Adaptive predictive multiplicative autoregressive model for medical image compression

Edge-preserving adaptive autoregressive model for Poisson noise reduction

Autoregressive models in image processing are linear prediction models that split an image into a predicted (i.e. filtered) image and a prediction error image, which extracts data on the image edges. Edge separation is a crucial feature of an autoregressive model. Data on the edges can be processed in different ways and then added to the filtered image. Another basic feature of our method is spatially varying modelling. In this short article, we propose an improved autoregressive model that preserves image sharpness around the edges of the image and focus on the reduction of Poisson noise, which degrades nuclear medicine images and presents a special challenge in medical imaging.

Mutual Information Correlation with Human Vision in Medical Image Compression

BACKGROUND

The lossy compression algorithm produces different results in various con-trasts areas. Low contrast area image quality declines greater than that of high contrast regions using equal compression ratio. These results were obtained in a subjective study. The objective image quali-ty metrics are more effective if the calculation method is more closely related to the human vision re-sults.

METHODS

This study first measured the PSNR and MI for discrimination between different contrast areas responding to lossy image compression in a SMPTE electronic pattern. The MI was consistent with human vision results in SMPTE electronic phantom but PSNR was not. The measurement was also applied to compressed medical images in different contrast cropping regions.

RESULTS

The MI was found to be close to human vision in CT and MR but not CRX. Both weighted PSNR and weighted MI were created to respond to the gray value and the contrast areas affected the quality estimation.

CONCLUSION

The W-PSNR and W-MI showed that they can discriminate between different contrast areas using image compression ratios and the series of lines are equal to the contrast values and better than the tranditional approach. The W-MI measures were found to perform better than W-PSNR and can be used as an image quality index.

Quality of compressed medical images

Previous studies have shown that Joint Photographic Experts Group (JPEG) 2000 compression is better than JPEG at higher compression ratio levels. However, some findings revealed that this is not valid at lower levels. In this study, the qualities of compressed medical images in these ratio areas ( approximately 20), including computed radiography, computed tomography head and body, mammographic, and magnetic resonance T1 and T2 images, were estimated using both a pixel-based (peak signal to noise ratio) and two 8 x 8 window-based [Q index and Moran peak ratio (MPR)] metrics. To diminish the effects of blocking artifacts from JPEG, jump windows were used in both window-based metrics. Comparing the image quality indices between jump and sliding windows, the results showed that blocking artifacts were produced from JPEG compression, even at low compression ratios. However, even after the blocking artifacts were omitted in JPEG compressed images, JPEG2000 outperformed JPEG at low compression levels. We found in this study that the image contrast and the average gray level play important roles in image compression and quality evaluation. There were drawbacks in all metrics that we used. In the future, the image gray level and contrast effect should be considered in developing new objective metrics.

Quality degradation in lossy wavelet image compression

The objective of this study was to develop a method for measuring quality degradation in lossy wavelet image compression. Quality degradation is due to denoising and edge blurring effects that cause smoothness in the compressed image. The peak Moran z histogram ratio between the reconstructed and original images is used as an index for degradation after image compression. The Moran test is applied to images randomly selected from each medical modality, computerized tomography, magnetic resonance imaging, and computed radiography and compressed using the wavelet compression at various levels. The relationship between the quality degradation and compression ratio for each image modality agrees with previous reports that showed a preference for mildly compressed images. Preliminary results show that the peak Moran z histogram ratio can be used to quantify the quality degradation in lossy image compression. The potential for this method is applications for determining the optimal compression ratio (the maximized compression without seriously degrading image quality) of an image for teleradiology.