Paul Geladi received a PhD in chemistry at the University of Antwerp. In 1990, he became associate professor at Umea University, with interests in multivariate calibration, multivariate image analysis and multiway analysis. He was awarded the EAS award for Chemometrics in 2002. He has coauthored about 90 scientific papers and 20 book chapters.Hans Grahn received his PhD in Physical Organic Chemistry in 1986. In 1990, Grahn received funding from the Swedisth Natural Science Foundation for 2D NMR work at Umea University. He has coauthored more than 35 scientific papers and book chapters. Today, he actively works in medical imaging business development.

• Images in the Natural Sciences and in Chemistry. Medical Diagnostic Tools: Magnetic Resonance Imaging. Principal Component Analysis with Basic Matrix Algebra andStatistics. Preprocessing and Transformation of Images. Principal Component Analysis on Multivariate Images. Principal Component Analysis on Covariance and Correlation Matricesof Multivariate Images. Visualization in Score Plots. The Residual and Residual Images. Local Models and Sampling. Miscellaneous Examples. Multivariate Image Analysis Applied to Magnetic ResonanceImages. Multivariate Image Regression. Epilog. References. Index. Plates.

Paul Geladi received a PhD in chemistry at the University of Antwerp. In 1990, he became associate professor at Umea University, with interests in multivariate calibration, multivariate image analysis and multiway analysis. He was awarded the EAS award for Chemometrics in 2002. He has coauthored about 90 scientific papers and 20 book chapters.Hans Grahn received his PhD in Physical Organic Chemistry in 1986. In 1990, Grahn received funding from the Swedisth Natural Science Foundation for 2D NMR work at Umea University. He has coauthored more than 35 scientific papers and book chapters. Today, he actively works in medical imaging business development.

• Images in the Natural Sciences and in Chemistry. Medical Diagnostic Tools: Magnetic Resonance Imaging. Principal Component Analysis with Basic Matrix Algebra andStatistics. Preprocessing and Transformation of Images. Principal Component Analysis on Multivariate Images. Principal Component Analysis on Covariance and Correlation Matricesof Multivariate Images. Visualization in Score Plots. The Residual and Residual Images. Local Models and Sampling. Miscellaneous Examples. Multivariate Image Analysis Applied to Magnetic ResonanceImages. Multivariate Image Regression. Epilog. References. Index. Plates.