Combining hyperspectral imaging and chemometrics to assess and interpret the effects of environmental stressors on zebrafish eye images at tissue level

Olmos, Víctor; Marro, Mónica; Loza-Álvarez, Pablo; Raldúa, Demetrio; Prats, Eva; Padrós, Francesc; Piña, Benjamı́n; Tauler, Romà; Juan, Anna de

doi:10.1002/jbio.201700089

Cited by 10 publications

(12 citation statements)

References 43 publications

(56 reference statements)

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“…In Fig. 7 it is possible to observe two components associated with biological contributions (in blue and orange) were identified, whereas two additional components (in gray) were attributed to instrumental noise detected in previous works 33 . The blue component was characterized as β-carotene (with typical Raman features at 1155 and 1525 cm −1 ) 34 .…”

Section: Resultsmentioning

confidence: 63%

Linear unmixing protocol for hyperspectral image fusion analysis applied to a case study of vegetal tissues

Gómez-Sánchez

Marro

Marsal

et al. 2021

Sci Rep

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Hyperspectral imaging (HSI) is a useful non-invasive technique that offers spatial and chemical information of samples. Often, different HSI techniques are used to obtain complementary information from the sample by combining different image modalities (Image Fusion). However, issues related to the different spatial resolution, sample orientation or area scanned among platforms need to be properly addressed. Unmixing methods are helpful to analyze and interpret the information of HSI related to each of the components contributing to the signal. Among those, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) offers very suitable features for image fusion, since it can easily cope with multiset structures formed by blocks of images coming from different samples and platforms and allows the use of optional and diverse constraints to adapt to the specific features of each HSI employed. In this work, a case study based on the investigation of cross-sections from rice leaves by Raman, synchrotron infrared and fluorescence imaging techniques is presented. HSI of these three different techniques are fused for the first time in a single data structure and analyzed by MCR-ALS. This example is challenging in nature and is particularly suitable to describe clearly the necessary steps required to perform unmixing in an image fusion context. Although this protocol is presented and applied to a study of vegetal tissues, it can be generally used in many other samples and combinations of imaging platforms.

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Section: Resultsmentioning

confidence: 63%

Linear unmixing protocol for hyperspectral image fusion analysis applied to a case study of vegetal tissues

Gómez-Sánchez

Marro

Marsal

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…In Fig. 7 it is possible to observe two components associated with biological contributions (in blue and orange) were identified, whereas two additional components (in gray) were attributed to instrumental noise detected in previous works 28 . The blue component was characterized as β-carotene (with typical Raman features at 1155 and 1525 cm -1 ) 29 .…”

Section: Sr-ftir Hsi Multiset Analysismentioning

confidence: 63%

Fusion of Hyperspectral Images. Looking at the structure of vegetal tissues: a case study.

Gómez-Sánchez¹,

Marro²,

Marsal³

et al. 2021

Preprint

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Hyperspectral imaging (HSI) is a useful non-invasive technique that offers spatial and chemical information of samples. Often, different HSI techniques are used to obtain complementary information from the sample by combining different image modalities (Image Fusion). However, issues related to the different spatial resolution, sample orientation or area scanned among platforms need to be properly addressed. Unmixing methods are helpful to analyze and interpret the information of HSI related to each of the components contributing to the signal. Among those, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) offers very suitable features for image fusion, since it can easily cope with multiset structures formed by blocks of images coming from different samples and platforms and allows the use of optional and diverse constraints to adapt to the specific features of each HSI employed. In this work, a case study based on the investigation of cross-sections from rice leaves by Raman, synchrotron infrared and fluorescence imaging techniques is presented. HSI of these three different techniques are fused for the first time in a single data structure and analyzed by MCR-ALS. This example is challenging in nature and allows describing clearly the necessary steps required to perform unmixing in an image fusion context.

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“…These multiset structures may be analyzed by any bilinear decomposition method, such as PCA, or typically by multivariate resolution methods, where constraints may be applied differently to each of the concentration blocks of the images of the multiset. The outcome of these analyses (expressed as PCA or MCR scores and loadings) can be further used as seeding information for other algorithms that may work in a multiset mode as well, such as segmentation approaches, or for other data analysis tasks, such as heterogeneity studies or classification problems …”

Section: Image Fusion Challenging Algorithms and Data Analysis Strumentioning

confidence: 99%

“…The outcome of these analyses (expressed as PCA or MCR scores and loadings) can be further used as seeding information for other algorithms that may work in a multiset mode as well, such as segmentation approaches, or for other data analysis tasks, such as heterogeneity studies or classification problems. 9,40,41 It is much more challenging performing image fusion on images coming from different platforms. The problems associated with this fusion can be linked to the spatial and spectral character of images.…”

Section: Introducing Spatial Information In Hyperspectral Image Anamentioning

confidence: 99%

Hyperspectral image analysis. When space meets Chemistry

Juan

2017

Journal of Chemometrics

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Hyperspectral images provide spatial and structural information on samples.This article aims at providing an overview on hyperspectral image analysis research trends, mainly focusing on specific aspects related to this kind of analytical measurement, such as the global/local and the spectral/spatial dualities, the hurdles associated with image fusion strategies, and the design of tools devoted to enhance the spatial definition provided by the instrumental measurement. The complexity of the measurement and the wealth of possibilities to use image properties and information ensure a very lively field of research for the coming years. | INTRODUCTIONHyperspectral images are beautiful measurements exploiting at the most the spatial and structural information of samples. Images can nowadays focus on the smallest detail (spatial resolutions of nanometer in single-molecule fluorescence images) or on the largest element (pixels covering squared kilometer in remote sensing), and the chemical structure of compounds in a sample can be gathered using almost all imaginable spectroscopies and spectrometries, providing all possible facets from macromolecular to micromolecular structure of the constituents/elements in a sample.Hyperspectral image analysis needs to take into account the spatial/spectral duality, although the research tendency on image analysis has been for a long time to give a larger weight to a particular side of the coin, depending on the field of knowledge and the related applications. Thus, the image processing community has put the accent on the spatial side, whereas classical chemometrics has longer considered hyperspectral images as nothing else than another kind of spectroscopic data set. Such a bias towards space or spectroscopy is not a coincidence. Image processing was born to deal with gray or RGB images, where the small number of channels per pixel (either a gray scale value or 3 color coordinates) did not give much room to exploit information other than the one derived from spatial characteristics, such as textures and edges. On the other hand, chemometrics developed a wide span of multivariate analysis tools focused on interpreting the large amount of high quality structural and chemical information provided by spectroscopic data, and the sole use of this knowledge was often sufficient to give a satisfactory interpretation of the hyperspectral image measurement. Recent research efforts on hyperspectral image analysis point to the development or adaptation of data analysis tools that may benefit from the synergy of using simultaneously properties and information encoded in both spatial and spectral dimensions to improve the final results obtained and take the most of the original measurement.

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Combining hyperspectral imaging and chemometrics to assess and interpret the effects of environmental stressors on zebrafish eye images at tissue level

Cited by 10 publications

References 43 publications

Linear unmixing protocol for hyperspectral image fusion analysis applied to a case study of vegetal tissues

Linear unmixing protocol for hyperspectral image fusion analysis applied to a case study of vegetal tissues

Fusion of Hyperspectral Images. Looking at the structure of vegetal tissues: a case study.

Hyperspectral image analysis. When space meets Chemistry

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