A statistical region selection and randomized volumetric features selection framework for early detection of Alzheimer's disease
Identification of dominant imaging biomarkers is important for early detection of Alzheimer's disease (AD) and to improve diagnostic accuracy. This work proposes a novel automatic computer aided diagnosis (CAD) system working on region selection framework. Voxel based morphometry and tissue seg...
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| Published in | International journal of imaging systems and technology Vol. 28; no. 4; pp. 302 - 314 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
01.12.2018
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0899-9457 1098-1098 |
| DOI | 10.1002/ima.22290 |
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| Abstract | Identification of dominant imaging biomarkers is important for early detection of Alzheimer's disease (AD) and to improve diagnostic accuracy. This work proposes a novel automatic computer aided diagnosis (CAD) system working on region selection framework. Voxel based morphometry and tissue segmentation is performed to get gray matter (GM) images. These pre‐processed images are anatomized to get 116 regions of brain using a standard automated anatomical labeling atlas. The proposed region selection algorithm identifies the most relevant brain regions out of 116 regions to discriminate AD and healthy control (HC) subjects. Volumetric features (standard deviation, skewness, kurtosis, energy, and shannon entropy) are extracted and random feature selection is performed to get the most discriminating regions to classify AD from HC. Supervised classification algorithms are used to explore and validate the proposed methodology. Experimental results indicate that the performance of the proposed system competes well with the state‐of‐the‐art techniques. |
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| AbstractList | Identification of dominant imaging biomarkers is important for early detection of Alzheimer's disease (AD) and to improve diagnostic accuracy. This work proposes a novel automatic computer aided diagnosis (CAD) system working on region selection framework. Voxel based morphometry and tissue segmentation is performed to get gray matter (GM) images. These pre‐processed images are anatomized to get 116 regions of brain using a standard automated anatomical labeling atlas. The proposed region selection algorithm identifies the most relevant brain regions out of 116 regions to discriminate AD and healthy control (HC) subjects. Volumetric features (standard deviation, skewness, kurtosis, energy, and shannon entropy) are extracted and random feature selection is performed to get the most discriminating regions to classify AD from HC. Supervised classification algorithms are used to explore and validate the proposed methodology. Experimental results indicate that the performance of the proposed system competes well with the state‐of‐the‐art techniques. |
| Author | Khanna, Pritee Mishra, Shiwangi Beheshti, Iman |
| Author_xml | – sequence: 1 givenname: Shiwangi orcidid: 0000-0001-7945-0992 surname: Mishra fullname: Mishra, Shiwangi organization: Department of Computer Science and Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing – sequence: 2 givenname: Iman surname: Beheshti fullname: Beheshti, Iman organization: National Center of Neurology and Psychiatry – sequence: 3 givenname: Pritee surname: Khanna fullname: Khanna, Pritee email: pkhanna@iiitdmj.ac.in organization: Department of Computer Science and Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing |
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| SubjectTerms | Algorithms Alzheimer's disease Automation Biomarkers Brain Diagnostic systems Entropy (Information theory) Feature extraction feature selection Image segmentation imaging biomarkers Kurtosis magnetic resonance imaging Medical imaging region segmentation region selection voxel‐based morphometry |
| Title | A statistical region selection and randomized volumetric features selection framework for early detection of Alzheimer's disease |
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