Minimize the Percentage of Noise in Biomedical Images Using Neural Networks
Author
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-17
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
The overall goal of the research is to improve the quality of biomedical image for telemedicine with minimum percentages of noise in the retrieved image and to take less computation time.
The novelty of this technique lies in the implementation of spectral coding for biomedical images using neural networks in order to accomplish the above objectives.
This work is in continuity of an ongoing research project aimed at developing a system for efficient image compression approach for telemedicine in Saudi Arabia.
We compare the efficiency of this technique against existing image compression techniques, namely, JPEG2000, in terms of compression ratio, peak signal to noise ratio (PSNR), and computation time.
To our knowledge, the research is the primary in providing a comparative study with other techniques used in the compression of biomedical images.
This work explores and tests biomedical images such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET).
American Psychological Association (APA)
Saudagar, Abdul Khader Jilani. 2014. Minimize the Percentage of Noise in Biomedical Images Using Neural Networks. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1050933
Modern Language Association (MLA)
Saudagar, Abdul Khader Jilani. Minimize the Percentage of Noise in Biomedical Images Using Neural Networks. The Scientific World Journal No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1050933
American Medical Association (AMA)
Saudagar, Abdul Khader Jilani. Minimize the Percentage of Noise in Biomedical Images Using Neural Networks. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1050933
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050933