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Hardcover | $60.00 Short | £41.95 | ISBN: 9780262013284 | 432 pp. | 6 x 9 in | 173 b&w illus., 22 tables, 9 color plates| March 2010
 
ebook | $42.00 Short | ISBN: 9780262260077 | 432 pp. | 6 x 9 in | 173 b&w illus., 22 tables, 9 color plates| March 2010
 

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Biomedical Signal Analysis

Contemporary Methods and Applications

Overview

Biomedical signal analysis has become one of the most important visualization and interpretation methods in biology and medicine. Many new and powerful instruments for detecting, storing, transmitting, analyzing, and displaying images have been developed in recent years, allowing scientists and physicians to obtain quantitative measurements to support scientific hypotheses and medical diagnoses. This book offers an overview of a range of proven and new methods, discussing both theoretical and practical aspects of biomedical signal analysis and interpretation.After an introduction to the topic and a survey of several processing and imaging techniques, the book describes a broad range of methods, including continuous and discrete Fourier transforms, independent component analysis (ICA), dependent component analysis, neural networks, and fuzzy logic methods. The book then discusses applications of these theoretical tools to practical problems in everyday biosignal processing, considering such subjects as exploratory data analysis and low-frequency connectivity analysis in fMRI, MRI signal processing including lesion detection in breast MRI, dynamic cerebral contrast-enhanced perfusion MRI, skin lesion classification, and microscopic slice image processing and automatic labeling. Biomedical Signal Analysis can be used as a text or professional reference. Part I, on methods, forms a self-contained text, with exercises and other learning aids, for upper-level undergraduate or graduate-level students. Researchers or graduate students in systems biology, genomic signal processing, and computer-assisted radiology will find both parts I and II (on applications) a valuable handbook.

About the Authors

Fabian J. Theis is head of Computational Modeling in Biology at the Institute of Bioinformatics at the German Research Center for Environmental Health (GSF), Neuherberg, Germany.

Anke Meyer-Bäse is Associate Professor in the Department of Scientific Computing at Florida State University. She is the author of Pattern Recognition in Medical Imaging.