Hyperspectral imaging and spectral unmixing of stained tissue sections using a spectrally programmable light engine

2007 ◽  
Author(s):  
N. B. MacKinnon ◽  
M. Khojasteh ◽  
P. M. Lane ◽  
C. E. MacAulay ◽  
M. Guillaud ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Spectral Unmixing ◽  
Tissue Sections ◽  
Light Engine

scholarly journals Near infrared hyperspectral imaging and spectral unmixing methods for evaluation of fiber distribution in enriched pasta

2021 ◽  
Vol 343 ◽  
pp. 128517
Author(s):  
Amanda Teixeira Badaró ◽  
José Manuel Amigo ◽  
Jose Blasco ◽  
Nuria Aleixos ◽  
Amanda Rios Ferreira ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spectral Unmixing ◽  
Fiber Distribution

scholarly journals Non-Linear Spectral Unmixing for the Estimation of the Distribution of Graphene Oxide Deposition on 3D Printed Composites

2020 ◽  
Vol 10 (21) ◽  
pp. 7792
Author(s):  
Giorgio Licciardi ◽  
Costantino Del Gaudio ◽  
Jocelyn Chanussot
Keyword(s):  
Graphene Oxide ◽  
Hyperspectral Imaging ◽  
Materials Science ◽  
Hyperspectral Image ◽  
Polymer Matrix Composites ◽  
Spectral Unmixing ◽  
Science Field ◽  
Hyperspectral Image Analysis ◽  
Custom Made ◽  
3D Printed

Hyperspectral analysis is a well-established technique that can be suitably implemented in several application fields, including materials science. This approach allows us to deal with data samples containing spatial and spectral information at very high resolution, thus enabling us to evaluate materials properties at a nanoscale level. As a proof of concept, hyperspectral imaging was here considered to investigate 3D printed polymer matrix composites, considering graphene oxide (GO) as a nanofiller. Commercial polycaprolactone and polylactic acid filaments were firstly treated with GO to be then printed into testing specimens. Raman analysis was performed to assess the GO distribution on samples surface by mapping different regions of interest and the collected data were the input of a custom-made algorithm for hyperspectral image analysis, tailored to detect the GO signature. Findings showed a valuable matching to Raman maps and were also characterized by the positive feature of avoiding to set specific conditions to perform the investigation as GO Raman distribution was carried out by fixing the wavenumber at 1580 cm−1, which is representative of the G band of the nanofiller. This occurrence might lead to an uneven intensity representation related to possible peak shifts which can bias the acquired results. Differently, hyperspectral imaging needs a minimal set of data input, i.e., the spectral signatures of neat materials, to directly identify the searched nanomaterial. More in-depth investigations need to be performed to fully validate the proposed approach, but the here presented results already show the potential and versatility of hyperspectral analysis in the materials science field.

scholarly journals Application of Spectroscopic and Hyperspectral Imaging Techniques for Rapid and Nondestructive Investigation of Jewish Ritual Parchment

2020 ◽  
Vol 7 ◽  
Author(s):  
Ioana Maria Cortea ◽  
Luminiţa Ghervase ◽  
Lucian Ratoiu ◽  
Roxana Rădvan
Keyword(s):  
Hyperspectral Imaging ◽  
Multispectral Imaging ◽  
Imaging Techniques ◽  
Inorganic Compounds ◽  
Historical Context ◽  
Short Wave ◽  
Spectral Unmixing ◽  
Materials Used ◽  
Iron Gall ◽  
Jewish Ritual

