Diagnosis of the Abdominal Aorta Aneurysm in Magnetic Resonance Imaging Images
| Main Authors: | W. Kultangwattana, K. Somkantha, P. Phuangsuwan |
|---|---|
| Format: | Article eJournal |
| Bahasa: | eng |
| Terbitan: |
, 2009
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/1076658 |
| ctrlnum |
1076658 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>W. Kultangwattana</creator><creator>K. Somkantha</creator><creator>P. Phuangsuwan</creator><date>2009-05-29</date><description>This paper presents a technique for diagnosis of the abdominal aorta aneurysm in magnetic resonance imaging (MRI) images. First, our technique is designed to segment the aorta image in MRI images. This is a required step to determine the volume of aorta image which is the important step for diagnosis of the abdominal aorta aneurysm. Our proposed technique can detect the volume of aorta in MRI images using a new external energy for snakes model. The new external energy for snakes model is calculated from Law-s texture. The new external energy can increase the capture range of snakes model efficiently more than the old external energy of snakes models. Second, our technique is designed to diagnose the abdominal aorta aneurysm by Bayesian classifier which is classification models based on statistical theory. The feature for data classification of abdominal aorta aneurysm was derived from the contour of aorta images which was a result from segmenting of our snakes model, i.e., area, perimeter and compactness. We also compare the proposed technique with the traditional snakes model. In our experiment results, 30 images are trained, 20 images are tested and compared with expert opinion. The experimental results show that our technique is able to provide more accurate results than 95%.</description><identifier>https://zenodo.org/record/1076658</identifier><identifier>10.5281/zenodo.1076658</identifier><identifier>oai:zenodo.org:1076658</identifier><language>eng</language><relation>doi:10.5281/zenodo.1076657</relation><relation>url:https://zenodo.org/communities/waset</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><subject>Adbominal Aorta Aneurysm</subject><subject>Bayesian Classifier</subject><subject>Snakes Model</subject><subject>Texture Feature.</subject><title>Diagnosis of the Abdominal Aorta Aneurysm in Magnetic Resonance Imaging Images</title><type>Journal:Article</type><type>Journal:Article</type><recordID>1076658</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Journal:eJournal |
| author |
W. Kultangwattana K. Somkantha P. Phuangsuwan |
| title |
Diagnosis of the Abdominal Aorta Aneurysm in Magnetic Resonance Imaging Images |
| publishDate |
2009 |
| topic |
Adbominal Aorta Aneurysm Bayesian Classifier Snakes Model Texture Feature |
| url |
https://zenodo.org/record/1076658 |
| contents |
This paper presents a technique for diagnosis of the abdominal aorta aneurysm in magnetic resonance imaging (MRI) images. First, our technique is designed to segment the aorta image in MRI images. This is a required step to determine the volume of aorta image which is the important step for diagnosis of the abdominal aorta aneurysm. Our proposed technique can detect the volume of aorta in MRI images using a new external energy for snakes model. The new external energy for snakes model is calculated from Law-s texture. The new external energy can increase the capture range of snakes model efficiently more than the old external energy of snakes models. Second, our technique is designed to diagnose the abdominal aorta aneurysm by Bayesian classifier which is classification models based on statistical theory. The feature for data classification of abdominal aorta aneurysm was derived from the contour of aorta images which was a result from segmenting of our snakes model, i.e., area, perimeter and compactness. We also compare the proposed technique with the traditional snakes model. In our experiment results, 30 images are trained, 20 images are tested and compared with expert opinion. The experimental results show that our technique is able to provide more accurate results than 95%. |
| id |
IOS17403.1076658 |
| institution |
Universitas PGRI Palembang |
| institution_id |
189 |
| institution_type |
library:university library |
| library |
Perpustakaan Universitas PGRI Palembang |
| library_id |
587 |
| collection |
Marga Life in South Sumatra in the Past: Puyang Concept Sacrificed and Demythosized |
| repository_id |
17403 |
| city |
KOTA PALEMBANG |
| province |
SUMATERA SELATAN |
| repoId |
IOS17403 |
| first_indexed |
2022-07-26T01:59:01Z |
| last_indexed |
2022-07-26T01:59:01Z |
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dc |
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1739407366084886528 |
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17.538404 |