Improving Diagnostic Quality of MR Images Through Controlled Lossy Compression Using SPIHT

Abstract

This paper attempts to improve the diagnostic quality of magnetic resonance (MR) images through application of lossy compression as a noise-reducing filter. The amount of imaging noise present in MR images is compared with the amount of noise introduced by the compression, with particular attention given to the situation where the compression noise is a fraction of the imaging noise. A popular wavelet-based algorithm with good performance, Set Partitioning in Hierarchical Trees (SPIHT), was employed for the lossy compression. Tests were conducted with a number of MR patient images and corresponding phantom images. Different plausible ratios between imaging noise and compression noise (ICR) were considered, and the achievable compression gain through the controlled lossy compression was evaluated. Preliminary results show that at certain ICR's, it becomes virtually impossible to distinguish between the original and compressed-decompressed image. Radiologists presented with a blind test, in certain cases, showed preference to the compressed image rather than the original uncompressed ones, indicating that under controlled circumstances, lossy image compression can be used to improve the diagnostic quality of the MR images.

Quantitative image quality evaluation of MR images using perceptual difference models

“…45 These metrics are based upon the power spectrum estimation and artifact measurements. Finally, in MRI there are particular objective image quality metrics, including signal-to-noise ratio, 46 peak signalto-noise ratio, 47 CNR, 48 mean-squared error ͑MSE͒, 24 rootmean-squared error, 49 and Shannon's information content. 7 Another similar objective measurement called artifact power was used in some parallel imaging applications.…”

“…Numerical observer evaluation is done with Case-PDM ͑v2, as established by Huo in his Ph.D. thesis, 54 this version has been used in recent publications, 17,25,55 and it is only slightly different than the original, described in 2002͒, Sarnoff's IDM, 40 DCTune, 41 SSIM, 42 IQM, 44 and NR. 45 MSE is also included as an objective metric because MSE has been used in many MR applications [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58] although researchers have reported limitations and poor performance of MSE as the image quality measurement. 24,25,27,59 And we apply seven image quality evaluation methods to fast MR images; these include Case-PDM by comparing model/ metric results to human evaluation of image quality.…”

Quality evaluation of no‐reference MR images using multidirectional filters and image statistics

“…Full‐reference IQA compares a test image with its ground‐truth or reference image. Numerical metrics such as the mean‐square error (MSE) , root‐mean‐square error (RMSE) , and peak signal‐to‐noise ratio (PSNR) , which are based on the difference between a reference and test image, are widely used for full‐reference IQA. Recently, several advanced metrics, such as the structural‐similarity (SSIM) index, feature‐similarity index (FSIM) , and multiscale SSIM , have been developed to account for the human visual system in evaluation and thus achieve high correlations with subjective scoring.…”

Lossy Compression of Noisy Images Based on Visual Quality: A Comprehensive Study

“…Lossless coding usually does not produce sufficient compression ratios for many practical applications. Although for certain applications, such as medical imaging and hyperspectral remote sensing, many experts consider lossy compression impossible or not recommended [3,4], nevertheless they are still used also for these applications (see, for example, [3,[5][6][7] to mention a few). In the rest of this paper by image compression we will mean a lossy compression if not otherwise specified.…”

“…Thus, when applied to noisy images, compression provides two fold benefits: it decreases the image size considerably and reduces noise. Under certain conditions (for properly selected parameters of compression), this can lead to additional positive outcomes as better classification of noisy remote sensing data [28] and improved diagnostic quality of medical images [3]. The authors of the paper [22] state and demonstrate by examples that compressed image visual quality can improve as well.…”