On the selection of an optimal wavelet basis for texture characterization.

Although most of the theoretical and implementation aspects of wavelet based algorithms in texture characterization are well studied and understood, many issues related to the choice of filter bank in texture processing remain unresolved. The impact of the wavelet basis has been mentioned in a few papers, whereas other more detailed investigations have considered only the choice of the wavelet basis in image coding. For example, regularity, number of vanishing moments and a shift variance degree have been used for the filter evaluation in image coding, but the utility of all these metrics in texture analysis has not yet been established.

Wavelet image extension for analysis and classification of infarcted myocardial tissue.

Some computer applications for tissue characterization in medicine and biology, such as analysis of the myocardium or cancer recognition, operate with tissue samples taken from very small areas of interest. In order to perform texture characterization in such an application, only a few texture operators can be employed: the operators should be insensitive to noise and image distortion and yet be reliable in order to estimate texture quality from the small number of image points available. In order to describe the quality of infarcted myocardial tissue, we propose a new wavelet-based approach for analysis and classification of texture samples with small dimensions.