Biosignal and Medical Image Processing, Second Edition

Biosignal and Medical Image Processing, Second Edition

Volumn , Issue , 2011, Pages 1-451

  Semmlow, John L ^a  

^a NONE

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EID: 85069043246     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/9780203024058     Document Type: Book

References (37)

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2. Aldroubi, A., and M. Unser, eds. 1996. Wavelets in Medicine and Biology. Boca Raton, FL: CRC Press. Presents a variety of applications of wavelet analysis to biomedical engineering.
3. Boashash, B. 1992. Time-Frequency Signal Analysis. Melbourne, Australia: Longman Cheshire. Early chapters provide a very useful introduction to time-frequency analysis followed by a number of medical applications.
4. Boashash, B., and P. J. Black. 1987. An efficient real-time implementation of the Wigner-Ville distribution. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-35: 1611-18. Practical information on calculating the Wigner-Ville distribution.
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6. Boudreaux-Bartels, G. F., and R. Murray. 1995. Time-frequency signal representations for biomedical signals. In The Biomedical Engineering Handbook, ed. J. D. Bronzino, 866-85. Boca Raton, FL: CRC Press. This article presents an exhaustive, or very nearly so, compilation of Cohen’s class of time-frequency distributions.
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12. Friston, K. 2002. Statistical Parametric Mapping. Online at http://www.fil.ion.ucl.ac.uk/spm. Through discussion of practical aspects of fMRI analysis, including preprocessing, statistical methods, and experimental design. Based around SPM analysis software capabilities.
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15. Haykin, S. 1991. Adaptive Filter Theory (2nd ed.). Englewood Cliffs, NJ: Prentice-Hall. The definitive text on adaptive filters, including Weiner filters and gradient-based algorithms.
16. Hyvärinen, A., J. Karhunen, and E. Oja. 2001. Independent Component Analysis. New York: Wiley. Fundamental, comprehensive, yet readable book on independent component analysis. Also provides a good review of principal component analysis.
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21. Kak, A. C., and M. Slaney. 1988. Principles of Computerized Tomographic Imaging. New York: IEEE Press. Thorough, understandable treatment of algorithms for reconstruction of tomographic images, including both parallel and fan-beam geometry. Also includes techniques used in reflection tomography as occurs in ultrasound imaging.
22. Marple, S. L. 1987. Digital Spectral Analysis with Applications. Englewood Cliffs, NJ: Prentice-Hall. Classic text on modern spectral analysis methods. In-depth, rigorous treatment of Fourier transform, parametric modeling methods (including AR and ARMA), and eigenanalysis-based techniques.
23. Najarian, K., and R. Splinter 2006. Biomedical Signal and Image Processing. Boca Raton, FL CRC, Taylor and Francis Press. Many of the same topics covered here including the Fourier transform, wavelet transform, digital filtering and classification and clustering. This textbook also includes topics on the physiological origin of signals and image instrumentation. Some MATLAB problems.
24. Poularikas, A. D., and Z. M. Ramadan 2006. Adaptive Filtering Primer with MATLAB. Boca Raton, CRC Press, Taylor and Francis. Very detailed description of Wiener filters and the LMS algorithm. With a few MATLAB examples.
25. Principe, J., M. R. Euliano, and W. C. Lefebvre. 1999. Neural and Adaptive Systems. New York: Wiley. A textbook with detailed coverage of selected classifiers with emphasis on adaptive neural nets. Also covers general signal processing concepts. Comes with a CD-ROM that has a complete software system to implement adaptive neural nets.
26. Rangayyan, R. M. 2005. Biomedical Image Analysis. Boca Raton, FL CRC Press, Taylor and Francis. Extensive coverage of image processing in a textbook format with questions and lab exercises at the end of each chapter. Everything you wanted to know abut image processing in an accessible format.
27. Rao, R. M., and A. S. Bopardikar. 1998. Wavelet Transforms: Introduction to Theory and Applications. Reading, MA: Addison-Wesley. Good development of wavelet analysis, including both the continuous and discreet wavelet transforms.
28. Shiavi, R. 1999. Introduction to Applied Statistical Signal Analysis (2nd ed.). San Diego, CA: Academic Press. Emphasizes spectral analysis of signals buried in noise. Excellent coverage of Fourier analysis and autoregressive methods. Good introduction to statistical signal processing concepts.
29. Smith, S. W. 1997. The Scientist and Engineer’s Guide to Digital Signal Processing. San Diego: California Technical Publishing. A very clear and detailed description of basic digital signal processing concepts using a minimum of mathematics. Excellent coverage of the basics: Fourier transform, convolution basic filters. Available through the Web site http://www.DSPGuide.com.
30. Sonka, M., V. Hlavac, and R. Boyle. 1993. Image Processing, Analysis, and Machine Vision. London: Chapman & Hall. A good description of edge-based and other segmentation methods.
31. Strang, G., and T. Nguyen. 1997. Wavelets and Filter Banks. Wellesley, MA: Wellesley-Cambridge Press. Thorough coverage of wavelet filter banks, including extensive mathematical background.
32. Stearns, S. D., and R. A. David. 1996. Signal Processing Algorithms in MATLAB. Upper Saddle River, NJ: Prentice Hall. Good treatment of the classical Fourier transform and digital filters. Also covers the LMS adaptive filter algorithm. CD-ROM included.
33. Tan L. 2008. Digital Signal Processing. Burlington, MA. Elsevier Press. Detailed coverage of many of the topics in this book. Some MATLAB problems and examples.
34. Wickerhauser, M. V. 1994. Adapted Wavelet Analysis from Theory to Software. Wellesley, MA: A. K. Peters/IEEE Press. Rigorous, extensive treatment of wavelet analysis.
35. Widrow, B., J. M. Glover, J. McCool, et al. 1975. Adaptive noise cancelling: Principles and applications. Proceedings of IEEE 63: 1692-16. Classic original article on adaptive noise cancellation.
36. Williams, W. J., and J. Jeong. 1992. Reduced interference time-frequency distributions. Time-Frequency Signal Analysis. Melbourne, Australia: Longman Cheshire. Detailed description of kernels used in time-frequency distributions that reduce cross products.
37. Wright, S. 2000. Nuclear magnetic resonance and magnetic resonance imaging. In Introduction to Biomedical Engineering, ed. J. D. Enderle, S. M. Blanchard, and J. D. Bronzino, 783-838. San Diego, CA: Academic Press. Good mathematical development of the physics of MRI using classical concepts.