Abstract: Wavelet transforms are efficient tools for texture analysis and classification. Separable techniques are classically used but present several drawbacks. First, diagonal coefficients contain poor information. Second, the other coefficients contain useful information only if the texture is oriented in the vertical and horizontal directions. So an approach of texture analysis by non-separable transform is proposed. An improved interscale resolution is allowed by the quincunx scheme and this analysis leads to only one detail image where no particular orientation is favored. New orthogonal isotropic filters for the decomposition are constructed by applying McClellan transform on one dimension B-spline filters. The obtained wavelet function have better isotropic and frequency properties than those previously proposed by Feauveau. Since IIR filters are obtained, an integration in Fourier domain of the whole operations of the transform is proposed. A texture analysis is performed on wavelet details coefficients. Simple parameters are calculated from each scale. Finally, the evolution over scales of the parameters is obtained and this multiscale parameter is used to characterize the different textures. An application of this method is posed with the analysis of human cells. The aim is to distinguish states of evolution. As no information is provided by monoscale classical methods on these images, the proposed process allows to identify several states. In this process a reference curve is constructed for each states calculated from the multiscale variance of known images. When a new image is analyzed, a new evolution curve is calculated and a measure of the distance with the references is done. This technique is more efficient than classical ones as multiscale information is used. !11
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