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Campos-Delgado DU, Gutierrez-Navarro O, Rico-Jimenez JJ, Duran E, Fabelo H, Ortega S, Callicó GM, Jo JA. Extended Blind End-member and Abundance Extraction for Biomedical Imaging Applications. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2019; 7:178539-178552. [PMID: 31942279 PMCID: PMC6961960 DOI: 10.1109/access.2019.2958985] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
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.
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Affiliation(s)
- D U Campos-Delgado
- Faculty of Sciences, Universidad Autonoma de San Luis Potosi, SLP, México
| | - O Gutierrez-Navarro
- Biomedical Engineering Department, Universidad Autonoma de Aguascalientes, AGS, México
| | - J J Rico-Jimenez
- Department of Biomedical Engineering, Texas A& M University, College Station, TX, USA
| | - E Duran
- Department of Biomedical Engineering, Texas A& M University, College Station, TX, USA
| | - H Fabelo
- Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - S Ortega
- Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - G M Callicó
- Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain
| | - J A Jo
- Department of Biomedical Engineering, Texas A& M University, College Station, TX, USA
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA
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Gutierrez-Navarro O, Campos-Delgado DU, Arce-Santana ER, Jo JA. Quadratic blind linear unmixing: A graphical user interface for tissue characterization. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 124:148-160. [PMID: 26589467 PMCID: PMC4818012 DOI: 10.1016/j.cmpb.2015.10.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2015] [Revised: 09/20/2015] [Accepted: 10/20/2015] [Indexed: 06/05/2023]
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.
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Affiliation(s)
| | - D U Campos-Delgado
- Facultad de Ciencias, Universidad Autonoma de San Luis Potosi, SLP, Mexico.
| | - E R Arce-Santana
- Facultad de Ciencias, Universidad Autonoma de San Luis Potosi, SLP, Mexico
| | - Javier A Jo
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
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Campos-Delgado DU, Navarro OG, Arce-Santana ER, Jo JA. Extended output phasor representation of multi-spectral fluorescence lifetime imaging microscopy. BIOMEDICAL OPTICS EXPRESS 2015; 6:2088-105. [PMID: 26114031 PMCID: PMC4473746 DOI: 10.1364/boe.6.002088] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Revised: 05/01/2015] [Accepted: 05/04/2015] [Indexed: 05/23/2023]
Abstract
In this paper, we investigate novel low-dimensional and model-free representations for multi-spectral fluorescence lifetime imaging microscopy (m-FLIM) data. We depart from the classical definition of the phasor in the complex plane to propose the extended output phasor (EOP) and extended phasor (EP) for multi-spectral information. The frequency domain properties of the EOP and EP are analytically studied based on a multiexponential model for the impulse response of the imaged tissue. For practical implementations, the EOP is more appealing since there is no need to perform deconvolution of the instrument response from the measured m-FLIM data, as in the case of EP. Our synthetic and experimental evaluations with m-FLIM datasets of human coronary atherosclerotic plaques show that low frequency indexes have to be employed for a distinctive representation of the EOP and EP, and to reduce noise distortion. The tissue classification of the m-FLIM datasets by EOP and EP also improves with low frequency indexes, and does not present significant differences by using either phasor.
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Affiliation(s)
| | | | - E. R. Arce-Santana
- Facultad de Ciencias, Universidad Autonoma de San Luis Potosi, SLP,
Mexico
| | - Javier A. Jo
- Department of Biomedical Engineering, Texas A& M University, College Station, TX,
USA
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Gutierrez-Navarro O, Campos-Delgado DU, Arce-Santana ER, Mendez MO, Jo JA. Blind end-member and abundance extraction for multispectral fluorescence lifetime imaging microscopy data. IEEE J Biomed Health Inform 2014; 18:606-17. [PMID: 24608060 DOI: 10.1109/jbhi.2013.2279335] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper proposes a new blind end-member and abundance extraction (BEAE) method for multispectral fluorescence lifetime imaging microscopy (m-FLIM) data. The chemometrical analysis relies on an iterative estimation of the fluorescence decay end-members and their abundances. The proposed method is based on a linear mixture model with positivity and sum-to-one restrictions on the abundances and end-members to compensate for signature variability. The synthesis procedure depends on a quadratic optimization problem, which is solved by an alternating least-squares structure over convex sets. The BEAE strategy only assumes that the number of components in the analyzed sample is known a spriori. The proposed method is first validated by using synthetic m-FLIM datasets at 15, 20, and 25 dB signal-to-noise ratios. The samples simulate the mixed response of tissue containing multiple fluorescent intensity decays. Furthermore, the results were also validated with six m-FLIM datasets from fresh postmortem human coronary atherosclerotic plaques. A quantitative evaluation of the BEAE was made against two popular techniques: minimum volume constrained nonnegative matrix factorization (MVC-NMF) and multivariate curve resolution-alternating least-squares (MCR-ALS). Our proposed method (BEAE) was able to provide more accurate estimations of the end-members: 0.32% minimum relative error and 13.82% worst-case scenario, despite different initial conditions in the iterative optimization procedure and noise effect. Meanwhile, MVC-NMF and MCR-ALS presented more variability in estimating the end-members: 0.35% and 0.34% for minimum errors and 15.31% and 13.25% in the worst-case scenarios, respectively. This tendency was also maintained for the abundances, where BEAE obtained 0.05 as the minimum absolute error and 0.12 in the worst-case scenario; MCR-ALS and MVC-NMF achieved 0.04 and 0.06 for the minimum absolute errors, and 0.15 and 0.17 under the worst-case conditions, respectively. In addition, the average computation time was evaluated for the synthetic datasets, where MVC-NMF achieved the fastest time, followed by BEAE and finally MCR-ALS. Consequently, BEAE improved MVC-NMF in convergence to a local optimal solution and robustness against signal variability, and it is roughly 3.6 time faster than MCR-ALS.
