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Classifying dementia using local binary patterns from different regions in magnetic resonance images.

Oppedal K, Eftestøl T, Engan K, Beyer MK, Aarsland D - Int J Biomed Imaging (2015)

Bottom Line: The best result we achieved was in the two-class problem NC versus AD + LBD with total accuracy of 0.98 (0.04).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway ; Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Overview of proposed method. See Section 5.1 for details.
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Related In: Results  -  Collection


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fig2: Overview of proposed method. See Section 5.1 for details.

Mentions: A computer based system for classification of AD, LBD, and healthy controls based on texture analysis was applied. Firstly, the two regions of interest, WML and WM, were extracted from the MR images. The WM regions were segmented using common functions in SPM8 and the WML were segmented from the FLAIR images using the thresholding technique proposed by Firbank et al. [48], as briefly described in Section 3.1. See also Block 1 in Figure 2.


Classifying dementia using local binary patterns from different regions in magnetic resonance images.

Oppedal K, Eftestøl T, Engan K, Beyer MK, Aarsland D - Int J Biomed Imaging (2015)

Overview of proposed method. See Section 5.1 for details.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4385607&req=5

fig2: Overview of proposed method. See Section 5.1 for details.
Mentions: A computer based system for classification of AD, LBD, and healthy controls based on texture analysis was applied. Firstly, the two regions of interest, WML and WM, were extracted from the MR images. The WM regions were segmented using common functions in SPM8 and the WML were segmented from the FLAIR images using the thresholding technique proposed by Firbank et al. [48], as briefly described in Section 3.1. See also Block 1 in Figure 2.

Bottom Line: The best result we achieved was in the two-class problem NC versus AD + LBD with total accuracy of 0.98 (0.04).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering and Computer Science, University of Stavanger, 4036 Stavanger, Norway ; Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus