Currently, there a growing demand of data produced and stored in clinical domains. Therefore, for effective dealings of massive sets of data, a fusion methodology needs to be analyzed by considering the algorithmic complexities. For effective minimization of the severance of image content, hence minimizing the capacity to store and communicate data in optimal forms, image processing methodology has to be involved. In that case, in this research, two compression methodologies: lossy compression and lossless compression were utilized for the purpose of compressing images, which maintains the quality of images. Also, a number of sophisticated approaches to enhance the quality of the fused images have been applied. The methodologies have been assessed and various fusion findings have been presented. Lastly, performance parameters were obtained and evaluated with respect to sophisticated approaches. Structure Similarity Index Metric (SSIM), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) are the metrics, which were utilized for the sample clinical pictures. Critical analysis of the measurement parameters shows higher efficiency compared to numerous image processing methods. This research draws understanding to these approaches and enables scientists to choose effective methodologies of a particular application.
Keywords
Image Enhancement, Image Compression, Structure Similarity Index Metric (SSIM), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR)
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Calvin Omind Munna
Calvin Omind Munna
College of Health Sciences, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya.
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Cite this article
Calvin Omind Munna, “A Novel Image Processing Methodology for X-ray Image Compression and Enhancement”, Journal of Biomedical and Sustainable Healthcare Applications, vol.2, no.1, pp. 001-008, January 2022. doi: 10.53759/0088/JBSHA202202001.
