Multivariate analysis of congruent images (MACI)

Abstract

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.

Computational evaluation of some indenopyrazole derivatives as anticancer compounds; application of QSAR and docking methodologies

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“…The generated model also passed the rest of the criteria that we operated to illustrate its predictive ability (see equations [12][13][14][15][16] …”

Application of Principal Components Regression for Analysis of X-Ray Diffraction Images of Wood

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