Assessment of Visually Lossless Irreversible Image Compression: Comparison of Three Methods by Using an Image-Comparison Workstation

Slone, Richard M.; Foos, David H.; Whiting, Bruce R.; Muka, Edward; Rubin, David A.; Pilgram, Thomas K.; Kohm, Kevin S.; Young, S. Susan; Ho, P. T. P.; Hendrickson, Dan

doi:10.1148/radiology.215.2.r00ap47543

Cited by 84 publications

(76 citation statements)

References 23 publications

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“…Several studies have suggested that levels between 10:1 and 20:1 are the lower and upper bounds of``compression tolerance'' for CT and MR images. 25 The results of our study are concordant with speculation that these compression levels may not aect diagnostic performance negatively.…”

Section: Discussionsupporting

confidence: 89%

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Computed Tomography Diagnosis Utilizing Compressed Image Data: An ROC Analysis Using Acute Appendicitis as a Model

Megibow

Rusinek

Lisi

et al. 2002

Journal of Digital Imaging

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Using receiver-operating characteristic (ROC) methodology, the ability to diagnose acute appendicitis with computed tomography (CT) images displayed at varying levels of lossy compression was evaluated. Nine sequential images over the ileocecal region were obtained from 53 consecutive patients with right lower quadrant pain who were clinically suspected to have acute appendicitis. Thirty were proven surgically to have acute appendicitis, alternative diagnoses con®rmed in 23. The image sets were subjected to a lossy wavelet-based compression algorithm``Embedded Predictive Wavelet Image Coder'' (EPWIC). Compression levels were: none, 8:1, 16:1, and 24:1, resulting in 4 sets of images per patient. Image sets were randomized and evaluated separately by 4 body radiologists on a 1,024´768-pixel SVGA color PC monitor in 512´512 format. The readers were aware of the clinical suspicion of appendicitis but were unaware of the positive fraction of cases. Individual and combined reader ROC and v 2 analyses of sensitivity, speci®city, and accuracy were determined. For all readers, sensitivity decreases at 16:1 and 24:1 levels (P < 0.01, P < 0.001, respectively). Accuracy decreased at 24:1 levels (P < 0.01). Speci®city was unaected. By ROC analysis there was statistically signi®cantly decreased area under the curve at 24:1 levels (P < 0.02) as compared with uncompressed images. Finite levels of lossy wavelet compression may be applied to CT images without compromising diagnostic performance.

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Section: Discussionsupporting

confidence: 89%

“…25 The changes induced by this magnitude of wavelet compression tend to eliminate only very high frequency noise and otherwise are visually imperceptible. As the compression ratio is increased, the observer begins to appreciate changes in the form of increased blurring and the appearance of artifacts.…”

Section: Discussionmentioning

confidence: 99%

Computed Tomography Diagnosis Utilizing Compressed Image Data: An ROC Analysis Using Acute Appendicitis as a Model

Megibow

Rusinek

Lisi

et al. 2002

Journal of Digital Imaging

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“…Each reader was asked to independently indicate if the test image on the right was identical to the PR image on the left, or if a detectable difference was present (binary response). This method has been advocated to be highly sensitive by Slone et al 17 for the evaluation of irreversibly compressed radiographic images, as comparison of a test image with a known original maximizes reader's sensitivity to the difference. 18 During the visual analysis, images were displayed using a standard diagnostic review workstation (DS3000, Impax version 4.5; Agfa HealthCare, Mortsel, Belgium), dual flat-panel monochrome monitors (ME315; Totoku, Tokyo, Japan) with a matrix size of 1,536×2,048 and a diagonal display size of 20.8 in.…”

Section: Visual Analysismentioning

confidence: 99%

Summation or Axial Slab Average Intensity Projection of Abdominal Thin-section CT Datasets: Can They Substitute for the Primary Reconstruction from Raw Projection Data?

et al. 2007

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We hypothesized that that the summation or axial slab average intensity projection (AIP) techniques can substitute for the primary reconstruction (PR) from a raw projection data for abdominal applications. To compare with PR datasets (5-mm thick, 20% overlap) in 150 abdominal studies, corresponding summation and AIP datasets were calculated from 2-mm thick images (50% overlap). The root-mean-square error between PR and summation images was significantly greater than that between PR and AIP images (9.55 [median] vs. 7.12, pG0.0001, Wilcoxon signed-ranks test). Four radiologists independently compared 2,000 test images (PR [as control], summation, or AIP) and their corresponding PR images to prove that the identicalness of summation or AIP images to PR images was not 1% less than the assessed identicalness of PR images to themselves (Wald-type test for clustered matched-pair data in a non-inferiority design). For each reader, both summation and AIP images were not inferior to PR images in terms of being rated identical to PR (p G0.05). Although summation and AIP techniques produce images that differ from PR images, these differences are not easily perceived by radiologists. Thus, the summation or AIP techniques can substitute for PR for the primary interpretation of abdominal CT.

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“…For some time now, considerable effort has been made to evaluate digital image compression techniques to permit the image quality required for medical images [9][10][11][12][13][14][15][16][17][18][19][20]. These evaluations include subjective and objective metrics.…”

Section: Introductionmentioning

confidence: 99%

JPEG2000 Still Image Coding Quality

Chen

Lin

et al. 2013

J Digit Imaging

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This work demonstrates the image qualities between two popular JPEG2000 programs. Two medical image compression algorithms are both coded using JPEG2000, but they are different regarding the interface, convenience, speed of computation, and their characteristic options influenced by the encoder, quantization, tiling, etc. The differences in image quality and compression ratio are also affected by the modality and compression algorithm implementation. Do they provide the same quality? The qualities of compressed medical images from two image compression programs named Apollo and JJ2000 were evaluated extensively using objective metrics. These algorithms were applied to three medical image modalities at various compression ratios ranging from 10:1 to 100:1. Following that, the quality of the reconstructed images was evaluated using five objective metrics. The Spearman rank correlation coefficients were measured under every metric in the two programs. We found that JJ2000 and Apollo exhibited indistinguishable image quality for all images evaluated using the above five metrics (r>0.98, p<0.001). It can be concluded that the image quality of the JJ2000 and Apollo algorithms is statistically equivalent for medical image compression.

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Assessment of Visually Lossless Irreversible Image Compression: Comparison of Three Methods by Using an Image-Comparison Workstation

Cited by 84 publications

References 23 publications

Computed Tomography Diagnosis Utilizing Compressed Image Data: An ROC Analysis Using Acute Appendicitis as a Model

Computed Tomography Diagnosis Utilizing Compressed Image Data: An ROC Analysis Using Acute Appendicitis as a Model

Summation or Axial Slab Average Intensity Projection of Abdominal Thin-section CT Datasets: Can They Substitute for the Primary Reconstruction from Raw Projection Data?

JPEG2000 Still Image Coding Quality

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