Resolution and segmentation of hyperspectral biomedical images by Multivariate Curve Resolution-Alternating Least Squares

Piqueras, Sara; Duponchel, Ludovic; Tauler, Romà; Juan, Anna de

doi:10.1016/j.aca.2011.05.020

Cited by 107 publications

(63 citation statements)

References 26 publications

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“…The potential between two latent nodes , is = Markov max (( − ) 2 , ) . (24) Here, the maximum operation causes the potential to punish only differences smaller than the threshold , while differences larger than the threshold are considered transitions between image regions that are wanted and should be preserved. Markov is the weighting factor weighting both potentials (23) and (24).…”

Section: Markov Random Field Denoisingmentioning

confidence: 99%

“…It is an often used algorithm in chemometrics, where it is also known as Multivariate Curve Resolution-Alternating Least-Squares (MCR-ALS) [23] and not only applied to hyperspectral images. For an application example including hyperspectral images, see, e. g., [24]. The unconstrained version of ALS calculates the unmixing solution of (1) by choosing initial matrices A and M and iteratively updating them [23]:…”

Section: Sequential Regularizationmentioning

confidence: 99%

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Hyperspectral image unmixing involving spatial information by extending the alternating least-squares algorithm

Bauer¹,

Stefan²,

León³

2015

Tm - Technisches Messen

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Hyperspectral images, in contrast to common RGB images, offer the possibility to not only determine the pure materials present in a scene, but also material abundances in mixtures. The calculation of the material fractions with the so-called linear mixing model is not unique, an infinite number of solutions exists. Therefore, additional constraints should be incorporated. Some algorithms involve spatial constraints explicitly, e. g., they assume that the abundances mostly do not change considerably from one pixel to another. Recently, we presented such algorithms. The calculation time with spatial constraints included, however, is rather long, so it was checked if there is a faster way to include the spatial information. In this paper, we extend the well-known alternating least-squares algorithm to implicitly include the previously used spatial information in a slightly different way, namely by adding an extra image denoising step to the calculation. The extended algorithm is called ALSmooth. We compare the computing time and the results of the ALSmooth and the previously presented algorithms. For this purpose, laboratory data of mixtures with known ground truth had been acquired. Both the previously investigated algorithms and the ALSmooth algorithm are quite sensitive towards parameter value changes; the ALSmooth algorithm is even more sensitive. For certain applications with defined environment and endmembers, however, it can be a faster alternative.Zusammenfassung: Im Gegensatz zu herkömmlichen Farbbildern bieten hyperspektrale Bilder die Möglichkeit, nicht nur die in einer Szene enthaltenen Reinstoffe, sondern auch deren Anteile in Materialmischungen festzustellen. Da die Berechnung der Materialanteile mittels des sogenannten Linearen Mischmodells nicht eindeutig ist, existieren für dieses Problem unendlich viele Lösungen. Aus diesem Grund sollten zusätzliche Nebenbedingungen mit eingebunden werden. Einige Algorithmen binden räumliche Nebenbedingungen auf explizite Weise ein, indem sie die Annahme ausnutzen, dass die Materialanteile sich von einem Pixel zu seinen Nachbarn nicht wesentlich ändern. Wir haben solche Algorithmen kürzlich untersucht. Da diese Algorithmen, die räumliche Nebenbedingungen einbeziehen, vergleichsweise viel Rechenzeit beanspruchen, wurde nach recheneffizienteren Wegen gesucht, um die räumliche Information einzubeziehen. In diesem Beitrag wird die Methode der alternierenden kleinsten Quadrate (Alternating Least-Squares, ALS) um einen zusätzlichen Bildglättungsschritt erweitert, um die räumliche Information auf diese Weise einzubeziehen. Wir nennen diesen erweiterten Algorithmus ALSmooth und vergleichen ihn mit den bereits vorgestellten Algorithmen hinsichtlich Rechenzeit und Güte der Ergebnisse. Zu diesem Zweck wurden hyperspektrale Bilder von Pulvermischungen mit bekanntem Mischungsverhältnis aufgenommen. Sowohl ALSmooth als auch die anderen Algorithmen reagieren sehr sensibel auf Änderungen von Parameterwerten, wobei ALSmooth noch sensibler ist. Für manche Anwendungen stellt er sich ...

