Acquisition and processing multispectral imaging system to cardiovascular tissue

Pérez, Sandra Martínez; Garzón, Johnson; Bustamante, John

doi:10.1109/pahce.2013.6568272

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…For diagnosis of these pathologies, noninvasive medical imaging techniques have allowed for more accurate medical concepts. Although these techniques allow for tissue structural and functional evaluation, they do not always cover all different diagnostic requirements, which lead to the need to develop additional imaging systems [8]. This paper presents a proof of concept of the use of multispectral images together with blind source separation techniques, for the analysis of ex-vivo aorta tissue.…”

Section: Introductionmentioning

confidence: 99%

Blind source separation of ex-vivo aorta tissue multispectral images

Galeano

Pérez

Montoya

et al. 2015

Biomed. Opt. Express

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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.

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Section: Introductionmentioning

confidence: 99%

Blind source separation of ex-vivo aorta tissue multispectral images

Galeano

Pérez

Montoya

et al. 2015

Biomed. Opt. Express

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show abstract

“…However, those systems have not been introduced to commercial applications and they need for further development [8]. The following article presents a proof of concept of the use of multispectral imaging systems and image processing methods for the analysis of the principal morphological and physiological components of cardiovascular bovine tissue.…”

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

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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.

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Acquisition and processing multispectral imaging system to cardiovascular tissue

Cited by 2 publications

References 4 publications

Blind source separation of ex-vivo aorta tissue multispectral images

Blind source separation of ex-vivo aorta tissue multispectral images

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

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