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Discriminant analysis in the study of Alzheimer’s disease using feature extractions and support vector machines in positron emission tomography with 18F-FDG

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Abstract

With more successful applications of advanced medical imaging technologies in clinical diagnosis, various analytic discriminant approaches, by seeking the imaging based characteristics of a given disease to achieve automatic diagnosis, gain greater attention in the medical community. However the existing computer-aided discriminant procedures for Alzheimer’s disease (AD) are yet to be improved for better identifying patients with mild cognitive impairment (MCI) from those with AD and those who are cognitively normal. In this work we present a computer assisted diagnosis approach by first statistically extracting characteristics from whole brain 2-deoxy-2-(18F)fluoro-D-glucose positron emission tomography (18F-FDG PET) images, and then using support vector machines for classification. Evaluations of the proposed procedure with patient data exhibit satisfactory accuracies in distinguishing AD from its early stage MCI, and normal controls.

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Authors and Affiliations

  1. School of Biomedical Engineering, Shanghai Jiaotong University, Shanghai, 200030, China

    Sai-sai Su  (苏赛赛), Ke-wei Chen  (陈克伟) & Qiu Huang  (黄 秋)

  2. Banner Alzheimer’s Institute, Phoenix, AZ, 85006, USA

    Ke-wei Chen  (陈克伟)

Authors
  1. Sai-sai Su  (苏赛赛)
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  2. Ke-wei Chen  (陈克伟)
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  3. Qiu Huang  (黄 秋)
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Corresponding author

Correspondence to Qiu Huang  (黄 秋).

Additional information

Foundation item: the National Natural Science Foundation of China (No. 81201114), the Shanghai Municipal Natural Science Foundation (No. 11ZR1416700), and the Innovation Program of Shanghai Municipal Education Commission (No. 13ZZ017)

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Su, Ss., Chen, Kw. & Huang, Q. Discriminant analysis in the study of Alzheimer’s disease using feature extractions and support vector machines in positron emission tomography with 18F-FDG. J. Shanghai Jiaotong Univ. (Sci.) 19, 555–560 (2014). https://doi.org/10.1007/s12204-014-1540-4

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  • DOI: https://doi.org/10.1007/s12204-014-1540-4

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