Poullet Jean-Baptiste, Martinez-Bisbal M Carmen, Valverde Dani, Monleon Daniel, Celda Bernardo, Arús Carles, Van Huffel Sabine
Department of Electrical Engineering, SCD-SISTA, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001 Heverlee (Leuven), Belgium.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:5407-10. doi: 10.1109/IEMBS.2007.4353565.
The goal of this work is to propose a complete protocol (preprocessing, processing and classification) for classifying brain tumors with proton high-resolution magic-angle spinning ((1)H HR-MAS) data. The different steps of the procedure are detailed and discussed. Feature extraction techniques such as peak integration, including also the automated quantitation method AQSES, were combined with linear (LDA) and non-linear (least-squares support vector machine or LS-SVM) classifiers. Classification accuracy was assessed using a stratified random sampling scheme. The results suggest that LS-SVM performs better than LDA while AQSES performs better than the standard peak integration feature extraction method.
这项工作的目标是提出一个完整的方案（预处理、处理和分类），用于利用质子高分辨率魔角旋转（(1)H HR-MAS）数据对脑肿瘤进行分类。详细阐述并讨论了该过程的不同步骤。将诸如峰积分等特征提取技术（还包括自动定量方法AQSES）与线性（LDA）和非线性（最小二乘支持向量机或LS-SVM）分类器相结合。使用分层随机抽样方案评估分类准确性。结果表明，LS-SVM的性能优于LDA，而AQSES的性能优于标准峰积分特征提取方法。