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Gutierrez-Navarro O, Campos-Delgado DU, Arce-Santana ER, Maitland KC, Cheng S, Jabbour J, Malik B, Cuenca R, Jo JA. Estimation of the number of fluorescent end-members for quantitative analysis of multispectral FLIM data. OPTICS EXPRESS 2014; 22:12255-12272. [PMID: 24921344 PMCID: PMC4086288 DOI: 10.1364/oe.22.012255] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2014] [Revised: 05/02/2014] [Accepted: 05/02/2014] [Indexed: 05/29/2023]
Abstract
Multispectral fluorescence lifetime imaging (m-FLIM) can potentially allow identifying the endogenous fluorophores present in biological tissue. Quantitative description of such data requires estimating the number of components in the sample, their characteristic fluorescent decays, and their relative contributions or abundances. Unfortunately, this inverse problem usually requires prior knowledge about the data, which is seldom available in biomedical applications. This work presents a new methodology to estimate the number of potential endogenous fluorophores present in biological tissue samples from time-domain m-FLIM data. Furthermore, a completely blind linear unmixing algorithm is proposed. The method was validated using both synthetic and experimental m-FLIM data. The experimental m-FLIM data include in-vivo measurements from healthy and cancerous hamster cheek-pouch epithelial tissue, and ex-vivo measurements from human coronary atherosclerotic plaques. The analysis of m-FLIM data from in-vivo hamster oral mucosa identified healthy from precancerous lesions, based on the relative concentration of their characteristic fluorophores. The algorithm also provided a better description of atherosclerotic plaques in term of their endogenous fluorophores. These results demonstrate the potential of this methodology to provide quantitative description of tissue biochemical composition.
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Affiliation(s)
- Omar Gutierrez-Navarro
- Universidad Autonoma de San Luis Potosi, Facultad de Ciencias, Av. Dr. Salvador Nava Mtz s/n, Zona Universitaria, C.P. 78290, San Luis Potosi,
Mexico
| | - Daniel U. Campos-Delgado
- Universidad Autonoma de San Luis Potosi, Facultad de Ciencias, Av. Dr. Salvador Nava Mtz s/n, Zona Universitaria, C.P. 78290, San Luis Potosi,
Mexico
| | - Edgar R. Arce-Santana
- Universidad Autonoma de San Luis Potosi, Facultad de Ciencias, Av. Dr. Salvador Nava Mtz s/n, Zona Universitaria, C.P. 78290, San Luis Potosi,
Mexico
| | - Kristen C. Maitland
- Texas A&M University, Department of Biomedical Engineering, 5045 Emerging Technologies Building, 3120 TAMU, College Station, Texas 77843USA
| | - Shuna Cheng
- Texas A&M University, Department of Biomedical Engineering, 5045 Emerging Technologies Building, 3120 TAMU, College Station, Texas 77843USA
| | - Joey Jabbour
- Texas A&M University, Department of Biomedical Engineering, 5045 Emerging Technologies Building, 3120 TAMU, College Station, Texas 77843USA
| | - Bilal Malik
- Texas A&M University, Department of Biomedical Engineering, 5045 Emerging Technologies Building, 3120 TAMU, College Station, Texas 77843USA
| | - Rodrigo Cuenca
- Texas A&M University, Department of Biomedical Engineering, 5045 Emerging Technologies Building, 3120 TAMU, College Station, Texas 77843USA
| | - Javier A. Jo
- Texas A&M University, Department of Biomedical Engineering, 5045 Emerging Technologies Building, 3120 TAMU, College Station, Texas 77843USA
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