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- Publisher Website: 10.1109/ICACSIS.2017.8355053
- Scopus: eid_2-s2.0-85051107108
- WOS: WOS:000434955800050
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Conference Paper: Voxel-based irregularity age map (IAM) for brain's white matter hyperintensities in MRI
Title | Voxel-based irregularity age map (IAM) for brain's white matter hyperintensities in MRI |
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Authors | |
Issue Date | 2017 |
Citation | 2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017, 2017, p. 321-326 How to Cite? |
Abstract | © 2017 IEEE. In this paper, we propose a novel way to produce voxel-based irregularity age map (IAM) for brain magnetic resonance image (MRI) to identify white matter hyperintensities (WMH) on scans with mild vascular pathology. Age map is a term used in computer graphic field that reveals age/progression of defected areas in images' texture. In this work, age map is used to reveal the age of irregularity of brain tissue (i.e., hyperintensities). Age map of WMH is useful because it shows not only the probability of voxels to be WMH but also the scale in the progression of voxels to become WMH. Our approach is fully automatic and unsupervised with little to none human interaction. We evaluated our approach using brain MRI data obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) database and visually compared the results with those obtained from the public toolbox Lesion Segmentation Toolbox (LST). We also evaluated our proposed approach on images from 10 different subjects using Dice similarity coefficient (DSC). |
Persistent Identifier | http://hdl.handle.net/10722/288746 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Rachmadi, Muhammad Febrian | - |
dc.contributor.author | Valdes-Hernandez, Maria Del C. | - |
dc.contributor.author | Komura, Taku | - |
dc.date.accessioned | 2020-10-12T08:05:46Z | - |
dc.date.available | 2020-10-12T08:05:46Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | 2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017, 2017, p. 321-326 | - |
dc.identifier.uri | http://hdl.handle.net/10722/288746 | - |
dc.description.abstract | © 2017 IEEE. In this paper, we propose a novel way to produce voxel-based irregularity age map (IAM) for brain magnetic resonance image (MRI) to identify white matter hyperintensities (WMH) on scans with mild vascular pathology. Age map is a term used in computer graphic field that reveals age/progression of defected areas in images' texture. In this work, age map is used to reveal the age of irregularity of brain tissue (i.e., hyperintensities). Age map of WMH is useful because it shows not only the probability of voxels to be WMH but also the scale in the progression of voxels to become WMH. Our approach is fully automatic and unsupervised with little to none human interaction. We evaluated our approach using brain MRI data obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) database and visually compared the results with those obtained from the public toolbox Lesion Segmentation Toolbox (LST). We also evaluated our proposed approach on images from 10 different subjects using Dice similarity coefficient (DSC). | - |
dc.language | eng | - |
dc.relation.ispartof | 2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017 | - |
dc.title | Voxel-based irregularity age map (IAM) for brain's white matter hyperintensities in MRI | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ICACSIS.2017.8355053 | - |
dc.identifier.scopus | eid_2-s2.0-85051107108 | - |
dc.identifier.spage | 321 | - |
dc.identifier.epage | 326 | - |
dc.identifier.isi | WOS:000434955800050 | - |