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Section: Markov Random Field Denoisingmentioning

confidence: 99%

Section: Sequential Regularizationmentioning

confidence: 99%

Hyperspectral image unmixing involving spatial information by extending the alternating least-squares algorithm

Bauer¹,

Stefan²,

León³

2015

Tm - Technisches Messen

View full text Add to dashboard Cite

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“…We had to wait until the turning of the millennium for seeing the resolution of hyperspectral images by PARAFAC (see e.g. Piqueras [49]). …”

Section: Layered Datasetsmentioning

confidence: 99%

Chemical and mathematical resolution

Vandeginste¹

2015

Chemometrics and Intelligent Laboratory Systems

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“…Endmember extraction methods, on the other hand, have been used to study and map pigments and binders in paintings using multispectral visible and near-infrared imaging spectroscopy [17,18]. In this paper, we propose the use multivariate curve resolution-alternating least squares (MCR-ALS) [19,20], another popular spectral unmixing method that has been used in the interpretation of Raman, FTIR, TOF-SIMS, LC-MS and EDXRF imaging data [21][22][23][24]. When applied to multispectral imaging, MCR-ALS analysis assumes that the spectrum of each pixel can be decomposed into the contributions of "pure" components and will proceed to extract both their individual spectrum and a measure of their concentration or relative abundance.…”

Section: Introductionmentioning

confidence: 99%

Piet Mondrian’s Broadway Boogie Woogie: non invasive analysis using macro X-ray fluorescence mapping (MA-XRF) and multivariate curve resolution-alternating least square (MCR-ALS)

et al. 2016

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Piet Mondrian's Broadway Boogie Woogie (1942Woogie ( -1943 was examined using Macro X-Ray Fluorescence mapping (MA-XRF) to help characterize the artist's materials and understand his creative process as well as the current condition issues of the painting. The presence and distribution of key chemical elements was used to identify the main pigments in the different paint layers and under-layers, namely titanium white/barium sulfate, zinc white, bone black, cadmium yellow and/or cadmium-zinc yellow, cadmium red and/or cadmium-barium red and ultramarine. The XRF data was also examined using a multivariate curve resolution-alternating least square (MCR-ALS) approach to virtually separate and help characterize the different paint layers. Results suggest that Broadway Boogie Woogie was originally conceived as an asymmetrical grid of interlacing red and yellow bars. Mondrian then reworked the composition extensively breaking the bars by painting small squares in red, blue and gray and repainting them over and over again changing their size, color or tonality, and by adding and reworking large colored shapes in the background. Mondrian scraped off the paint in some areas before making adjustments to the composition but did not do it consistently throughout the painting. The yellow paint on the surface is severely cracked. Wherever red paint has been covered with yellow paint, it has oozed through the cracks in the top layer. The results illustrate how the MA-XRF / MCR-ALS approach can complement the examination of a painting and contribute to the understanding of the artist's process and choice of materials in a non-invasive way.

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Resolution and segmentation of hyperspectral biomedical images by Multivariate Curve Resolution-Alternating Least Squares

Cited by 107 publications

References 26 publications

Hyperspectral image unmixing involving spatial information by extending the alternating least-squares algorithm

Hyperspectral image unmixing involving spatial information by extending the alternating least-squares algorithm

Chemical and mathematical resolution

Piet Mondrian’s Broadway Boogie Woogie: non invasive analysis using macro X-ray fluorescence mapping (MA-XRF) and multivariate curve resolution-alternating least square (MCR-ALS)

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