Multiband imaging and linear unmixing of optical fiber intrusion signal

Yuan, Zhimin; Sun, Chengbin; Yang, Dan; Qu, Hongquan

doi:10.1016/j.ijleo.2018.03.082

Cited by 4 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, the unmixing can utilize 1210 nm and 1730/1760 nm lipid fingerprints, also bearing in the calculation of the background water absorption bands. This can be considered as a continuous absorption multiband unmixing [58,59] in the NIR-SWIR range. There, the best distinguishing of small lipid inclusions and a low background noise, appeared due to the less misrecognition, are noticeable.…”

Section: Hyperspectral Imaging Of Lipids and Proteinsmentioning

confidence: 99%

Hyperspectral imaging of lipids in biological tissues using near‐infrared and shortwave infrared transmission mode: A pilot study

Golovynskyi

Golovynska

Roganova

et al. 2023

Journal of Biophotonics

View full text Add to dashboard Cite

Label-free hyperspectral imaging (HSI) of lipids was demonstrated in the nearinfrared (NIR) and shortwave infrared (SWIR) regions (950-1800 nm) using porcine tissue. HSI was performed in the transmission light-pass configuration, using a NIR-SWIR camera coupled with a liquid crystal tunable filter. The transmittance spectra of the regions of interest (ROIs), which correspond to the lipid and muscle areas in the specimen, were utilized for the spectrum unmixing. The transmittance spectra in ROIs were compared with those recorded by a spectrophotometer using samples of adipose and muscle. The lipid optical absorption bands at 1210 and 1730 nm were first used for the unmixing and mapping. Then, we performed the continuous multiband unmixing over the entire available spectral range, thereby, considering a combination of characteristic absorption bands of lipids, proteins, and water. The enhanced protocol demonstrates the ability to visualize small adipose inclusions of 1-10 μm size.

show abstract

Section: Hyperspectral Imaging Of Lipids and Proteinsmentioning

confidence: 99%

Hyperspectral imaging of lipids in biological tissues using near‐infrared and shortwave infrared transmission mode: A pilot study

Golovynskyi

Golovynska

Roganova

et al. 2023

Journal of Biophotonics

View full text Add to dashboard Cite

show abstract

“…As a result, EBEAE provided four end-members for the BG class {p 1 BG , p 2 BG , p 3 BG , p 4 BG }. Next, the end-members matrix P was constructed with seven spectral profiles {p NT , p TT , p BV } {p 1 BG , p 2 BG , p 3 BG , p 4 BG }, which described the four classes of studied tissue in the HSI database. To identify the abundance of each class, the estimation algorithm in (17) is just computed withμ = 0.2.…”

Section: Hsi Applicationmentioning

confidence: 99%

“…For many biomedical applications, the measurements in the dataset can be modeled by a linear combination of some basic components [1]- [4]. These studied measurements could be related to temporal-dynamic responses, spectroscopic and/or morphological information.…”

Section: Introductionmentioning

confidence: 99%

“…referred as end-members, and their concentrations in the measurements are denoted as the abundances. An unmixing analysis allows a quantitative characterization of a dataset by identifying the end-members and their corresponding abundances in a linear mixture model [1]- [3]. Hence, the problem of jointly estimating the end-members and their abundances in a dataset is called blind linear unmixing (BLU) analysis [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extended Blind End-Member and Abundance Extraction for Biomedical Imaging Applications

et al. 2019

View full text Add to dashboard Cite

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.INDEX TERMS Blind linear unmixing, constrained optimization, fluorescence lifetime imaging microscopy, hyperspectral imaging, optical coherence tomography.

show abstract

Optical fiber intrusion signal unmixing by constrained quadratic programming approach

Zhang

Tian

Pang

et al. 2018

Optik

View full text Add to dashboard Cite

Multiband imaging and linear unmixing of optical fiber intrusion signal

Cited by 4 publications

References 23 publications

Hyperspectral imaging of lipids in biological tissues using near‐infrared and shortwave infrared transmission mode: A pilot study

Hyperspectral imaging of lipids in biological tissues using near‐infrared and shortwave infrared transmission mode: A pilot study

Extended Blind End-Member and Abundance Extraction for Biomedical Imaging Applications

Optical fiber intrusion signal unmixing by constrained quadratic programming approach

Contact Info

Product

Resources

About