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Tissue texture is known to exhibit a heterogeneous or non-stationary nature,therefore using a single resolution approach for optimum classification mightnot suffice. A clinical decision support system that exploits the subbandtextural fractal characteristics for best bases selection of meningioma brainhistopathological image classification is proposed. Each subband is analysedusing its fractal dimension instead of energy, which has the advantage of beingless sensitive to image intensity and abrupt changes in tissue texture. Themost significant subband that best identifies texture discontinuities will bechosen for further decomposition, and its fractal characteristics wouldrepresent the optimal feature vector for classification. The performance wastested using the support vector machine (SVM), Bayesian and k-nearest neighbour(kNN) classifiers and a leave-one-patient-out method was employed forvalidation. Our method outperformed the classical energy based selectionapproaches, achieving for SVM, Bayesian and kNN classifiers an overallclassification 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 thepotential usefulness as a decision support system that could complementradiologists diagnostic capability to discriminate higher order statisticaltextural information, for which it would be otherwise difficult via ordinaryhuman vision.

A Multiresolution Clinical Decision Support System Based on Fractal Model Design for Classification of Histological Brain Tumours