XMIAR: X-ray medical image annotation and retrieval
The huge development of the digitized medical image has been steered to the enlargement and research of the Content Based Image Retrieval (CBIR) systems. Those systems retrieve and extract the images by their own low level features, like texture, shape and color. But those visual features did...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Verlag
2019
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| Online Access: | http://irep.iium.edu.my/76758/ http://irep.iium.edu.my/76758/ http://irep.iium.edu.my/76758/2/2020_Bookmatter_AdvancesInComputerVision.pdf http://irep.iium.edu.my/76758/1/10.1007%40978-3-030-17798-051.pdf |
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iium-767582020-01-09T10:16:47Z http://irep.iium.edu.my/76758/ XMIAR: X-ray medical image annotation and retrieval Abdulrazzaq, M. M. Alshaikhli, Imad Fakhri Taha Mohd Noah, Shahrul Azman Fadhil, M. A. Ashour, M. U. QA75 Electronic computers. Computer science The huge development of the digitized medical image has been steered to the enlargement and research of the Content Based Image Retrieval (CBIR) systems. Those systems retrieve and extract the images by their own low level features, like texture, shape and color. But those visual features did not aloe the users to request images by the semantic meanings. The image annotation or classification systems can be considered as the solution for the limitations of the CBIR, and to reduce the semantic gap, this has been aimed annotating or to make the classification of the image with few controlled keywords. In this paper, we suggest a new hierarchal classification for the X-ray medical image using the machine learning techniques, which are called the Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN). Hierarchy classification design was proposed based on the main body region. Evaluation was conducted based on ImageCLEF2005 database. The obtained results in this research were improved compared to the previous related studies. Springer Verlag 2019 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/76758/2/2020_Bookmatter_AdvancesInComputerVision.pdf application/pdf en http://irep.iium.edu.my/76758/1/10.1007%40978-3-030-17798-051.pdf Abdulrazzaq, M. M. and Alshaikhli, Imad Fakhri Taha and Mohd Noah, Shahrul Azman and Fadhil, M. A. and Ashour, M. U. (2019) XMIAR: X-ray medical image annotation and retrieval. In: Computer Vision Conference, CVC 2019, 25-26 April 2019, Las Vegas, USA. https://link.springer.com/book/10.1007/978-3-030-17798-0 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English English |
| topic |
QA75 Electronic computers. Computer science |
| spellingShingle |
QA75 Electronic computers. Computer science Abdulrazzaq, M. M. Alshaikhli, Imad Fakhri Taha Mohd Noah, Shahrul Azman Fadhil, M. A. Ashour, M. U. XMIAR: X-ray medical image annotation and retrieval |
| description |
The huge development of the digitized medical image has been steered
to the enlargement and research of the Content Based Image Retrieval (CBIR)
systems. Those systems retrieve and extract the images by their own low level
features, like texture, shape and color. But those visual features did not aloe the
users to request images by the semantic meanings. The image annotation or
classification systems can be considered as the solution for the limitations of the
CBIR, and to reduce the semantic gap, this has been aimed annotating or to make
the classification of the image with few controlled keywords. In this paper, we
suggest a new hierarchal classification for the X-ray medical image using the
machine learning techniques, which are called the Support Vector Machine
(SVM) and k-Nearest Neighbour (k-NN). Hierarchy classification design was
proposed based on the main body region. Evaluation was conducted based on
ImageCLEF2005 database. The obtained results in this research were improved
compared to the previous related studies. |
| format |
Conference or Workshop Item |
| author |
Abdulrazzaq, M. M. Alshaikhli, Imad Fakhri Taha Mohd Noah, Shahrul Azman Fadhil, M. A. Ashour, M. U. |
| author_facet |
Abdulrazzaq, M. M. Alshaikhli, Imad Fakhri Taha Mohd Noah, Shahrul Azman Fadhil, M. A. Ashour, M. U. |
| author_sort |
Abdulrazzaq, M. M. |
| title |
XMIAR: X-ray medical image annotation and retrieval |
| title_short |
XMIAR: X-ray medical image annotation and retrieval |
| title_full |
XMIAR: X-ray medical image annotation and retrieval |
| title_fullStr |
XMIAR: X-ray medical image annotation and retrieval |
| title_full_unstemmed |
XMIAR: X-ray medical image annotation and retrieval |
| title_sort |
xmiar: x-ray medical image annotation and retrieval |
| publisher |
Springer Verlag |
| publishDate |
2019 |
| url |
http://irep.iium.edu.my/76758/ http://irep.iium.edu.my/76758/ http://irep.iium.edu.my/76758/2/2020_Bookmatter_AdvancesInComputerVision.pdf http://irep.iium.edu.my/76758/1/10.1007%40978-3-030-17798-051.pdf |
| first_indexed |
2023-09-18T21:48:25Z |
| last_indexed |
2023-09-18T21:48:25Z |
| _version_ |
1777413605380063232 |