Search for


TEXT SIZE

search for



CrossRef (0)
Classification of 18F-Florbetaben Amyloid Brain PET Image using PCA-SVM
Biomed Sci Letters 2019;25:99-106
Published online March 31, 2019;  https://doi.org/10.15616/BSL.2019.25.1.99
© 2019 The Korean Society For Biomedical Laboratory Sciences.

Kook Cho1,짠,**, Woong-Gon Kim3,짠,**, Hyeon Kang1,*, Gyung-Seung Yang5,**, Hyun-Woo Kim4,*, Ji-Eun Jeong1,2,**, Hyun-Jin Yoon1,2,**, Young-Jin Jeong1,2,*** and Do-Young Kang1,2,,***

1Institute of Convergence Bio-Health, Dong-A University, Busan 49201, Korea 2Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine, Busan 49201, Korea 3Economic Survey, Gyeongin Regional Statistics Office, Gwacheon 13809, Korea 4Department of Industrial Engineering, Hanyang University, Seoul 04763, Korea 5Ubicod Company, Seoul 08381, Korea
Correspondence to: Do-Young Kang. Department of Nuclear Medicine, Dong-A University Medical Center, Dong-A University College of Medicine, #26 Daesingongwon-ro, Seo-gu, Busan 49201, Korea.
Tel: +82-51-240-5630, Fax: +82-51-242-7237, e-mail: dykang@dau.ac.kr
These authors contributed equally to this manuscript.
Received December 27, 2018; Revised January 14, 2019; Accepted January 14, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
 Abstract
Amyloid positron emission tomography (PET) allows early and accurate diagnosis in suspected cases of Alzheimer's disease (AD) and contributes to future treatment plans. In the present study, a method of implementing a diagnostic system to distinguish 棺-Amyloid (A棺) positive from A棺 negative with objectiveness and accuracy was proposed using a machine learning approach, such as the Principal Component Analysis (PCA) and Support Vector Machine (SVM). 18F-Florbetaben (FBB) brain PET images were arranged in control and patients (total n = 176) with mild cognitive impairment and AD. An SVM was used to classify the slices of registered PET image using PET template, and a system was created to diagnose patients comprehensively from the output of the trained model. To compare the per-slice classification, the PCA-SVM model observing the whole brain (WB) region showed the highest performance (accuracy 92.38, specificity 92.87, sensitivity 92.87), followed by SVM with gray matter masking (GMM) (accuracy 92.22, specificity 92.13, sensitivity 92.28) for A棺 positivity. To compare according to per-subject classification, the PCA-SVM with WB also showed the highest performance (accuracy 89.21, specificity 71.67, sensitivity 98.28), followed by PCA-SVM with GMM (accuracy 85.80, specificity 61.67, sensitivity 98.28) for A棺 positivity. When comparing the area under curve (AUC), PCA-SVM with WB was the highest for per-slice classifiers (0.992), and the models except for SVM with WM were highest for the per-subject classifier (1.000). We can classify 18F-Florbetaben amyloid brain PET image for A棺 positivity using PCA-SVM model, with no additional effects on GMM
Keywords : Alzheimer's disease, Gray matter, 棺-Amyloid, PCA, SVM, 18F-FBB PET