On the extraction of spectral and spatial information from images

Liu, J. Jay; MacGregor, John F.

doi:10.1016/j.chemolab.2006.05.011

Cited by 51 publications

(29 citation statements)

References 18 publications

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“…FastICA algorithm with hyperbolical tangents nonquadratic function was used [5,6]. We have choose this method because it preserves histological properties in best manner.…”

Section: Methodsmentioning

confidence: 99%

MRI brain tumors images by using independent component analysis

Mihailović

Savić²,

Bogdanovic-Pristov³

et al. 2011

2011 IEEE 9th International Symposium on Intelligent Systems and Informatics

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The magnetic properties of nuclei have significant applications in medical imaging. These applications are possible because the relaxation properties and the resonance frequency for a nucleus depend upon its environment. Factors such as the presence of chemical bonds, paramagnetic ions and the rate of flow of fluids influence the magnetic resonance (MR) signal. Therefore, different regions of a biological sample produce different MR signals for the given imaging parameters. While the reasons for these differences are often so complex that interpretation is difficult, their existence yields an intrinsic subject contrast between tissues. It is possible to capture several images by changing imaging conditions with different contrasting between the tissues. Independent component analysis (ICA) applied to images provides identification of tissues with the similar response in different imaging conditions. In order to identify tumor regions we have used correlation between symmetrical brain regions and image segmentation based on pixel brightness.

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“…FastICA algorithm with hyperbolical tangents nonquadratic function was used [5,6]. We have choose this method because it preserves histological properties in best manner.…”

Section: Methodsmentioning

confidence: 99%

MRI brain tumors images by using independent component analysis

Mihailović

Savić²,

Bogdanovic-Pristov³

et al. 2011

2011 IEEE 9th International Symposium on Intelligent Systems and Informatics

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“…A multiresolutional multivariate image analysis (MR-MIA) performed better than the wavelet textural analysis. The MR-MIA was helpful in the data decomposition of hypercubes that were acquired from using a large number of wavelength bands in hyperspectral imaging (Liu and MacGregor, 2007).…”

Section: Softwarementioning

confidence: 99%

Hyperspectral imaging to classify and monitor quality of agricultural materials

Sivakumar

Jayas

Paliwal

et al. 2015

Journal of Stored Products Research

175

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“…Regarding N-way models, very few works have treated these techniques [21,22] However, only very recently [23,24] the integration of both types of information has been studied, although these works have been developed in the image domain, which makes the defect detection task more difficult.…”

Section: Introductionmentioning

confidence: 99%

Integration of colour and textural information in multivariate image analysis: defect detection and classification issues

Prats-Montalbán

Ferrer

2007

Journal of Chemometrics

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In industrial processes, the detection and visualisation of defects and the development of efficient automated classification tools are strategic issues, especially when dealing with random colour textures (RCTs). This paper discusses the benefits of integrating colour and spatial (i.e. textural) information of digital RGB colour images in multivariate image analysis (MIA) to deal with these topics. Regarding the first one, a simple and computational cost-effective monitoring procedure based on colour-textural MIA merged with multivariate statistical process control (MSPC) ideas is outlined. Two novel computed images: T 2 and RSS Images are proposed. The procedure is applied on digital RGB colour images from artificial stone plates. With respect to the second issue, when colour-textural MIA is used for image classification a lot of factors (e.g. pre-processing, modelling, . . .) likely affecting the success rate in the classification (SRC) show up. This paper presents a methodology based on the combination of experimental design and logistic regression for choosing the best combination of factors to maximise the SRC of different types of images. Digital RGB colour images from ceramic tiles and orange fruits are used to illustrate the potential of the proposed methodology.

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On the extraction of spectral and spatial information from images

Cited by 51 publications

References 18 publications

MRI brain tumors images by using independent component analysis

MRI brain tumors images by using independent component analysis

Hyperspectral imaging to classify and monitor quality of agricultural materials

Integration of colour and textural information in multivariate image analysis: defect detection and classification issues

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