TY - JOUR
T1 - Wavelets and imaging informatics
T2 - A review of the literature
AU - Wang, James Z.
N1 - Funding Information:
This work was supported in part by the National Science Foundation under Grant IIS-9817511 and an endowment from the PNC Foundation. The author thanks Russ Altman, Xiaoming Huo, Jia Li, Hector Garcia-Molina, Gio Wiederhold, Stephen Wong, and anonymous reviewers for their help.
PY - 2001
Y1 - 2001
N2 - Modern medicine is a field that has been revolutionized by the emergence of computer and imaging technology. It is increasingly difficult, however, to manage the ever-growing enormous amount of medical imaging information available in digital formats. Numerous techniques have been developed to make the imaging information more easily accessible and to perform analysis automatically. Among these techniques, wavelet transforms have proven prominently useful not only for biomedical imaging but also for signal and image processing in general. Wavelet transforms decompose a signal into frequency bands, the width of which are determined by a dyadic scheme. This particular way of dividing frequency bands matches the statistical properties of most images very well. During the past decade, there has been active research in applying wavelets to various aspects of imaging informatics, including compression, enhancements, analysis, classification, and retrieval. This review represents a survey of the most significant practical and theoretical advances in the field of wavelet-based imaging informatics.
AB - Modern medicine is a field that has been revolutionized by the emergence of computer and imaging technology. It is increasingly difficult, however, to manage the ever-growing enormous amount of medical imaging information available in digital formats. Numerous techniques have been developed to make the imaging information more easily accessible and to perform analysis automatically. Among these techniques, wavelet transforms have proven prominently useful not only for biomedical imaging but also for signal and image processing in general. Wavelet transforms decompose a signal into frequency bands, the width of which are determined by a dyadic scheme. This particular way of dividing frequency bands matches the statistical properties of most images very well. During the past decade, there has been active research in applying wavelets to various aspects of imaging informatics, including compression, enhancements, analysis, classification, and retrieval. This review represents a survey of the most significant practical and theoretical advances in the field of wavelet-based imaging informatics.
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U2 - 10.1006/jbin.2001.1010
DO - 10.1006/jbin.2001.1010
M3 - Review article
C2 - 11515412
AN - SCOPUS:0034890226
SN - 1532-0464
VL - 34
SP - 129
EP - 141
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
IS - 2
ER -