Classification of Alzheimer's disease using unsupervised diffusion component analysis
The goal of this study is automated discrimination between early stage Alzheimer$'$s disease (AD) magnetic resonance imaging (MRI) and healthy MRI data. Unsupervised Diffusion Component Analysis, a novel approach based on the diffusion mapping framework, reduces data dimensionality and provides...
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| Format: | Article |
| Language: | English |
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AIMS Press
2016-07-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2016033 |
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| _version_ | 1832590075053998080 |
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| author | Dominique Duncan Thomas Strohmer |
| author_facet | Dominique Duncan Thomas Strohmer |
| author_sort | Dominique Duncan |
| collection | DOAJ |
| description | The goal of this study is automated discrimination between early stage Alzheimer$'$s disease (AD) magnetic resonance imaging (MRI) and healthy MRI data. Unsupervised Diffusion Component Analysis, a novel approach based on the diffusion mapping framework, reduces data dimensionality and provides pattern recognition that can be used to distinguish AD brains from healthy brains. The new algorithm constructs coordinates as an extension of diffusion maps and generates efficient geometric representations of the complex structure of the MRI data. The key difference between our method and others used to classify and detect AD early in its course is our nonlinear and local network approach, which overcomes calibration differences among different scanners and centers collecting MRI data and solves the problem of individual variation in brain size and shape. In addition, our algorithm is completely automatic and unsupervised, which could potentially be a useful and practical tool for doctors to help identify AD patients. |
| format | Article |
| id | doaj-art-baf311b5c4384669ae09dbfe03503bb8 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2016-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-baf311b5c4384669ae09dbfe03503bb82025-01-24T02:37:49ZengAIMS PressMathematical Biosciences and Engineering1551-00182016-07-011361119113010.3934/mbe.2016033Classification of Alzheimer's disease using unsupervised diffusion component analysisDominique Duncan0Thomas Strohmer1Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern CaliforniaDepartment of Mathematics, University of California, DavisThe goal of this study is automated discrimination between early stage Alzheimer$'$s disease (AD) magnetic resonance imaging (MRI) and healthy MRI data. Unsupervised Diffusion Component Analysis, a novel approach based on the diffusion mapping framework, reduces data dimensionality and provides pattern recognition that can be used to distinguish AD brains from healthy brains. The new algorithm constructs coordinates as an extension of diffusion maps and generates efficient geometric representations of the complex structure of the MRI data. The key difference between our method and others used to classify and detect AD early in its course is our nonlinear and local network approach, which overcomes calibration differences among different scanners and centers collecting MRI data and solves the problem of individual variation in brain size and shape. In addition, our algorithm is completely automatic and unsupervised, which could potentially be a useful and practical tool for doctors to help identify AD patients.https://www.aimspress.com/article/doi/10.3934/mbe.2016033manifold learningkernel methods.diffusion mapsmrialzheimer's disease |
| spellingShingle | Dominique Duncan Thomas Strohmer Classification of Alzheimer's disease using unsupervised diffusion component analysis Mathematical Biosciences and Engineering manifold learning kernel methods. diffusion maps mri alzheimer's disease |
| title | Classification of Alzheimer's disease using unsupervised diffusion component analysis |
| title_full | Classification of Alzheimer's disease using unsupervised diffusion component analysis |
| title_fullStr | Classification of Alzheimer's disease using unsupervised diffusion component analysis |
| title_full_unstemmed | Classification of Alzheimer's disease using unsupervised diffusion component analysis |
| title_short | Classification of Alzheimer's disease using unsupervised diffusion component analysis |
| title_sort | classification of alzheimer s disease using unsupervised diffusion component analysis |
| topic | manifold learning kernel methods. diffusion maps mri alzheimer's disease |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2016033 |
| work_keys_str_mv | AT dominiqueduncan classificationofalzheimersdiseaseusingunsuperviseddiffusioncomponentanalysis AT thomasstrohmer classificationofalzheimersdiseaseusingunsuperviseddiffusioncomponentanalysis |