Clinical Decision Support System for Fractal analysis of histological brain tumors

What is it about?

Clinical decision support system for histopathological brain tumor classification

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Why is it important?

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 subbands’ 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, Bayesian and k-nearest neighbour classifiers and a leave-one-patient-out method was employed for validation.

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