XMIAR

X-ray Medical Image Annotation and Retrieval

M. M. Abdulrazzaq, I. F.T. Yaseen, Shahrul Azman Mohd Noah, M. A. Fadhil, M. U. Ashour

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationAdvances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC
EditorsKohei Arai, Supriya Kapoor
PublisherSpringer Verlag
Pages638-651
Number of pages14
ISBN (Print)9783030177973
DOIs
Publication statusPublished - 1 Jan 2020
EventComputer Vision Conference, CVC 2019 - Las Vegas, United States
Duration: 25 Apr 201926 Apr 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume944
ISSN (Print)2194-5357

Conference

ConferenceComputer Vision Conference, CVC 2019
CountryUnited States
CityLas Vegas
Period25/4/1926/4/19

Fingerprint

X rays
Image retrieval
Semantics
Support vector machines
Learning systems
Textures
Color

Keywords

  • Machine learning
  • Medical image analysis
  • Support vector machines

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Abdulrazzaq, M. M., Yaseen, I. F. T., Mohd Noah, S. A., Fadhil, M. A., & Ashour, M. U. (2020). XMIAR: X-ray Medical Image Annotation and Retrieval. In K. Arai, & S. Kapoor (Eds.), Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC (pp. 638-651). (Advances in Intelligent Systems and Computing; Vol. 944). Springer Verlag. https://doi.org/10.1007/978-3-030-17798-0_51

XMIAR : X-ray Medical Image Annotation and Retrieval. / Abdulrazzaq, M. M.; Yaseen, I. F.T.; Mohd Noah, Shahrul Azman; Fadhil, M. A.; Ashour, M. U.

Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC. ed. / Kohei Arai; Supriya Kapoor. Springer Verlag, 2020. p. 638-651 (Advances in Intelligent Systems and Computing; Vol. 944).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abdulrazzaq, MM, Yaseen, IFT, Mohd Noah, SA, Fadhil, MA & Ashour, MU 2020, XMIAR: X-ray Medical Image Annotation and Retrieval. in K Arai & S Kapoor (eds), Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC. Advances in Intelligent Systems and Computing, vol. 944, Springer Verlag, pp. 638-651, Computer Vision Conference, CVC 2019, Las Vegas, United States, 25/4/19. https://doi.org/10.1007/978-3-030-17798-0_51
Abdulrazzaq MM, Yaseen IFT, Mohd Noah SA, Fadhil MA, Ashour MU. XMIAR: X-ray Medical Image Annotation and Retrieval. In Arai K, Kapoor S, editors, Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC. Springer Verlag. 2020. p. 638-651. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-17798-0_51
Abdulrazzaq, M. M. ; Yaseen, I. F.T. ; Mohd Noah, Shahrul Azman ; Fadhil, M. A. ; Ashour, M. U. / XMIAR : X-ray Medical Image Annotation and Retrieval. Advances in Computer Vision - Proceedings of the 2019 Computer Vision Conference CVC. editor / Kohei Arai ; Supriya Kapoor. Springer Verlag, 2020. pp. 638-651 (Advances in Intelligent Systems and Computing).
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