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With the increasingly mature medical imaging technology, super-resolution imaging and other systems have been widely applied to pathological medical image and tele-medical image transmission system has also attracted extensive heed and application. While pathological diagnosis is expedited, the transmission and storage of pathological medical image have become a problem demanding prompt solution. Due to its excellent spatial resolution and frequency resolution, wavelet analysis is particularly applicable to the analysis of non-stationary signals. As natural image has this non-stationary characteristic, it can be seen as a linear combination of signals with concentration of energy space (image edges and details) and frequency (the part of image with gradual changes). Because conventional wavelet transform requires rounding operation of wavelet coefficients, it cannot reconstruct the original lossless image. So, this paper introduces bi-orthogonal filter bank into wavelet transform, provides the conditions for accurate reconstruction of bi-orthogonal filter bank suited for wavelet transform as well as the multi-resolution analysis under bi-orthogonal condition and the construction method for bi-orthogonal wavelet, achieves the three-level orthogonal wavelet transform in the Lifting Scheme by means of symmetric and periodic extension, and concentrates the energy into a few wavelet coefficients. With the final experiment method, this paper analyzes the distribution characteristics of image wavelet coefficients, laying a foundation for selecting proper compressed encoding scheme. And the experiment result proves that the image compression conducted via bi-orthogonal wavelet transform in this paper can avoid the rounding error of computer and avails the lossless compression of medical image so as to improve image compression in both the efficiency and the accuracy. [ABSTRACT FROM AUTHOR]

An outline is presented for construction of wavelet filters with compact support. Our approach does not require any extensive simulations for obtaining the values of design variables like other methods. A unified framework is proposed for designing halfband polynomials with varying vanishing moments. Optimum filter pairs can then be generated by factorization of the halfband polynomial. Although these optimum wavelets have characteristics close to that of CDF 9/7 (Cohen-Daubechies-Feauveau), a compact support may not be guaranteed. Subsequently, we show that by proper choice of design parameters finite wordlength wavelet construction can be achieved. These hardware friendly wavelets are analyzed for their possible applications in image compression and feature extraction. Simulation results show that the designed wavelets give better performances as compared to standard wavelets. Moreover, the designed wavelets can be implemented with significantly reduced hardware as compared to the existing wavelets. [ABSTRACT FROM AUTHOR]