title: Optimization of wavelet decomposition for image compression and feature preservation.
abstract: A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in mammograms and bone in computed tomography (CT) head images. We have used this method to evaluate the performance of tap-4 wavelets on mammograms, CTs, magnetic resonance images, and Lena images. We found that the Daubechies wavelet or those wavelets with similar filtering characteristics can produce the highest compression efficiency with the smallest mean-square-error for many image patterns including general image textures as well as microcalcifications in digital mammograms. However, the Haar wavelet produces the best results on sharp edges and low-noise smooth areas. We also found that a special wavelet whose low-pass filter coefficients are 0.32252136, 0.85258927, 1.38458542, and -0.14548269) produces the best preservation outcomes in all tested microcalcification features including the peak signal-to-noise ratio, the contrast and the figure of merit in the wavelet lossy compression scheme. Having analyzed the spectrum of the wavelet filters, we can find the compression outcomes and feature preservation characteristics as a function of wavelets. This newly developed optimization approach can be generalized to other image analysis applications where a wavelet decomposition is employed.
authors.affiliation: Center of Imaging Science and Information Systems, Radiology Department, Georgetown University Medical Center, 2115 Wisconsin Avenue. N.W., Suite 603, Washington, D.C. 20007, USA. lo@isis.imac.georgetown.edu
authors.forename: Shih-Chung B
authors.initials: SC
authors.lastname: Lo
authors.forename: Huai
authors.initials: H
authors.lastname: Li
authors.forename: Matthew T
authors.initials: MT
authors.lastname: Freedman
mesh_terms: D000465:Algorithms; D001941:Breast Diseases / Q000000981:diagnostic imaging; D002114:Calcinosis / Q000000981:diagnostic imaging; D044962:Data Compression / Q000379:methods*; D006257:Head / Q000000981:diagnostic imaging; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008327:Mammography / Q000379:methods; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted