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The quantity of visual information encountered experimentally by scientists across a wide range of fields has grown dramatically in recent years. As a result, the importance of dealing with multivariate data (data obtained by measuring a number of different quantities simultaneously) present in images has become much more important, and the requirement for techniques which are able to manage and analyse these data has become crucial for the practising scientist in many diverse disciplines. Multivariate Image Analysis gives the reader a sound understanding of the importance of, and the principles behind, multivariate image analysis. A short introduction to the image and its perception is followed by a discussion of some popular techniques of multivariate image formation, taken from fields such as microscopy, remote sensing and medical imaging. The principles behind one of the key multivariate techniques, principal components analysis, are thoroughly explained without going too far into the theory: The important concepts of residual visualization and local modelling are explained. Throughout, the power of the techniques discussed is demonstrated with the use of simple worked examples to illustrate the concepts, and more complex examples to indicate to the reader how a complete analysis would be carried out. The book is richly illustrated with colour images. Multivariate Image Analysis is of great interest to all those involved in the analysis of data contained in complex images. The techniques discussed are widely applicable, and are finding use in fields such as microscopy, satellite remote sensing, medical imaging, radiology, analytical chemistry, spectroscopy and astronomy.
- Sales Rank: #9500037 in Books
- Brand: Brand: Wiley
- Published on: 1997-01-23
- Original language: English
- Number of items: 1
- Dimensions: 9.51" h x 1.02" w x 6.46" l, .0 pounds
- Binding: Hardcover
- 330 pages
Features
- Used Book in Good Condition
From the Publisher
This book introduces the reader to the chemometric technique of multi-variate image analysis and its applications in chemistry. The technique provides the user with statistical data structure and geometrical structure elements from the measurement of physical and chemical interactions, and is applied in many areas of process control in the food, chemical and textile industries. The book begins by introducing the reader to the basic ideas and principles necessary for understanding later chapters, which include discussions of magnetic resonance imaging, principal component analysis, and the pre-processing and transformation of images.
From the Back Cover
The quantity of visual information encountered experimentally by scientists across a wide range of fields has grown dramatically in recent years. As a result, the importance of dealing with multivariate data (data obtained by measuring a number of different quantities simultaneously) present in images has become much more important, and the requirement for techniques which are able to manage and analyse these data has become crucial for the practising scientist in many diverse disciplines. Multivariate Image Analysis gives the reader a sound understanding of the importance of, and the principles behind, multivariate image analysis. A short introduction to the image and its perception is followed by a discussion of some popular techniques of multivariate image formation, taken from fields such as microscopy, remote sensing and medical imaging. The principles behind one of the key multivariate techniques, principal components analysis, are thoroughly explained without going too far into the theory: The important concepts of residual visualization and local modelling are explained. Throughout, the power of the techniques discussed is demonstrated with the use of simple worked examples to illustrate the concepts, and more complex examples to indicate to the reader how a complete analysis would be carried out. The book is richly illustrated with colour images. Multivariate Image Analysis is of great interest to all those involved in the analysis of data contained in complex images. The techniques discussed are widely applicable, and are finding use in fields such as microscopy, satellite remote sensing, medical imaging, radiology, analytical chemistry, spectroscopy and astronomy.
Most helpful customer reviews
2 of 2 people found the following review helpful.
The book sets the baseline for the area, and is readable too
By A Customer
If you believe that image sensor technology will continue to be developed concerning size, spatial resolution, measurement accuracy etc, you will have to ask yourself how the increased data volume should be understood, visualized and analysed. The short story on Multivariate Image Analysis (MIA) is that number crunching algorithms are available for data reduction, but the focus is on visualization and the application problem. You will depend on the human ability to explore and iteratively identify the problem, and the ability of the eye-brain to identify relevant information in the visualizations used. I admit that I am a supporter of this strategy.
The book starts out with introducing imaging, images, image operations etc. The authors are well aware that this introduction can not replace the vast literature in the area, it has to be basic, but it is well done. It is interesting to note that the necessity of knowing the characteristics of the image (depending on sensor technology) and the (imaging) experiment is better described here than in conventional image processing literature. A good analysis depend on these factors, right? The main application area magnetic resonance imaging (MRI) is described, but of course it becomes basic too. The used algorithm is principal component analysis (PCA). It is well described, using theory, examples and graphics. I especially appreciate the chapter on pre-processing techniques, with coupling to image and experiment characteristics.
After these introductions, it is time for MIA. The corner stones of MIA; visualisations, data reduction and iterative model work, are described and used in many examples. To be more specific, local models can be created, different matrices can be used, residuals are analysed, a multitude of visualizations are used, and the examples cover many applications (some are a little strange, for example hard bread (kn„ckebr"d)). The main example is an MRI example. The result is a segmentation, or an understanding of the data which helps in further experiments.
The book includes many examples and also high level language code, so it is possible to understand everything in depth if desirable. My own experience is that MIA is quite multi-disciplinary, it demands several experts (sensor technology, image processing, possibly statistics and especially the application expert!) to be successful. Without no do doubt, the book fills the role of creating a common platform for any such project. This book is to my knowledge the only dedicated one presently. There is an historical overview of work in the area which I appreciate very much. The references are adequate, and there are pointers to relevant journals.
Unfortunately, the book can not escape two crucial questions for MIA: What software should be used? After reading about MIA, I would expect a chapter on software. Just think of all the visualisation needed, and what you would like to have in the future. To write your own software would be a never ending project, I know, I have done it. What does the created images show? This is the curse of PCA (and factor analysis in general). Any application expert would wonder, and it therefore becomes a crucial question. The only way I know (and used) to explain this is to use synthetic data, of which you have control, and it is a good exercise to model the image characteristics. This approach is not used, and I can understand that. It puts even more demands on the used software.
Finn.Pedersen (formerly Uppsala University, Sweden)
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