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issn:1618-727
1.  Multi-scale Regularization Approaches of Non-parametric Deformable Registrations 
Journal of Digital Imaging  2010;24(4):586-597.
Most deformation algorithms use a single-value smoother during optimization. We investigate multi-scale regularizations (smoothers) during the multi-resolution iteration of two non-parametric deformable registrations (demons and diffeomorphic algorithms) and compare them to a conventional single-value smoother. Our results show that as smoothers increase, their convergence rate decreases; however, smaller smoothers also have a large negative value of the Jacobian determinant suggesting that the one-to-one mapping has been lost; i.e., image morphology is not preserved. A better one-to-one mapping of the multi-scale scheme has also been established by the residual vector field measures. In the demons method, the multi-scale smoother calculates faster than the large single-value smoother (Gaussian kernel width larger than 0.5) and is equivalent to the smallest single-value smoother (Gaussian kernel width equals to 0.5 in this study). For the diffeomorphic algorithm, since our multi-scale smoothers were implemented at the deformation field and the update field, calculation times are longer. For the deformed images in this study, the similarity measured by mean square error, normal correlation, and visual comparisons show that the multi-scale implementation has better results than large single-value smoothers, and better or equivalent for smallest single-value smoother. Between the two deformable registrations, diffeormophic method constructs better coherence space of the deformation field while the deformation is large between images.
doi:10.1007/s10278-010-9313-6
PMCID: PMC3138939  PMID: 20574767
Deformation registration; multi-scale regularization; diffeomorphic algorithm; demon algorithm
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
6.  Beam Hardening Correction for Computed Tomography Images Using a Postreconstruction Method and Equivalent Tisssue Concept  
Journal of Digital Imaging  2001;14(2):54-61.
A postreconstruction method for correcting the beam-hardening artifacts in computed tomography (CT) images is proposed. This method does not require x-ray spectrum measurement. The authors assumed that a pixel in a CT image can be decomposed into equivalent tissue percentages, depending on its CT number. A scout view of the step wedges made of these equivalent tissues was performed to obtain a beam-hardening correction curve for each tissue. Projecting through the CT image from various angles generated simulated projection data and the total thickness of each tissue along the ray. The correction term was estimated using the tissue thickness traveled by the ray, and this term was then added to its corresponding projection data. A second reconstruction using the corrected projection data yielded a beam-hardening corrected image. The preliminary results show that this method reduces beam hardening artifacts by 14% for aluminum and increased the object contrast by 18% near the aluminum-water boundary. The variation in CT numbers at different locations were reduced, and the aluminum CT number also was restored.
doi:10.1007/s10278-001-0003-2
PMCID: PMC3452760  PMID: 11440255

Results 1-6 (6)