Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images
Dementia is an evolving challenge in society, and no disease-modifying treatment exists. Diagnosis can be demanding and MR imaging may aid as a noninvasive method to increase prediction accuracy. We explored the use of 2D local binary pattern (LBP) extracted from FLAIR and T1 MR images of the brain...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2015/572567 |
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| _version_ | 1832561982515970048 |
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| author | Ketil Oppedal Trygve Eftestøl Kjersti Engan Mona K. Beyer Dag Aarsland |
| author_facet | Ketil Oppedal Trygve Eftestøl Kjersti Engan Mona K. Beyer Dag Aarsland |
| author_sort | Ketil Oppedal |
| collection | DOAJ |
| description | Dementia is an evolving challenge in society, and no disease-modifying treatment exists. Diagnosis can be demanding and MR imaging may aid as a noninvasive method to increase prediction accuracy. We explored the use of 2D local binary pattern (LBP) extracted from FLAIR and T1 MR images of the brain combined with a Random Forest classifier in an attempt to discern patients with Alzheimer's disease (AD), Lewy body dementia (LBD), and normal controls (NC). Analysis was conducted in areas with white matter lesions (WML) and all of white matter (WM). Results from 10-fold nested cross validation are reported as mean accuracy, precision, and recall with standard deviation in brackets. The best result we achieved was in the two-class problem NC versus AD + LBD with total accuracy of 0.98 (0.04). In the three-class problem AD versus LBD versus NC and the two-class problem AD versus LBD, we achieved 0.87 (0.08) and 0.74 (0.16), respectively. The performance using 3DT1 images was notably better than when using FLAIR images. The results from the WM region gave similar results as in the WML region. Our study demonstrates that LBP texture analysis in brain MR images can be successfully used for computer based dementia diagnosis. |
| format | Article |
| id | doaj-art-7b5bc7d8468f48a18d8ad54ae3b0d47e |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-7b5bc7d8468f48a18d8ad54ae3b0d47e2025-02-03T01:23:43ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962015-01-01201510.1155/2015/572567572567Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance ImagesKetil Oppedal0Trygve Eftestøl1Kjersti Engan2Mona K. Beyer3Dag Aarsland4Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, NorwayDepartment of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, NorwayDepartment of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, NorwayDepartment of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, NorwayCentre for Age-Related Medicine, Stavanger University Hospital, Stavanger, NorwayDementia is an evolving challenge in society, and no disease-modifying treatment exists. Diagnosis can be demanding and MR imaging may aid as a noninvasive method to increase prediction accuracy. We explored the use of 2D local binary pattern (LBP) extracted from FLAIR and T1 MR images of the brain combined with a Random Forest classifier in an attempt to discern patients with Alzheimer's disease (AD), Lewy body dementia (LBD), and normal controls (NC). Analysis was conducted in areas with white matter lesions (WML) and all of white matter (WM). Results from 10-fold nested cross validation are reported as mean accuracy, precision, and recall with standard deviation in brackets. The best result we achieved was in the two-class problem NC versus AD + LBD with total accuracy of 0.98 (0.04). In the three-class problem AD versus LBD versus NC and the two-class problem AD versus LBD, we achieved 0.87 (0.08) and 0.74 (0.16), respectively. The performance using 3DT1 images was notably better than when using FLAIR images. The results from the WM region gave similar results as in the WML region. Our study demonstrates that LBP texture analysis in brain MR images can be successfully used for computer based dementia diagnosis.http://dx.doi.org/10.1155/2015/572567 |
| spellingShingle | Ketil Oppedal Trygve Eftestøl Kjersti Engan Mona K. Beyer Dag Aarsland Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images International Journal of Biomedical Imaging |
| title | Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images |
| title_full | Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images |
| title_fullStr | Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images |
| title_full_unstemmed | Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images |
| title_short | Classifying Dementia Using Local Binary Patterns from Different Regions in Magnetic Resonance Images |
| title_sort | classifying dementia using local binary patterns from different regions in magnetic resonance images |
| url | http://dx.doi.org/10.1155/2015/572567 |
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