The article presents a multi-analytic investigation of a severely degraded Jewish ritual parchment coming from a private collection. The main aim of the study was to obtain key information on the parchment manufacturing technique and original materials used, information that could help understand the historical context of the object. To this aim, a series of noninvasive investigations were carried out by means of multi- and hyperspectral imaging, Fourier transform infrared (FTIR) spectroscopy and X-ray fluorescence (XRF) spectroscopy. Specific degradations and mapping of previous conservation treatments could be highlighted via multispectral imaging. Short-wave infrared images indicated the use of both iron gall and carbon black ink, probably one related to the original writing and the other to a later intervention. To improve the imaging of degraded or partially lost text, a linear spectral unmixing classification of the HSI dataset was proposed that showed promising results, allowing it to be applied to similar objects. XRF analysis offered an in-depth view of the chemical fingerprint of the original iron gall ink and critical findings on the existence of other inorganic compounds originating from the parchment manufacture. Registered FTIR data indicated denaturation of the collagen fibers and the presence of fungal-derived calcium oxalates and zinc carboxylates. In accordance with ancient Jewish parchment preparation techniques, the use of calcium sulfate, vegetable tannins, and oils was also inferred from the registered infrared spectra. The corroborated results offer valuable information on the origin, production technology, and overall degradation state of the parchment manuscript. Not least, the findings could be of great interest for conservators and restorers in the field.

scholarly journals Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering

2020 ◽  
Vol 12 (21) ◽  
pp. 3585
Author(s):  
José Prades ◽  
Gonzalo Safont ◽  
Addisson Salazar ◽  
Luis Vergara
Keyword(s):  
Independent Component Analysis ◽  
Hyperspectral Imaging ◽  
Hyperspectral Image ◽  
Spectral Unmixing ◽  
Independent Component ◽  
Hyperspectral Images ◽  
Agglomerative Clustering ◽  
Number Of Clusters ◽  
Knowing How ◽  
Pixel Matching

Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a hyperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain.

scholarly journals Information theoretic analysis of hyperspectral imaging systems with applications to fluorescence microscopy

2018 ◽  
Author(s):  
Sripad Ram
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Hyperspectral Imaging ◽  
Spectral Resolution ◽  
Stokes Shift ◽  
Information Matrix ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Resolution Limit ◽  
Performance Bound

AbstractWe present a general stochastic model for hyperspectral imaging data and derive analytical expressions for the Fisher information matrix for the underlying spectral unmixing problem. We investigate the linear mixing model as a special case and define a linear unmixing performance bound by using the Cramer-Rao inequality. As an application, we consider fluorescence imaging and show how the performance bound provides a spectral resolution limit that predicts how accurately a pair of spectrally similar fluorescent labels can be spectrally unmixed. We also report a novel result that shows how the spectral resolution limit can be overcome by exploiting the phenomenon of anti-Stokes shift fluorescence. In addition, we investigate how photon statistics, channel addition and channel splitting affect the performance bound. Finally by using the performance bound as a benchmark, we compare the performance of the least squares and the maximum likelihood estimators for spectral unmixing. For the imaging conditions tested here, our analysis shows that both estimators are unbiased and that the standard deviation of the maximum likelihood estimator is consistently closer to the performance bound than that of the least squares estimator. The results presented here are based on broad assumptions regarding the underlying data model and are applicable to hyperspectral data acquired with point detectors, sCMOS, CCD and EMCCD imaging detectors.EDICS: ELI-COL, COI-MCI.

scholarly journals Estimation of tissue optical parameters with hyperspectral imaging and spectral unmixing

2015 ◽  
Author(s):  
Guolan Lu ◽  
Xulei Qin ◽  
Dongsheng Wang ◽  
Zhuo G. Chen ◽  
Baowei Fei
Keyword(s):  
Hyperspectral Imaging ◽  
Spectral Unmixing ◽  
Optical Parameters

scholarly journals Hyperspectral imaging and spectral unmixing for improving whole-body fluorescence cryo-imaging

2020 ◽  
Vol 12 (1) ◽  
pp. 395
Author(s):  
Dennis Wirth ◽  
Brook Byrd ◽  
Boyu Meng ◽  
Rendall R. Strawbridge ◽  
Kimberley S. Samkoe ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Spectral Unmixing ◽  
Whole Body
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