Unmixing of human skin optical reflectance maps by Non-negative Matrix Factorization algorithm

Galeano, July; Jolivot, Romuald; Marzani, Franck; Benezeth, Yannick

doi:10.1016/j.bspc.2012.08.007

Cited by 10 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonnegative matrix factorization 26,27 is a new factor-extraction method for pattern recognition and is useful for obtaining representations of nonnegative data. It aims to create two nonnegative matrices, the products of which can be used to approximate the original matrix, naturally resulting in a parts-based representation.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Soil Salinity Using Near-Infrared Reflectance Spectroscopy with Nonnegative Matrix Factorization

et al. 2016

View full text Add to dashboard Cite

As a key, yet difficult, issue currently in the quantitative remote sensing analysis of soil, the accurate and stable monitoring of soil salinity content (SSC) in situ should be studied and improved. The purpose of this study is to explore the method of fusing spectra outdoors with spectra indoors and improve the estimation precision of SSC based on near-infrared (NIR) reflectance hyper-spectra. First, samples of saline soil from the Yellow River delta of China were collected and analyzed. We measured three groups of sample spectra using a spectrometer: (1) situ-spectra, measured at sampling points in situ; (2) out-spectra, measured outdoors on air-dried samples; and, (3) lab-spectra, measured in a dark laboratory with the above air-dried samples. Second, four algorithms (multiplicative update, alternating least-squares, sparse affine non-negative matrix factorization (NMF), and gradient projection algorithms) of NMF were used to fuse the situ-spectra or out-spectra with the lab-spectra for the calibration of SSC. Finally, estimation models of SSC were built using the multiple linear regression method based on the first derivatives of the un-fused and fused spectra. The results indicate that using the NMF method to fuse the situ-spectra or out-spectra with the lab-spectra can heighten the correlation between SSC and the outdoor spectra in most wavelength ranges and improve the accuracy of the prediction model. The gradient projection algorithm shows the best performance with fewer variables and highest accuracy of the SSC model based on the NIR spectra.

show abstract

Section: Methodsmentioning

confidence: 99%

Prediction of Soil Salinity Using Near-Infrared Reflectance Spectroscopy with Nonnegative Matrix Factorization

et al. 2016

View full text Add to dashboard Cite

show abstract

“…This is a constrain well suited for our data which correspond to reflectance spectrum [5]. The main goal of Blind Source Separation (BSS) methods is to represent a given signal as a weighted sum of main sources.…”

Section: Non-negative Matrix Factorization-nmfmentioning

confidence: 99%

“…This information can be very useful for tissue characterization and can contribute to the early detection of a wide range of pathologies [5]. For example, in skin analysis, multispectral images can determine the levels of melanin and hemoglobin, which are essential chromophores in the study of hyper or hypo-pigmentation as well as in the assessment of skin appearance [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Analysis of mutli-spectral images from cardiovascular tissue by means of blind source separation methods

Galeano

Pérez

Botina

et al. 2014

2014 XIX Symposium on Image, Signal Processing and Artificial Vision

Self Cite

View full text Add to dashboard Cite

This article presents the use of blind source separation methods for the decomposition of cardiovascular tissue multispectral images in its main morphological components. The evaluated images were acquired with two kinds of systems: one based in a confocal configuration and another based in interference filters. The implemented source separation algorithms are based on a multiplicative coefficient upload and on Principal Component Analysis (PCA) techniques. The goal is to represent a given multispectral image as the weighted sum of different components. The resulting weighted coefficients are used to quantify the content of the main components in a given multi-spectral image. The methodology is validated on cardiovascular bovine tissue. The results show that PCA not only allows image reduction but also an increase in the image contrast. This fact allows for a better determination of the tissue's structure. Also, the result of applying NMF shows that the method allows for maps that quantify the principal chromophores that compose cardiovascular tissue.

show abstract

“…This is a constrain well suited for our data which correspond to reflectance spectrum [20]. As described in section 2.3, BSS approximates a given n × m matrix Y , with Y nm ≥ 0, into the product of two non-negative matrices W ∈ R n×r (matrix of weighted coefficients W nr ) and H ∈ R r×m (matrix of main sources H rm ), i.e.…”

Section: Non-negative Matrix Factorization-nmfmentioning

confidence: 99%

Blind source separation of ex-vivo aorta tissue multispectral images

Galeano

Pérez

Montoya

et al. 2015

Biomed. Opt. Express

Self Cite

View full text Add to dashboard Cite

Blind Source Separation methods (BSS) aim for the decomposition of a given signal in its main components or source signals. Those techniques have been widely used in the literature for the analysis of biomedical images, in order to extract the main components of an organ or tissue under study. The analysis of skin images for the extraction of melanin and hemoglobin is an example of the use of BSS. This paper presents a proof of concept of the use of source separation of ex-vivo aorta tissue multispectral Images. The images are acquired with an interference filter-based imaging system. The images are processed by means of two algorithms: Independent Components analysis and Non-negative Matrix Factorization. In both cases, it is possible to obtain maps that quantify the concentration of the main chromophores present in aortic tissue. Also, the algorithms allow for spectral absorbance of the main tissue components. Those spectral signatures were compared against the theoretical ones by using correlation coefficients. Those coefficients report values close to 0.9, which is a good estimator of the method's performance. Also, correlation coefficients lead to the identification of the concentration maps according to the evaluated chromophore. The results suggest that Multi/hyper-spectral systems together with image processing techniques is a potential tool for the analysis of cardiovascular tissue.

show abstract

Unmixing of human skin optical reflectance maps by Non-negative Matrix Factorization algorithm

Cited by 10 publications

References 23 publications

Prediction of Soil Salinity Using Near-Infrared Reflectance Spectroscopy with Nonnegative Matrix Factorization

Prediction of Soil Salinity Using Near-Infrared Reflectance Spectroscopy with Nonnegative Matrix Factorization

Analysis of mutli-spectral images from cardiovascular tissue by means of blind source separation methods

Blind source separation of ex-vivo aorta tissue multispectral images

Contact Info

Product

Resources

About