A comparison of daubechies and gabor wavelets for classification of MR images
2007 IEEE International Conference on Signal Processing and Communications (ICSPC 2007), 676–679
Abstract
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This 2007 conference paper compares Daubechies and Gabor wavelet features for classifying magnetic resonance images, using support vector machines. The authors report stronger classification performance with Gabor features in their experiments; the results should be understood in the context of that study, not as evidence of clinical readiness.
Summary
Ulas Bagci and Li Bai compare image features derived from Daubechies and Gabor wavelets for classifying magnetic resonance images into normal and abnormal groups. The paper describes using support vector machines with different kernel functions and reports that Gabor wavelet features performed better than Daubechies wavelets in the experiments. The reported performance is specific to the study; the available source does not establish prospective validation or clinical readiness.
The paper appeared on pages 676–679 of the 2007 IEEE International Conference on Signal Processing and Communications (ICSPC 2007). Read the paper.
Cite this work
Ulas Bagci; Li Bai (2007). A comparison of daubechies and gabor wavelets for classification of MR images. 2007 IEEE International Conference on Signal Processing and Communications (ICSPC 2007), 676–679 https://doi.org/10.1109/ICSPC.2007.4728409
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