Download e-book for kindle: Biomedical Imaging: Principles and Applications by Reiner Salzer

By Reiner Salzer

ISBN-10: 0470648473

ISBN-13: 9780470648476

This publication offers and describes imaging applied sciences that may be used to review chemical methods and structural interactions in dynamic platforms, mostly in biomedical platforms. The imaging applied sciences, principally biomedical imaging applied sciences reminiscent of MRT, Fluorescence mapping, raman mapping, nanoESCA, and autos microscopy, were chosen in keeping with their program diversity and to the chemical details content material in their facts. those applied sciences enable for the research and review of soft organic samples, which mustn't ever be disturbed in the course of the profess. finally, this can suggest fewer animal lab checks and medical trials.

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Extra resources for Biomedical Imaging: Principles and Applications

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Probably the best example for this is the PET method. Here, the images represent quantitatively the number of radioactive decays of the applied tracer in the respective image pixel. Since this number in turn is directly proportional to IMAGE RECONSTRUCTION 33 the amount of tracer substance in the corresponding pixel volume, one obtains a direct measure of this quantity. PET images are therefore directly interpretable as quantitative tracer concentration maps. For other modalities the situation usually is a bit more complicated.

Leaving further mathematical details as well as considerations concerning the consequences of violating the assumptions (discrete point samples from a bandlimited signal) aside, the sampling theorem implies that a reasonable estimate of the sampling rate required for approximately restoring the continuous Gaussian g(x) from discrete samples is two times the above limiting frequency, that is, 2/σ .

8. Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM Comput Surv 1991;31:264. 9. Fukunaga K. Introduction to Statistical Pattern Recognition. 2nd ed. London: Academic Press; 1990. 10. Richards JA, Jia X. Remote Sensing Digital Image Analysis. Berlin: Springer; 1999. 11. Mao J, Jain AK. A self-organizing network for hyperellipsoidal clustering. IEEE Trans Neural Networks 1996;7:16–29. 12. Fraley C. Algorithms for model-based Gaussian hierarchical clustering. SIAM J Sci Comput 1998;20:270–281.

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Biomedical Imaging: Principles and Applications by Reiner Salzer


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