Some special topics in multivariate image analysis

Geladi, Paul

doi:10.1016/0169-7439(92)80119-o

Cited by 28 publications

(11 citation statements)

References 15 publications

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“…A point in such an array is called a pixel and its numerical value represents its intensity (for example brightness) [1,2]. When a series of congruent 2-D images are produced and stacked together, a three-way data table is constructed.…”

Section: General Considerationsmentioning

confidence: 99%

“…Unfolding of the three-way data table into a two-way data structure is often done to facilitate the data analysis. The most common methods for multivariate image analysis have been described at length at several occasions [1][2][3][4][5]. More recently, methods for multivariate image regression [6], image texture analysis [7,8] and curve resolution in image analysis [9], have been considered.…”

Section: General Considerationsmentioning

confidence: 99%

“…Thus, this way of unfolding the image data will result in a 'long and lean' X matrix [see e.g. [1][2][3][4][5], with as many rows as there are pixels. The number of columns in X is equal to the number of channels, that is each channel becomes one column in the unfolded X matrix.…”

Section: Image Analysis With Different Objectivesmentioning

confidence: 99%

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Multivariate analysis of congruent images (MACI)

Eriksson

Wold

Trygg

2005

Journal of Chemometrics

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The multivariate analysis of congruent images (MACI) is discussed. Here, each image represents one observation and the data set contains a set of congruent images. With 'congruent images' we mean a set of images, properly pre-processed, oriented and aligned, so that each data element ('feature', pixel) corresponds to the same element across all images. An example may be a set of frames from a fixed video camera looking at a stable process. The purpose of a MACI is to find and express patterns over a set of images for the purpose of classification or quantitative regression-like relationships. This is in contrast to standard image analysis, which is usually concerned with a single image and the identification of parts of the image, for example tumour tissue versus normal. We also extend MACI to the case with a set of images that initially are not fully congruent, but are made so by the use of wavelet analysis and the distributions of the wavelet coefficients. Thus, the resulting description forms a set of congruent vectors amenable to multivariate data analysis. The MACI approach will be illustrated by four data sets, three easy-to-understand tutorial image data sets and one industrial image data set relating to quality control of steel rolls.

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Section: General Considerationsmentioning

confidence: 99%

Section: General Considerationsmentioning

confidence: 99%

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Multivariate analysis of congruent images (MACI)

Eriksson

Wold

Trygg

2005

Journal of Chemometrics

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“…Examples of these techniques, when applied to different topics in microscope imaging, are given in Geladi (1992); Van Espen et al (1992); Bonnet et al (1992); Bonnet and Zahm (1998), among others. Partial reviews are given in Bonnet (1998Bonnet ( , 2000).…”

Section: Multivariate Statistical Analysis (Msa)mentioning

confidence: 99%

Some trends in microscope image processing

Bonnet

2004

Micron

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“…Therefore, PCA has been applied to generate two‐dimensional PCA feature spaces of each multispectral image region. These two‐dimensional feature spaces are defined on a common base and can be used for image (i.e., feature space) recognition (12). Subsequently, PCA and LDA were applied to the series of PCA feature spaces to distinguish tumorous from healthy labelled image regions.…”

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confidence: 99%

Multispectral magnetic resonance image analysis using principal component and linear discriminant analysis

Witjes

Rijpkema

Melssen

et al. 2003

Magnetic Resonance Imaging

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Purpose:To explore the possibilities of combining multispectral magnetic resonance (MR) images of different patients within one data matrix. Materials and Methods:Principal component and linear discriminant analysis was applied to multispectral MR images of 12 patients with different brain tumors. Each multispectral image consisted of T1-weighted, T2-weighted, proton-density-weighted, and gadolinium-enhanced T1-weighted MR images, and a calculated relative regional cerebral blood volume map.Results: Similar multispectral image regions were clustered, while dissimilar multispectral image regions were scattered in a single plot. Both principal component and linear discriminant analysis allowed discrimination between healthy and tumor regions on the image. In addition, linear discriminant analysis allowed discrimination between oligodendrogliomas and astrocytomas. However, the discriminant analysis method was partially capable of recognizing the tumor identity in unknown multispectral images. Conclusion:The proposed method may help the radiologist in comparing multispectral MR images of different patients in a more easy and objective way.

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Some special topics in multivariate image analysis

Cited by 28 publications

References 15 publications

Multivariate analysis of congruent images (MACI)

Multivariate analysis of congruent images (MACI)

Some trends in microscope image processing

Multispectral magnetic resonance image analysis using principal component and linear discriminant analysis

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