Prediction of Perceptible Artifacts in JPEG2000 Compressed Abdomen CT Images Using a Perceptual Image Quality Metric

Kim, Bohyoung; Lee, Kyung Ho; Kim, Kil Joong; Mantiuk, Rafał; Bajpai, Vasundhara; Kim, Tae Jung; Kim, Young Hoon; Yoon, Chang Jin; Hahn, Seokyung

doi:10.1016/j.acra.2007.10.018

Cited by 20 publications

(27 citation statements)

References 42 publications

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“…Necessitated by teleradiology and large imaging data sets, researchers in various studies have investigated the acceptability of lossy image compression. In these studies, researchers found that high compression ratios, in excess of 8:1, can be achieved without perceptible degradation of image quality (9)(10)(11)(12). Although these methods help diminish time for electronic transfer of images, they do not directly reduce the time required for acquisition of the images.…”

Section: Discussionmentioning

confidence: 99%

Improved Pediatric MR Imaging with Compressed Sensing

Vasanawala¹,

Alley²,

Hargreaves³

et al. 2010

Radiology

238

203

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Purpose:To develop a method that combines parallel imaging and compressed sensing to enable faster and/or higher spatial resolution magnetic resonance (MR) imaging and show its feasibility in a pediatric clinical setting. Materials and Methods:Institutional review board approval was obtained for this HIPAA-compliant study, and informed consent or assent was given by subjects. A pseudorandom k-space undersampling pattern was incorporated into a threedimensional (3D) gradient-echo sequence; aliasing then has an incoherent noiselike pattern rather than the usual coherent fold-over wrapping pattern. This k-space-sampling pattern was combined with a compressed sensing nonlinear reconstruction method that exploits the assumption of sparsity of medical images to permit reconstruction from undersampled k-space data and remove the noiselike aliasing. Thirty-four patients (15 female and 19 male patients; mean age, 8.1 years; range, 0-17 years) referred for cardiovascular, abdominal, and knee MR imaging were scanned with this 3D gradient-echo sequence at high acceleration factors. Obtained k-space data were reconstructed with both a traditional parallel imaging algorithm and the nonlinear method. Both sets of images were rated for image quality, radiologist preference, and delineation of specifi c structures by two radiologists. Wilcoxon and symmetry tests were performed to test the hypothesis that there was no signifi cant difference in ratings for image quality, preference, and delineation of specifi c structures. Results:Compressed sensing images were preferred more often, had signifi cantly higher image quality ratings, and greater delineation of anatomic structures ( P , .001) than did images obtained with the traditional parallel reconstruction method. Conclusion:A combination of parallel imaging and compressed sensing is feasible in a clinical setting and may provide higher resolution and/or faster imaging, addressing the challenge of delineating anatomic structures in pediatric MR imaging.q RSNA, 2010

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Section: Discussionmentioning

confidence: 99%

Improved Pediatric MR Imaging with Compressed Sensing

Vasanawala¹,

Alley²,

Hargreaves³

et al. 2010

Radiology

238

203

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“…Several investigators have attempted to use image fidelity metrics, which measure the fidelity of a distorted image, to predict the VLT. Some of their results have been promising (5,(9)(10)(11)16). However, to achieve the VLT of a given CT image, it would be necessary to iteratively compress the image to multiple compression levels and to measure the image fidelity at each compression level until the measured fidelity reaches a threshold predefined by the metric.…”

Section: Methodsmentioning

confidence: 99%

“…The purpose of using five subsets was to introduce heterogeneity in the test data set in terms of structural content and image noise. The heterogeneity was considered to be important in measuring the robustness of the constructed models in predicting the VLTs of CT images, as it is well known that image compressibility is markedly affected by structural content (5,7,8) and image noise level (11)(12)(13)16).…”

Section: Implications For Patient Carementioning

confidence: 99%

Use of Image Features in Predicting Visually Lossless Thresholds of JPEG2000 Compressed Body CT Images: Initial Trial

