  Providing an improved technique which can assist pathologists in correctly
classifying meningioma tumours with a significant accuracy is our main
objective. The proposed technique, which is based on optimum texture measure
combination, inspects the separability of the RGB colour channels and selects
the channel which best segments the cell nuclei of the histopathological
images. The morphological gradient was applied to extract the region of
interest for each subtype and for elimination of possible noise (e.g. cracks)
which might occur during biopsy preparation. Meningioma texture features are
extracted by four different texture measures (two model-based and two
statistical-based) and then corresponding features are fused together in
different combinations after excluding highly correlated features, and a
Bayesian classifier was used for meningioma subtype discrimination. The
combined Gaussian Markov random field and run-length matrix texture measures
outperformed all other combinations in terms of quantitatively characterising
the meningioma tissue, achieving an overall classification accuracy of 92.50%,
improving from 83.75% which is the best accuracy achieved if the texture
measures are used individually.
