  Tissue texture is known to exhibit a heterogeneous or non-stationary nature,
therefore using a single resolution approach for optimum classification might
not suffice. A clinical decision support system that exploits the subband
textural fractal characteristics for best bases selection of meningioma brain
histopathological image classification is proposed. Each subband is analysed
using its fractal dimension instead of energy, which has the advantage of being
less sensitive to image intensity and abrupt changes in tissue texture. The
most significant subband that best identifies texture discontinuities will be
chosen for further decomposition, and its fractal characteristics would
represent the optimal feature vector for classification. The performance was
tested using the support vector machine (SVM), Bayesian and k-nearest neighbour
(kNN) classifiers and a leave-one-patient-out method was employed for
validation. Our method outperformed the classical energy based selection
approaches, achieving for SVM, Bayesian and kNN classifiers an overall
classification accuracy of 94.12%, 92.50% and 79.70%, as compared to 86.31%,
83.19% and 51.63% for the co-occurrence matrix, and 76.01%, 73.50% and 50.69%
for the energy texture signatures, respectively. These results indicate the
potential usefulness as a decision support system that could complement
radiologists diagnostic capability to discriminate higher order statistical
textural information, for which it would be otherwise difficult via ordinary
human vision.
