XMIAR: X-ray medical image annotation and retrieval

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.

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Abstract

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.

Item Type:	Conference or Workshop Item (Other)
Additional Information:	6534/76758
Uncontrolled Keywords:	Machine Learning, Support Vector Machines, Medical Image Analysis.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Professor Imad Taha
Date Deposited:	20 Dec 2019 16:11
Last Modified:	07 Jul 2020 10:35
URI:	http://irep.iium.edu.my/id/eprint/76758

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