Independent Component Analysis Applied to Ultrasound Speckle Texture Analysis and Tissue Characterization

Analysis of ultrasound speckle texture will provide us information about the underlying properties of tissue, could find applications in early lesion detection and tissue characterization. Traditional first and second order statistics based approaches ignore the higher order statistics information in the texture. On the other hand, conventional multichannel filtering or multiresolution analysis approaches rely on the predefined analytical bases which are not fully adaptive to the data being analyzed. In this paper independent component analysis (ICA), which is based on higher order statistics, is proposed to deal with the ultrasound speckle texture analysis problem. ICA image bases obtained from the training images are applied as a filter bank to the testing images. Then the independent features containing higher order statistics information can be extracted from the marginal distributions of the filtered images. ICA is used here as a dimensionality reduction tool to overcome the difficulty of estimating high dimensional joint density of texture. Support Vector Machine (SVM) is then used as a classifier to classify the tissues. By using the digitally simulated tissues and corresponding B-scan images, we can further correlate the change of tissue microstructure or change of imaging conditions with the change of the ICA feature vectors. Our numerical simulation has shown ICA to be a promising technique for ultrasound speckle texture analysis and tissue characterization compared with some traditional methods such as PCA and Gabor transform.