2015 Seventh International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) (2015)
Dec. 12, 2015 to Dec. 14, 2015
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PAAP.2015.26
(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.
Brain, Yttrium, Discrete wavelet transforms, Classification algorithms, Sensitivity, Magnetic resonance imaging
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", 2015 Seventh International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), vol. 00, no. , pp. 87-91, 2015, doi:10.1109/PAAP.2015.26