IntOPMICM: Intelligent Medical Image Size Reduction Model. (25th February 2022)

• Record Type: Journal Article
• Authors: Pareek, Piyush Kumar; Sridhar, Chethana; Kalidoss, R.; Aslam, Muhammad; Maheshwari, Manish; Shukla, Prashant Kumar; Nuagah, Stephen Jeswinde
• Other Names: Gloria Antonio Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2022(2022)
• Publication Date: 2022-02-25
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2022/5171016
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : Due to the increasing number of medical imaging images being utilized for the diagnosis and treatment of diseases, lossy or improper image compression has become more prevalent in recent years. The compression ratio and image quality, which are commonly quantified by PSNR values, are used to evaluate the performance of the lossy compression algorithm. This article introduces the IntOPMICM technique, a new image compression scheme that combines GenPSO and VQ. A combination of fragments and genetic algorithms was used to create the codebook. PSNR, MSE, SSIM, NMSE, SNR, and CR indicators were used to test the suggested technique using real-time medical imaging. The suggested IntOPMICM approach produces higher PSNR SSIM values for a given compression ratio than existing methods, according to experimental data. Furthermore, for a given compression ratio, the suggested IntOPMICM approach produces lower MSE, RMSE, and SNR values than existing methods.