  Meningioma brain tumour discrimination is challenging as many histological
patterns are mixed between the different subtypes. In clinical practice,
dominant patterns are investigated for signs of specific meningioma pathology;
however the simple observation could result in inter- and intra-observer
variation due to the complexity of the histopathological patterns. Also
employing a computerised feature extraction approach applied at a single
resolution scale might not suffice in accurately delineating the mixture of
histopathological patterns. In this work we propose a novel multiresolution
feature extraction approach for characterising the textural properties of the
different pathological patterns (i.e. mainly cell nuclei shape, orientation and
spatial arrangement within the cytoplasm). The pattern textural properties are
characterised at various scales and orientations for an improved separability
between the different extracted features. The Gabor filter energy output of
each magnitude response was combined with four other fixed-resolution texture
signatures (2 model-based and 2 statistical-based) with and without cell nuclei
segmentation. The highest classification accuracy of 95% was reported when
combining the Gabor filters energy and the meningioma subimage fractal
signature as a feature vector without performing any prior cell nuceli
segmentation. This indicates that characterising the cell-nuclei
self-similarity properties via Gabor filters can assists in achieving an
improved meningioma subtype classification, which can assist in overcoming
variations in reported diagnosis.
