With the progress in medical diagnostic equipment, imaging systems have faster speeds and higher resolution as development goals. The picture archiving and communication system (PACS) is used to manage and store large numbers of images. The amount of medical imaging data is increasing with the latest high-resolution imaging equipment. Large amounts of data may affect the performance of PACS systems. The most cost-effective way to improve PACS performance is to use image compression technology to reduce the data. Appropriate image compression can save space and speed transmission without diagnostic impact. Therefore, image compression for PACS is the most efficient way to save cost and improve efficiency.
Lossless (reversible) image compression ratio (CR
volume of original/volume of compressed image) have a maximum of 2.5. The lossy (irreversible) compression can achieve a higher CR, but the images will become different from the original. Because of the differences, the image quality needs to be evaluated for diagnostic applications.
The lossy image assessment methods can be divided into subjective and objective. In general, the subjective methods evaluate image quality using the human eye as the basis. They are ROC (receivers operating characteristics) and the MOS (mean opinion score). The objective methods are based on mathematical calculations. The changes between the original and the compressed images are calculated using some math formulas. The MSE (mean square error) and the PSNR (peak signal to noise ratio) are frequently used objective metrics. Both metrics are used to calculate the differences in pixels from original and compressed image to indicate the change in image quality. The higher differences represent higher image quality degradation.
PSNR is most commonly as an index of image quality. The differences between pixels are calculated directly. Recently, Wang et al. found that the pixel to pixel calculation had difficultly reflecting the human visual experience. They suggested that even with the same MSE value for two images, they did not have the same image quality [1
İsmail et al. reported that both MSE and PSNR are sensitive for only detection noise. There is no connection to the human visual response [3
Recently, the metrics combined perceptual quality measurement and human visual system (HVS) features [4
] has been developed. Since a human observer is the end user of image quality measurement, the metrics used for assessing the image quality should take into account the impact of HVS [6
]. The Sarnoff just noticeable differences (JND) vision model is being used successfully to predict digital-video quality [7
]. The JND metrics is a computational model that simulates known physiological mechanisms in the human visual system, including the contrast sensitivity of the eye, luminance, spatial frequency and orientation responses of the visual cortex [5
]. The success of these metrics is in some sense heuristic and developed as an ISO standard (ISO 20462) [7
]. However, Ponomarenko et al. [6
]. suggested that “currently there are no reliable mathematical models for the HVS resulting in the impossibility of defining an optimum metric that perfectly matches the HVS”.
A number of studies have used windows as the basis for the quality indices. These indices evaluate variation in quality by calculating correlation between the pixel grey values in windows, as proposed by Wang et al. [1
] and Chen et al. [5
]. The Universal Quality Index (Q
] and the mean structural similarity (MSSIM) [2
] are obtained through using the changes in variance, average and covariance values between two image windows as indicators. Chen et al. proposed statistical Moran’s I
test to measure the spatial correlation between windows from original and compressed images to assess the image quality [5
]. The human vision on an image should be a block (window) rather than one point only. The Q
and MSSIM were proven to have a high correlation with the human eye [1
]. The Moran peak ratio (MPR) suggested by Chen et al. also showed a high correlation with the smoothing and sharpening of images [5
]. Therefore, window-based computing is a feasible option as the image quality index.
For these reasons, the grey relational coefficient (GRC) [12
] calculation for windows between images may be developed as a new image quality index. The GRC calculates the correlation coefficients between two sequences. It is equivalent to calculating the correlation of two image windows if the pixel values in windows are rearranged as sequences. The metrics are the same as the above window-based metrics.
In this study, the most common images, requiring most storage space were chosen. Three modalities, digital radiology (DR), computerized tomography (CT) and magnetic resonance imaging (MRI) were used as experimental images. These images were first compressed at ten different CRs (10
100) using a medical image compression algorithm, named JJ2000 (available on http://jj2000.epfl.ch
). Following that, the quality of the reconstructed images was evaluated using the GRC and some objective metrics. The GRC results are consistent with some objective metrics, such as MSE, PSNR, Q
and MPR. This method was also compared with other windows-based objective metrics with varied window sizes. It was found that GRC is less susceptible to window size variations. GRC is a stable image quality index relative to Q