A performance review for hybrid region of interest-based medical image compression.

• Main Authors: Aziz, Suhaila, Mohd. Sam, Suriani, Hassan, Noor Hafizah, Abas, Hafiza, Abdul Rasid, Siti Zaleha, Yusof, Muhammad Fathi, Mohamed, Norliza
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Subjects: Q Science (General); QC Physics
• Online Access: http://eprints.utm.my/104900/1/SurianiMohdSam2023_APerformanceReviewforHybridRegionofInterest.pdf; http://eprints.utm.my/104900/; http://dx.doi.org/10.1109/ACCESS.2023.3312265
• Institution: Universiti Teknologi Malaysia

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.