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1.  A Novel Medical Image Quality Index 
Journal of Digital Imaging  2010;24(5):874-882.
A novel medical image quality index using grey relational coefficient calculation is proposed in this study. Three medical modalities, DR, CT and MRI, using 30 or 60 images with a total of 120 images used for experimentation. These images were first compressed at ten different compression ratios (10 ∼ 100) using a medical image compression algorithm named JJ2000. Following that, the quality of the reconstructed images was evaluated using the grey relational coefficient calculation. The results were shown consistent with popular objective quality metrics. The impact of different image aspects on four grey relational coefficient methods were further tested. The results showed that these grey relational coefficients have different slopes but very high consistency for various image areas. Nagai’s grey relational coefficient was chosen in this study because of higher calculation speed and sensitivity. A comparison was also made between this method and other windows-based objective metrics for various window sizes. Studies found that the grey relational coefficient results are less sensitive to window size changes. The performance of this index is better than some windows-based objective metrics and can be used as an image quality index.
doi:10.1007/s10278-010-9353-y
PMCID: PMC3180531  PMID: 21104000
Image compression; Image quality analysis; JPEG2000
2.  A Novel Image Smoothing Filter Using Membership Function 
Journal of Digital Imaging  2007;20(4):381-392.
This paper presents a new class of image noise smoothing algorithms utilizing the membership information of the neighboring pixels. The basic idea of this method is to compute the smoothed output using neighboring pixels from the same cluster to avoid image blurring. A fuzzy c-means algorithm is first applied to the image to separate the image pixels into a certain number of clusters. A membership function is defined as the probability that a pixel belongs to a cluster. The proposed method uses this membership function as a weight to calculate the weighted sum of the pixel values from its neighboring pixels. The results of the application of this algorithm to various images show that it can smooth images with edge enhancement. The smoothness of the resultant images can be controlled by the cluster number and window size.
doi:10.1007/s10278-006-1043-4
PMCID: PMC3043923  PMID: 17252169
Membership function; fuzzy c-means; noise smoothing
3.  Quality of Compressed Medical Images 
Journal of Digital Imaging  2007;20(2):149-159.
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 (∼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 × 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.
doi:10.1007/s10278-007-9013-z
PMCID: PMC3043905  PMID: 17318703
Image quality; JPEG; JPEG2000; image compression
4.  A Blurring Index for Medical Images 
Journal of Digital Imaging  2005;19(2):118-125.
This study was undertaken to investigate a useful image blurring index. This work is based on our previously developed method, the Moran peak ratio. Medical images are often deteriorated by noise or blurring. Image processing techniques are used to eliminate these two factors. The denoising process may improve image visibility with a trade-off of edge blurring and may introduce undesirable effects in an image. These effects also exist in images reconstructed using the lossy image compression technique. Blurring and degradation in image quality increases with an increase in the lossy image compression ratio. Objective image quality metrics [e.g., normalized mean square error (NMSE)] currently do not provide spatial information about image blurring. In this article, the Moran peak ratio is proposed for quantitative measurement of blurring in medical images. We show that the quantity of image blurring is dependent upon the ratio between the processed peak of Moran's Z histogram and the original image. The peak ratio of Moran's Z histogram can be used to quantify the degree of image blurring. This method produces better results than the standard gray level distribution deviation. The proposed method can also be used to discern blurriness in an image using different image compression algorithms.
doi:10.1007/s10278-005-8736-y
PMCID: PMC3045183  PMID: 16283091
Moran peak ratio; image blurring; image quality
5.  Quality Degradation in Lossy Wavelet Image Compression  
Journal of Digital Imaging  2003;16(2):210-215.
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.
doi:10.1007/s10278-003-1652-0
PMCID: PMC3046470  PMID: 14517721
Wavelet compression; quality evaluation; Moran test

Results 1-5 (5)