Comparison of Statistical Features for Medical Colour Image Classification

Analysis of cells and tissues allow the evaluation and diagnosis of a vast number of diseases. Nowadays this analysis is still performed manually, involving numerous drawbacks, in particular the results accuracy heavily depends on the operator skills. Differently, the automated analysis by computer is performed quickly, requires only one image of the sample and provides precise results. In this work we investigate different texture descriptors extracted from colour medical images. We compare and combine these features in order to identify the features set able to properly classify medical images presenting different classification problems. The tested feature sets are based on a generalization of some existent grey scale approaches for feature extraction to colour images. The generalization has been applied to the calculation of Grey-Level Co-Occurrence Matrix, Grey-Level Difference Matrix and Grey-Level Run-Length Matrix. Furthermore, we calculate Grey-Level Run-Length Matrix starting from the Grey-Level Difference Matrix. The resulting feature sets performances have been compared using the Support Vector Machine model. To validate our method we have used three different databases, HistologyDS, Pap-smear and Lymphoma, that present different medical problems and so they represent different classification problems. The obtained experimental results have showed that the features extracted from the generalized Grey-Level Co-Occurrence Matrix perform better than the other set of features, demonstrating also that a combination of features selected from all the feature subsets leads always to better performances.