White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach
White Matter (WM) lesions are diffuse white matter abnormalities, that appear as hyperintense (bright) regions in cranial Magnetic Resonance Imaging (MRI). WM lesions are often observed in older population and are important indicators of stroke, multiple sclerosis, dementia and other brain-related d...
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| Format: | Thesis |
| Language: | English |
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2011
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| Online Access: | http://eprints.usm.my/42262/1/ONG_KOK_HAUR.pdf |
| _version_ | 1825834587000930304 |
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| author | Ong , Kok Haur |
| author_facet | Ong , Kok Haur |
| author_sort | Ong , Kok Haur |
| collection | USM |
| description | White Matter (WM) lesions are diffuse white matter abnormalities, that appear as hyperintense (bright) regions in cranial Magnetic Resonance Imaging (MRI). WM lesions are often observed in older population and are important indicators of stroke, multiple sclerosis, dementia and other brain-related disorders. Manual detection of WM lesions is laborious and the currently adopted visual scoring approaches for lesion grading is very subjective. In this thesis, a new approach for automated WM Lesions Segmentation is presented. In the proposed approach, the presence of WM lesions is detected as outliers in the intensity distribution of the
Fluid Attenuated Inversion Recovery (FLAIR) MR images using an Adaptive Outlier Detection technique. |
| first_indexed | 2024-03-06T15:24:57Z |
| format | Thesis |
| id | usm.eprints-42262 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:24:57Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | usm.eprints-422622019-04-12T05:26:33Z http://eprints.usm.my/42262/ White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach Ong , Kok Haur QA75.5-76.95 Electronic computers. Computer science White Matter (WM) lesions are diffuse white matter abnormalities, that appear as hyperintense (bright) regions in cranial Magnetic Resonance Imaging (MRI). WM lesions are often observed in older population and are important indicators of stroke, multiple sclerosis, dementia and other brain-related disorders. Manual detection of WM lesions is laborious and the currently adopted visual scoring approaches for lesion grading is very subjective. In this thesis, a new approach for automated WM Lesions Segmentation is presented. In the proposed approach, the presence of WM lesions is detected as outliers in the intensity distribution of the Fluid Attenuated Inversion Recovery (FLAIR) MR images using an Adaptive Outlier Detection technique. 2011-06 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/42262/1/ONG_KOK_HAUR.pdf Ong , Kok Haur (2011) White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach. Masters thesis, Universiti Sains Malaysia. |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Ong , Kok Haur White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach |
| title | White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach
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| title_full | White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach
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| title_fullStr | White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach
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| title_full_unstemmed | White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach
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| title_short | White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach
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| title_sort | white matter lesion segmentation in brain mri using adaptive trimmed mean approach |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://eprints.usm.my/42262/1/ONG_KOK_HAUR.pdf |
| work_keys_str_mv | AT ongkokhaur whitematterlesionsegmentationinbrainmriusingadaptivetrimmedmeanapproach |