(Aim) It is of great importance to find abnormal or pathological brains in the early stage, to save hospital and social resources. However, potential of wavelet-energy is not widely used in this field. (Method) The popular "wavelet-energy" is regarded as a prevalent feature descriptor, which achieves good performance in many applications. In this work, we propose a wavelet-energy based new method for classification of magnetic resonance brain images. The approach is a three-stage system, including wavelet decomposition, energy extraction, and k-Nearest Neighbors algorithm. (Results) The proposed approach achieved excellent performance with a sensitivity of 93.75%, a specificity of 100%, and an accuracy of 95.45%. (Conclusion) Its performance is comparable to the state-of-the-art methods. It provides a new approach to detect features indicative of abnormal and pathological brains.
['Guangshuai Zhang', 'Zhihai Lu', 'Genlin Ji', 'Ping Sun', 'Jianfei Yang', 'Yudong Zhang']
Automated Classification of Brain MR Images by Wavelet-Energy and k-Nearest Neighbors Algorithm