Mudali D, Teune L K, Renken R J, Leenders K L, Roerdink J B T M
Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Nijenborgh 9, 9747 AG Groningen, Netherlands.
Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, Netherlands.
Comput Math Methods Med. 2015;2015:136921. doi: 10.1155/2015/136921. Epub 2015 Mar 31.
Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.
像氟代脱氧葡萄糖正电子发射断层扫描（FDG-PET）这样的医学成像技术已被用于辅助神经退行性脑疾病的鉴别诊断。在本研究中，目标是将帕金森综合征（帕金森病、多系统萎缩和进行性核上性麻痹）患者的FDG-PET脑部扫描与健康对照进行分类。将缩放子轮廓模型/主成分分析（SSM/PCA）方法应用于FDG-PET脑图像数据，以获得协方差模式和相应的受试者分数。后者被用作通过C4.5决策树方法进行监督分类的特征。采用留一法交叉验证来确定分类器性能。我们与其他类型的分类器进行了比较。决策树分类的一大优势是结果易于人类理解。决策树的可视化表示有力地支持了解释过程，这在医学诊断背景下非常重要。基于增加训练数据数量、通过装袋增强决策树方法以及基于（功能）磁共振成像数据添加额外特征，提出了进一步的改进建议。
