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Discrimination of Poorly Distinguishable Random Textures by Statistical Analysis of Morphological Spectra

Przytulska M., Kulikowski J. L., Wierzbicka D.

Machine Graphics and Vision

2011

Vol. 20, No. 4

455-477

The paper describes a method for discrimination of poorly distinguishable textures based on application of morphological spectra. The textures are analyzed as random fields of specific probability distributions. The samples of textures are thus considered as their instances and so are also their morphological spectra. Some basic properties of morphological spectra, as well as the definition of similarity measure are shortly reminded. The problem of textures discrimination is formulated as similarity assessment of spectral components histograms. For this purpose, various statistics like: mean value, standard deviation, skewness and kurtosis, as well as some secondary statistics based on theformer, are used. A discriminating index is introduced for evaluation of their discriminating properties. The method of evaluating the discriminating power of statistics based on 1st and 2nd level morphological spectra is illustrated by analysis of the spectra of USG liver images in the groups of healthy persons and patients affected by liver fibrosis. A short description of a IASS program used to the calculations is given. The problem of textures discrimination invariant to rotations and parallel translations of images is described. It is shown that the proposed method discriminates statistically the “ill” and “healthy” textures despite the fact that the differences between them are visually not distinguishable.

Discrimination of biomedical textures based on logical similarity measure

Kulikowski J. L., Przytulska M., Wierzbicka D.

Journal of Medical Informatics & Technologies

2011

Vol. 17

89--95

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.