A performance review for hybrid region of interest-based medical image compression.
In this modern era, medical image sharing has become a routine activity within hospital information systems. Digital medical images have become valuable resources that aid health care systems' decision-making and treatment procedures. A medical image consumes a significant amount of memory, and...
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Institute of Electrical and Electronics Engineers Inc.
2023
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my.utm.1049002024-03-25T09:28:18Z http://eprints.utm.my/104900/ A performance review for hybrid region of interest-based medical image compression. Aziz, Suhaila Mohd. Sam, Suriani Hassan, Noor Hafizah Abas, Hafiza Abdul Rasid, Siti Zaleha Yusof, Muhammad Fathi Mohamed, Norliza Q Science (General) QC Physics In this modern era, medical image sharing has become a routine activity within hospital information systems. Digital medical images have become valuable resources that aid health care systems' decision-making and treatment procedures. A medical image consumes a significant amount of memory, and the size of medical images continues to grow as medical imaging technology progresses. In addition, an image is shared for analysis to support knowledge sharing and disease diagnosis. Therefore, health care systems must ensure that medical images are appropriately distributed without information loss in a timely and secure manner. Image compression is the primary process performed on each medical image before it is shared to ensure that the purpose of sharing an image is accomplished. The hybrid region of interest-based medical compression algorithms reduces image size. Furthermore, these algorithms shorten the image compression process time by manipulating the advantages of lossy and lossless compression techniques. A comprehensive review of previous studies that utilized this approach was conducted. Sample studies were selected from published articles in an open database subscribed to by Universiti Teknologi Malaysia for ten years (2012 to 2023). This work aims to critically review and comprehensively analyze previous types of algorithms by focusing on their main performance results: compression ratio, mean square error and peak signal-to-noise ratio. This article will identify which type of algorithm can give optimal value to the primary performance metric for compressing medical images. Institute of Electrical and Electronics Engineers Inc. 2023 Article PeerReviewed application/pdf en http://eprints.utm.my/104900/1/SurianiMohdSam2023_APerformanceReviewforHybridRegionofInterest.pdf Aziz, Suhaila and Mohd. Sam, Suriani and Hassan, Noor Hafizah and Abas, Hafiza and Abdul Rasid, Siti Zaleha and Yusof, Muhammad Fathi and Mohamed, Norliza (2023) A performance review for hybrid region of interest-based medical image compression. IEEE Access, 11 . pp. 98025-98038. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2023.3312265 DOI: 10.1109/ACCESS.2023.3312265 |
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Q Science (General) QC Physics Aziz, Suhaila Mohd. Sam, Suriani Hassan, Noor Hafizah Abas, Hafiza Abdul Rasid, Siti Zaleha Yusof, Muhammad Fathi Mohamed, Norliza A performance review for hybrid region of interest-based medical image compression. |
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In this modern era, medical image sharing has become a routine activity within hospital information systems. Digital medical images have become valuable resources that aid health care systems' decision-making and treatment procedures. A medical image consumes a significant amount of memory, and the size of medical images continues to grow as medical imaging technology progresses. In addition, an image is shared for analysis to support knowledge sharing and disease diagnosis. Therefore, health care systems must ensure that medical images are appropriately distributed without information loss in a timely and secure manner. Image compression is the primary process performed on each medical image before it is shared to ensure that the purpose of sharing an image is accomplished. The hybrid region of interest-based medical compression algorithms reduces image size. Furthermore, these algorithms shorten the image compression process time by manipulating the advantages of lossy and lossless compression techniques. A comprehensive review of previous studies that utilized this approach was conducted. Sample studies were selected from published articles in an open database subscribed to by Universiti Teknologi Malaysia for ten years (2012 to 2023). This work aims to critically review and comprehensively analyze previous types of algorithms by focusing on their main performance results: compression ratio, mean square error and peak signal-to-noise ratio. This article will identify which type of algorithm can give optimal value to the primary performance metric for compressing medical images. |
format |
Article |
author |
Aziz, Suhaila Mohd. Sam, Suriani Hassan, Noor Hafizah Abas, Hafiza Abdul Rasid, Siti Zaleha Yusof, Muhammad Fathi Mohamed, Norliza |
author_facet |
Aziz, Suhaila Mohd. Sam, Suriani Hassan, Noor Hafizah Abas, Hafiza Abdul Rasid, Siti Zaleha Yusof, Muhammad Fathi Mohamed, Norliza |
author_sort |
Aziz, Suhaila |
title |
A performance review for hybrid region of interest-based medical image compression. |
title_short |
A performance review for hybrid region of interest-based medical image compression. |
title_full |
A performance review for hybrid region of interest-based medical image compression. |
title_fullStr |
A performance review for hybrid region of interest-based medical image compression. |
title_full_unstemmed |
A performance review for hybrid region of interest-based medical image compression. |
title_sort |
performance review for hybrid region of interest-based medical image compression. |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2023 |
url |
http://eprints.utm.my/104900/1/SurianiMohdSam2023_APerformanceReviewforHybridRegionofInterest.pdf http://eprints.utm.my/104900/ http://dx.doi.org/10.1109/ACCESS.2023.3312265 |
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