Details

Preface. <p>List of Contributors.</p> <p>List of Abbreviations.</p> <p>1 Multivariate Images, Hyperspectral Imaging: Background and Equipment <i>(Paul L. M. Geladi, Hans F. Grahn and James E. Burger).</i></p> <p>2 Principles of Multivariate Image Analysis (MIA) in Remote Sensing, Technology and Industry <i>(Kim H. Esbensen and Thorbjørn T. Lied).</i></p> <p>3 Clustering and Classification in Multispectral Imaging for Quality Inspection of Postharvest Products <i>(Jacco C. Noordam and Willie H. A. M. van den Broek).</i></p> <p>4 Self-modeling Image Analysis with SIMPLISMA <i>(Willem Windig, Sharon Markel and Patrick M. Thompson).</i></p> <p>5 Multivariate Analysis of Spectral Images Composed of Count Data <i>(Michael R. Keenan).</i></p> <p>6 Hyperspectral Image Data Conditioning and Regression Analysis <i>(James E. Burger and Paul L. M. Geladi).</i></p> <p>7 Principles of Image Cross-validation (ICV): Representative Segmentation of Image Data Structures <i>(Kim H. Esbensen and Thorbjørn T. Lied).</i></p> <p>8 Detection, Classification, and Quantification in Hyperspectral Images Using Classical Least Squares Models <i>(Neal B. Gallagher).</i></p> <p>9 Calibration Standards and Image Calibration <i>(Paul L. M. Geladi).</i></p> <p>10 Multivariate Movies and their Applications in Pharmaceutical and Polymer Dissolution Studies <i>(Jaap van der Weerd and Sergei G. Kazarian).</i></p> <p>11 Multivariate Image Analysis of Magnetic Resonance Images: Component Resolution with the Direct Exponential Curve Resolution Algorithm (DECRA) <i>(Brian Antalek, Willem Windig and Joseph P. Hornak).</i></p> <p>12 Hyperspectral Imaging Techniques: an Attractive Solution for the Analysis of Biological and Agricultural Materials <i>(Vincent Baeten, Juan Antonio Fernández Pierna and Pierre Dardenne).</i></p> <p>13 Application of Multivariate Image Analysis in Nuclear Medicine: Principal Component Analysis (PCA) on Dynamic Human Brain Studies with Positron Emission Tomography (PET) for Discrimination of Areas of Disease at High Noise Levels <i>(Pasha Razifar and Mats Bergström).</i></p> <p>14 Near Infrared Chemical Imaging: Beyond the Pictures <i>(E. Neil Lewis, Janie Dubois, Linda H. Kidder and Kenneth S. Haber).</i></p> <p>Index.</p>

<b>Paul Geladi</b> received a Ph.D. in chemistry at the University of Antwerp in 1979. In 1990, he became associate professor at Umeå University, with interests in multivariate calibration, multivariate image analysis and multiway analysis. He was awarded the EAS award for Chemometrics in 2002. Currently he is head of research for NIRCE centered in Umeå and Vasa. Paul Geladi has coauthored about 90 scientific papers and some 20 book chapters and has given many invited lectures throughout Europe and North America. He was European editor of Journal of Chemometrics from 1989 - 1995, and has been review editor of the journal since 1999. He served as a member of the Editorial Board of Chemometrics and Intelligent Laboratory Systems from 1986 to 1991. <p><b>Hans F Grahn</b> received his Ph.D. in Physical Organic Chemistry in 1986. Following several years of work abroad he began MRI studies in the laboratory of Dr Zeverenyi at SUNY Health Center, NY. At this time (1988) Hans also began to collaborate with Paul Geladi and the MIA (Multivariate Image Analysis) software for MRI multivariate images was written. In 1990 Hans received funding from the Swedish Natural Science Foundation for 2D NMR work at Umeå University. In 1991 he began a 3 year project at AstraZeneca. During this period he continued to collaborate with the pharmaceutical industry and the Karolinska Institute, where he received a position as a preclinical researcher and associate Professor at a new MRI -centre. Hans has more than 35 coauthored scientific papers and book chapters. He is now active as Business Developer in the Medical Imaging business and is also active in his own company.</p>

This book is about multivariate and hyperspectral imaging, not only on how to produce the images but on how to clean, transform, analyze and presnet them. The emphasis is on visualization of images, models and statistical diagnostics but some useful n umbers and equations are given where needed. The book is divided into two parts- the first chapters are about definitions, nomenclature and data analytical and visualization aspects, i.e. the definition of multivariate and hyperspectral images. They introduce nomenclature; insights into factor and component modeling used on the spectral information in the images; the concepts and models for regression modeling on hyperspectral images and multivariate image regression (MIR). <p>The final five applied chapters present a diverse catalog of things that can be done with hyperspectral images using different types of variables including:</p> <ul> <li> <div><b>Multivariate movies in different variables, mainly optical, infrared, Raman and nuclear magnetic resonance.</b></div> </li> <li> <div><b>The DAECRA technique as it can be used on phantoms and brain images in magnetic resonance imaging.</b></div> </li> <li> <div><b>Agricultural and biological applications of optical multivariate and hyperspectral imaging.</b></div> </li> <li> <div><b>Brain studies using positron emission tomography (PET). PET images are extremely noisy and require special care.</b></div> </li> <li> <div><b>Chemical imaging using near infrared spectroscopy. Pharmaceutical granulate mixtures are the examples used.</b></div> </li> </ul> <p>This book is intended for both an audience new to multivariate image analysis as well as to those who are already using image analysis techniques. It is relevant to academic and industrial researchers in chemistry, biology and medical sciences.</p>

GB