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Multi stage classification and segmentation of brain tumor

In this paper, a multi stage approach has been proposed to detect tumor, classify them into glioma or meningioma and perform their segmentation. In the first stage, preprocessing is performed, which includes noise filtering, image cropping, scaling operations and histogram equalisation. Feature extraction is performed in the second phase in which 19 features are extracted from Gray Level Co-occurrence Matrix, Gray Level Run Length Matrix and histogram based techniques which are the input to the classifier. Random forest classifier has been used to classify Magnetic Resonance Images into normal or abnormal and if abnormal, into glioma or meningioma. Final phase deals with the tumor detection performed using Fast Bounding Box and the tumor segmentation using active contour model. The experiments has been carried out on 120 patients, out of which 38 were glioma cases, 57 were meningioma cases and 25 were normal healthy subjects. The classification accuracy using random forest classifier was found to be 87.62%.