Kim¹,

Kim²,

Lee³

et al. 2013

Radiology

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Purpose:To test the image features that may be useful in predicting the visually lossless thresholds (VLTs) of body computed tomographic (CT) images for Joint Photographic Experts Group 2000 (JPEG2000) compression. Materials andMethods:The institutional review board approved this study, with a waiver of informed patient consent. One hundred body CT studies obtained in different patients by using five scanning protocols were obtained, and 100 images, each of which was selected from each of the 100 studies, were collected. Five radiologists independently determined the VLT of each image for JPEG2000 compression by using the QUEST algorithm. The 100 images were randomly divided into two data sets-the training set (50 images) and the testing set (50 images)-and the division was repeated 200 times. For each of the 200 divisions, a multiple linear regression model was constructed on a training set and tested on a testing set regarding each of five image features-standard deviation of image intensity, image entropy, relative percentage of low-frequency (LF) energy, variation in high-frequency (HF) energy, and visual complexity-as independent variables and considering the VLTs determined with the median value of the radiologists' responses as a dependent variable. The root mean square residual and intraclass correlation coefficient (ICC) for the 200 divisions between the VLTs predicted by the models and those determined by radiologists were compared between the models by using repeated-measures analysis of variance with post-hoc comparisons. Results:Mean root-mean-square residuals for multiple linear regression models constructed with variation in HF energy (1.20 6 0.10 [standard deviation]) and visual complexity (1.09 6 0.07) were significantly lower than those for standard deviation of image intensity (1.65 6 0.13), image entropy (1.63 6 0.14), and relative percentage of LF energy (1.58 6 0.12) (P , .01). ICCs for variation in HF energy (0.64 6 0.05) and visual complexity (0.71 6 0.04) were significantly higher than those for standard deviation of image intensity (0.04 6 0.02), image entropy (0.05 6 0.02), and relative percentage of LF energy (0.20 6 0.04) (P , .01). Conclusion:Among the five tested image features, variation in HF energy and visual complexity were the most promising in predicting the VLTs of body CT images for JPEG2000 compression.q RSNA, 2013Supplemental material: http://radiology.rsna.org/lookup/suppl

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“…In [8] and [3], they also noted that acceptable compression ratios are highly dependent on noise level and that, in the case of CT scans, this level is affected by the radiation dose as well as acquisition time. In [21], [22] and [23], the authors used two set of images of different slice thickness generated from the same raw projection data ; one group was reconstructed with slice thickness of 0.67mm while the other at 5mm. They showed, with radiologist observations, that thick slices (5mm) could be slightly more compressed that thin slices (0.67mm) to achieve the visually lossless threshold.…”

Section: Related Workmentioning

confidence: 99%

“…In [21,22,23] a group of researchers used the High Dynamic Range Visual Difference Predictor(HDR-VDP) algorithm on abdomen and body images compressed with ratio ranging from 4:1 to 15:1. PSNR, HDR-VDP, MultiScale Structural Similarity and five radiologist's pooled assessment of artifacts were compared.…”

Section: Related Workmentioning

confidence: 99%

Perceptual quantitative quality assessment of JPEG2000 compressed ct images with various slice thicknesses

Pambrun

Noumeir

2011

2011 IEEE International Conference on Multimedia and Expo

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Modern medical equipments produce huge amounts of data that need to be archived for long periods and efficiently transferred over networks. Data compression plays an essential role in reducing the amount of medical imaging data. Medical images can usually be compressed by a factor of three before any degradation appears. Higher compression levels are desirable but can only be achieved with lossy compression, thus scarifying image quality. The diagnosis value of compressed medical images has been studied and recommendations about maximum acceptable compression ratios have been provided based on qualitative visual analysis. It has been suggested, without further investigation, that CT images, with thicknesses below five mm, cannot undergo lossy compression if diagnostic value needed to be preserved. In this paper, we present an objective quantitative quality assessment of compressed CT images using Visual Signal to Noise Ratio. Our results show that visual fidelity can be significantly affected by two factors, slice thickness and exposure time, for images compressed using the same compression ratio.

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Prediction of Perceptible Artifacts in JPEG2000 Compressed Abdomen CT Images Using a Perceptual Image Quality Metric

Cited by 20 publications

References 42 publications

Improved Pediatric MR Imaging with Compressed Sensing

Improved Pediatric MR Imaging with Compressed Sensing

Use of Image Features in Predicting Visually Lossless Thresholds of JPEG2000 Compressed Body CT Images: Initial Trial

Perceptual quantitative quality assessment of JPEG2000 compressed ct images with various slice thicknesses

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