Abstract
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 medical images in different colour spaces. We compare these features in order to identify the features set able to properly classify medical images presenting different classification problems. Furthermore, we investigate different colour spaces to identify most suitable for this purpose. The feature sets tested 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 in general features extracted from the HSV colour space perform better than the other and that the best feature subset has been obtained from the generalized Grey-Level Co-Occurrence Matrix, demonstrating excellent performances for this purpose.
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Di Ruberto, C., Fodde, G., Putzu, L. (2015). On Different Colour Spaces for Medical Colour Image Classification. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_40
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DOI: https://doi.org/10.1007/978-3-319-23192-1_40
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