Computer aided diagnosis of functional brain disorders using PET on a PC based platform. Spetsieris, P. G; Dhawan, V.; Ma, Y.; Moeller, J. R; Mentis, M. J; and Eidelberg, D. In Engineering in Medicine and Biology, 2002. 24\textsuperscriptth Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint, volume 2, pages 1107-- 1108 vol.2, 2002. IEEE.
doi  abstract   bibtex   
We describe a portable PC based platform for computer aided diagnosis (CAD) of functional brain disorders using positron emission tomography (PET) that integrates a variety of methods including quantitative and visual techniques as well as extensively automated procedures. Statistical procedures such as the scaled subprofile model (SSM) in conjunction with principal component analysis are employed to derive neuronal networks that can uniquely identify characteristic metabolic covariance patterns. By measuring the expression of these networks in diseased subjects on an individual basis we can help automate the diagnosis of conditions such as Parkinson's disease and evaluate treatment efficacy and disease progression.
@InProceedings{Spetsieris2002,
  author =    {Spetsieris, P. G and Dhawan, V. and Ma, Y. and Moeller, J. R and Mentis, M. J and Eidelberg, D.},
  title =     {{Computer aided diagnosis of functional brain disorders using {PET} on a {PC} based platform}},
  booktitle = {{Engineering in Medicine and Biology, 2002. 24\textsuperscript{th} Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society {EMBS/BMES} Conference, 2002. Proceedings of the Second Joint}},
  year =      {2002},
  volume =    {2},
  pages =     {1107-- 1108 vol.2},
  publisher = {{IEEE}},
  abstract =  {We describe a portable {PC} based platform for computer aided diagnosis {(CAD)} of functional brain disorders using positron emission tomography {(PET)} that integrates a variety of methods including quantitative and visual techniques as well as extensively automated procedures. Statistical procedures such as the scaled subprofile model {(SSM)} in conjunction with principal component analysis are employed to derive neuronal networks that can uniquely identify characteristic metabolic covariance patterns. By measuring the expression of these networks in diseased subjects on an individual basis we can help automate the diagnosis of conditions such as Parkinson's disease and evaluate treatment efficacy and disease progression.},
  doi =       {10.1109/IEMBS.2002.1106300},
  isbn =      {0-7803-7612-9},
  keywords =  {Brain; computer aided diagnosis; Data visualization; diseased subjects; disease progression; diseases; extensively automated procedures; functional brain disorders; Image processing; individual basis; Magnetic resonance imaging; medical image processing; metabolic covariance patterns; microcomputer applications; neuronal networks; neurophysiology; Parkinson Disease; Parkinson's disease; {PET}; portable {PC} based platform; positron emission tomography; Principal Component Analysis; quantitative techniques; scaled subprofile model; Scanning probe microscopy; Software packages; statistical analysis; statistical procedures; Sugar; treatment efficacy; visual techniques}
}
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