Renal Dynamic Image Compression Using Singular Value Decomposition

Aims and Objective:The objective of this study was to evaluate the compression of renal dynamic (RD) study images using singular value decomposition (SVD) technique.Materials and Methods:4600 images of fifty RD study were compressed by using SVD technique. Two Nuclear Medicine (NM) Physicians compared compressed images with their corresponding input images and labeled these as acceptable or unacceptable. The SVD computation time and compression ratio were calculated for each image. The quality of compressed image was also assessed objectively using the following image quality metrics: Error, structural similarity (SSIM), Brightness, global contrast factor, contrast per pixel (CPP), and blur. The error in split function (i.e., the error between split function calculated from compressed image and split function calculated from original image) was computed for every RD study. Wilcoxon signed-rank test with continuity correction was applied to find a statistically significant difference in ROI counts on compressed and original image at.Results:As per NM physicians compressed image frames look identical to the original image frames. Objectively the compressed images were brighter, less noisy, and also have better CPP. Based on the visual assessment, time activity curve generated from original and compressed image frames was identical. There was insignificant difference of ROI counts between the input and compressed image frames of 99m-Tc LLEC RD Study. There was no significant difference between the split renal function estimated from original and its compressed RD study. The average SSIM value, average compression ratio, and SVD computation time were found to be 0.7521, 1.475, and 0.1200.Conclusions:Visually, compressed image was identical to the original image. The percentage compression achieved was found to be up to 58% (compression factor achieved = 1.57). The SVD computation time was approximately 0.12 s for 64 × 64 matrix size image frame.