Xia Yong, Lu Shen, Wen Lingfeng, Eberl Stefan, Fulham Michael, Feng David Dagan
Shaanxi Provincial Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China ; Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia ; Department of Molecular Imaging, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia.
Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia.
Biomed Res Int. 2014;2014:421743. doi: 10.1155/2014/421743. Epub 2014 Feb 2.
Parametric FDG-PET images offer the potential for automated identification of the different dementia syndromes. However, various existing image features and classifiers have their limitations in characterizing and differentiating the patterns of this disease. We reported a hybrid feature extraction, selection, and classification approach, namely, the GA-MKL algorithm, for separating patients with suspected Alzheimer's disease and frontotemporal dementia from normal controls. In this approach, we extracted three groups of features to describe the average level, spatial variation, and asymmetry of glucose metabolic rates in 116 cortical volumes. An optimal combination of features, that is, capable of classifying dementia cases was identified by a genetic algorithm- (GA-) based method. The condition of each FDG-PET study was predicted by applying the selected features to a multikernel learning (MKL) machine, in which the weighting parameter of each kernel function can be automatically estimated. We compared our approach to two state-of-the-art dementia identification algorithms on a set of 129 clinical cases and improved the performance in separating the dementia types, achieving accuracy of 94.62%. There is a very good agreement between the proposed automated technique and the diagnosis made by clinicians.
参数化FDG-PET图像为自动识别不同的痴呆综合征提供了可能。然而，现有的各种图像特征和分类器在表征和区分这种疾病的模式方面存在局限性。我们报告了一种混合特征提取、选择和分类方法，即GA-MKL算法，用于将疑似阿尔茨海默病和额颞叶痴呆患者与正常对照区分开来。在这种方法中，我们提取了三组特征来描述116个皮质区域葡萄糖代谢率的平均水平、空间变化和不对称性。通过基于遗传算法（GA）的方法确定了能够对痴呆病例进行分类的特征的最佳组合。通过将所选特征应用于多核学习（MKL）机器来预测每项FDG-PET研究的情况，其中每个核函数的加权参数可以自动估计。我们在一组129个临床病例上，将我们的方法与两种最先进的痴呆识别算法进行了比较，并提高了区分痴呆类型的性能，准确率达到了94.62%。所提出的自动化技术与临床医生的诊断之间有非常好的一致性。
