CA138653NCI NIH HHS R03 CA191860NHLBI NIH HHS R01HL11136NHLBI NIH HHS R01 HL111361
6 · The paper itself

Abstract

Spectral unmixing is the process of breaking down data from a sample into its basic components and their abundances. Previous work has been focused on blind unmixing of multi-spectral fluorescence lifetime imaging microscopy (m-FLIM) datasets under a linear mixture model and quadratic approximations. This method provides a fast linear decomposition and can work without a limitation in the maximum number of components or end-members. Hence this work presents an interactive software which implements our blind end-member and abundance extraction (BEAE) and quadratic blind linear unmixing (QBLU) algorithms in Matlab. The options and capabilities of our proposed software are described in detail. When the number of components is known, our software can estimate the constitutive end-members and their abundances. When no prior knowledge is available, the software can provide a completely blind solution to estimate the number of components, the end-members and their abundances. The characterization of three case studies validates the performance of the new software: ex-vivo human coronary arteries, human breast cancer cell samples, and in-vivo hamster oral mucosa. The software is freely available in a hosted webpage by one of the developing institutions, and allows the user a quick, easy-to-use and efficient tool for multi/hyper-spectral data decomposition.

Indexed as

AlgorithmsComputer GraphicsLinear ModelsUser-Computer InterfaceBiopsyComputer SimulationHumansImage EnhancementImage Interpretation, Computer-AssistedMicroscopy, FluorescenceReproducibility of ResultsSensitivity and SpecificityChemometricsEndogenous fluorescenceGraphical user interfaceLinear spectral unmixingQuadratic optimization

Identifiers

PMID26589467
PMCPMC4818012
OpenAlexW2180922958

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.