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Brain Tumour Diagnostic Support Based on Medical Image Segmentation

High-grade gliomas represent rapidly growing malignant brain tumours. Early diagnostics of this decease and immediately applied treatment entails better life prognosis for the patient. The goal of this work is to initialize the development of an automated tumour recognition method based on computed tomography images processing. The resulting method is aimed at early glioma diagnostics support by distinguishing between the healthy tissue and the tumour tissue. The proposed technique involves, subsequently, image preprocessing, feature extraction, and classification of the extracted features using an artificial neural network. To obtain features, we compute the skewness of the discrete wavelet transform coefficients from selected rectangular image regions using three neighboring image slices to increase the number of samples while preserving good segmentation resolution within the image. The segmentation results are evaluated in cooperation with the neurologist. For all computations and visualizations, we exploit Matlab.