XMIAR: X-ray medical image annotation and retrieval

• Main Authors: Abdulrazzaq, M. M., Alshaikhli, Imad Fakhri Taha, Mohd Noah, Shahrul Azman, Fadhil, M. A., Ashour, M. U.
• Format: Conference or Workshop Item
• Language: English; English; English
• Published: Springer Verlag 2019
• Subjects: QA75 Electronic computers. Computer science
• Online Access: 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; http://irep.iium.edu.my/76758/13/76758_XMIAR%20X-ray%20medical%20image%20annotation_SCOPUS.pdf; http://irep.iium.edu.my/76758/; https://link.springer.com/book/10.1007/978-3-030-17798-0

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.