Abstract：Alzheimer′s disease (AD) is an irreversible neurodegenerative disease. PiB PET imaging can be used for AD diagnosis in the early stage. However, AD clinical diagnosis based on PiB PET technology is mainly based on visual assessment currently, which relies on the subjective experience of radiologists, leading to inaccurate and inadequate results. This study therefore proposed a computer aided AD diagnosis (CAAD) approach. The CAAD approach applied 3D thresholding-lattice boltzmann model foramyloid-beta (Aβ) ROIs segmentation, PCA method for feature extraction, and SVM with polynomial kernel for AD classification. In order to verify the CAAD approach, comparison experiments were carried out by selecting a total of 149 PiB PET datasheets from ADNI database and PET center of HuashanHospital (Shanghai). Results showed that the average accuracy rate of ROIs segmentation was 91.53%±3.0% in term of Dice coefficient. Besides, the classification accuracy of the novel CAAD approach for AD-healthy control (HC), mild cognitive impairment (MCI)\|HC and AD/MCI-HC achieved 87.01%, 93.04% and 91.95%, respectively. Compared with the AD diagnosis methods in literatures, the CAAD approach could achieve a better accuracy of about 10% higher. These results indicated that the CAAD approach proposed in this study achieved the excellent performance for AD, MCI and HC classification.
基金资助:教育部留学回国人员科研启动基金(1685);上海市高校青年教师培训资助计划# 中国生物医学工程学会高级会员(Senior member, Chinese Society of Biomedical Engineering)
段火强 舒星辉 徐俊 蒋皆恢. 基于PiB PET图像感兴趣区域的阿尔茨海默症计算机辅助分析[J]. 中国生物医学工程学报, 2016, 35(6): 641-647.
Duan Huoqiang Shu Xinghui Xu Jun Jiang Jiehui. A Novel Computer Aided Alzheimer′s Analysis Approach Based on Regions of Interests of PiB PET Images. Chinese Journal of Biomedical Engineering, 2016, 35(6): 641-647.
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