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In this paper, a complete and fully automatic MRI brain tumour detection and segmentation methodology is presented as an efficient clinical-aided tool using Gaussian mixture model, Fuzzy C-Means, active contour, wavelet transform and entropy segmentation methods. The proposed algorithm is based on two main parts: the skull stripping and tumour auto-detection and segmentation. The first part was evaluated using IBSR, LPBA40 and OASIS databases, and the obtained results show that our proposed method outclasses the best popular algorithms of brain extraction with scores of 0.913, 0.954 and 0.957 for the Jaccard index, Dice coefficient and sensitivity, respectively. The second part has been evaluated using BRATS database; this methodology has achieved an accuracy of 69% of true detection, and a false detection is around 22% of healthy cases detected as tumour cases and a false detection is around 9 …