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,k-nearest neighbors, MR images, classification, wavelet energy
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