Discrimination of biomedical textures based on logical similarity measure

The paper presents an approach to discrimination of textures in radiological images based on multi-aspect similarity measures composed of logical tests. There are formulated basis assumptions for similarity measures which can be composed by products of partial (single-aspect) similarity measures. On the basis of similarity measures -similarity classes are defined. Next, two types: strong and weak similarity measures are defined. It is shown that they make possible to define similarity measures based on quality objects properties as well as on their numerical parameters. As an example of application of the general concept discrimination of normal and ill (lesions affected) tissues is considered. It is illustrated by analysis of USG images of liver tissues for which morphological spectra and their statistical parameters have been calculated. It is shown that the differences between values of some pairs of corresponding parameters can be used to a construction of an effective algorithm of textures discrimination. This algorithm takes into consideration both, numerical features of the texture samples and some qualitative data concerning the patients. Conclusions are formulated at the end of the paper.