Classification of the ultrasound liver images with the 2N×1-D wavelet transform

The authors propose a new separable extension of the 1-D wavelet transform to the 2-D case and describe its application to the texture characterization problem. Comparing to previous decompositions, with the same resolution levels for each subband in horizontal and vertical directions, the new method has different resolutions for different directions. The new algorithm is applied to 84 ultrasound liver images, to detect liver cirrhosis in it´s early stage. The classification accuracy was 92%. The method was compared to other texture description methods (gray level cooccurrence, Laws filters, pyramid and tree structured wavelet decompositions). The proposed 2N×1-D wavelet decomposition, gave the highest classification rate, showing its applicability for the approach based analysis of the large class of natural textures