Definition of Compression Ratio: Difference Between Two Commercial JPEG2000 Program Libraries

Computed Tomography Image Compressibility and Limitations of Compression Ratio-Based Guidelines

Finding optimal compression levels for diagnostic imaging is not an easy task. Significant compressibility variations exist between modalities, but little is known about compressibility variations within modalities. Moreover, compressibility is affected by acquisition parameters. In this study, we evaluate the compressibility of thousands of computed tomography (CT) slices acquired with different slice thicknesses, exposures, reconstruction filters, slice collimations, and pitches. We demonstrate that exposure, slice thickness, and reconstruction filters have a significant impact on image compressibility due to an increased high frequency content and a lower acquisition signal-to-noise ratio. We also show that compression ratio is not a good fidelity measure. Therefore, guidelines based on compression ratio should ideally be replaced with other compression measures better correlated with image fidelity. Value-of-interest (VOI) transformations also affect the perception of quality. We have studied the effect of value-of-interest transformation and found significant masking of artifacts when window is widened.

Evaluation of the robustness of the preprocessing technique improving reversible compressibility of CT images: tested on various CT examinations

PURPOSE

To modify the preprocessing technique, which was previously proposed, improving compressibility of computed tomography (CT) images to cover the diversity of three dimensional configurations of different body parts and to evaluate the robustness of the technique in terms of segmentation correctness and increase in reversible compression ratio (CR) for various CT examinations.

METHODS

This study had institutional review board approval with waiver of informed patient consent. A preprocessing technique was previously proposed to improve the compressibility of CT images by replacing pixel values outside the body region with a constant value resulting in maximizing data redundancy. Since the technique was developed aiming at only chest CT images, the authors modified the segmentation method to cover the diversity of three dimensional configurations of different body parts. The modified version was evaluated as follows. In randomly selected 368 CT examinations (352,787 images), each image was preprocessed by using the modified preprocessing technique. Radiologists visually confirmed whether the segmented region covers the body region or not. The images with and without the preprocessing were reversibly compressed using Joint Photographic Experts Group (JPEG), JPEG2000 two-dimensional (2D), and JPEG2000 three-dimensional (3D) compressions. The percentage increase in CR per examination (CRI) was measured.

RESULTS

The rate of correct segmentation was 100.0% (95% CI: 99.9%, 100.0%) for all the examinations. The median of CRI were 26.1% (95% CI: 24.9%, 27.1%), 40.2% (38.5%, 41.1%), and 34.5% (32.7%, 36.2%) in JPEG, JPEG2000 2D, and JPEG2000 3D, respectively.

CONCLUSIONS

In various CT examinations, the modified preprocessing technique can increase in the CR by 25% or more without concerning about degradation of diagnostic information.

JPEG2000 still image coding quality

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.

JPEG2000 compression of CT images used for measuring coronary artery calcification score: assessment of optimal compression threshold

OBJECTIVE

The purpose of our study was to assess the acceptable compression threshold for JPEG2000 compression of CT images used for measuring coronary artery calcification scores (CACS) in terms of variability.

MATERIALS AND METHODS

In a retrospective review, 80 patients who had undergone CT for determination of the CACS were compiled in four subsets (20 scans each) according to CACS: 0, subset A; > 0 to ≥ 100, subset B; > 100 to ≤ 400, subset C; and > 400, subset D. Each scan was compressed using eight compression ratios (CRs). We measured the CACS on all 720 CT scans (80 original and 640 compressed scans). For each compressed scan, the variability in CACS was evaluated by comparing with the CACS of the corresponding original CT scan.

RESULTS

For each subset and each CR, we determined whether the upper limit of the one-sided 95% CI of the variability in CACS exceeded 5%. The variability in CACS tended to increase as the CR increased and tended to decrease in the order of increasing CACSs at each CR (i.e., subset B > subset C > subset D). With 5% as the limit of variability, acceptable compression CRs were between 20:1 and 25:1 for subset B; between 40:1 and 60:1 for subset C; and > 100:1 for subset D.

CONCLUSION

A level of 20:1 could be a potentially acceptable threshold for JPEG2000 compression of CT images used for measuring CACS, with 5% of the variability in CACS as the acceptable limit of variability.

Predicting the fidelity of JPEG2000 compressed CT images using DICOM header information

PURPOSE

To propose multiple logistic regression (MLR) and artificial neural network (ANN) models constructed using digital imaging and communications in medicine (DICOM) header information in predicting the fidelity of Joint Photographic Experts Group (JPEG) 2000 compressed abdomen computed tomography (CT) images.

METHODS

Our institutional review board approved this study and waived informed patient consent. Using a JPEG2000 algorithm, 360 abdomen CT images were compressed reversibly (n = 48, as negative control) or irreversibly (n = 312) to one of different compression ratios (CRs) ranging from 4:1 to 10:1. Five radiologists independently determined whether the original and compressed images were distinguishable or indistinguishable. The 312 irreversibly compressed images were divided randomly into training (n = 156) and testing (n = 156) sets. The MLR and ANN models were constructed regarding the DICOM header information as independent variables and the pooled radiologists' responses as dependent variable. As independent variables, we selected the CR (DICOM tag number: 0028, 2112), effective tube current-time product (0018, 9332), section thickness (0018, 0050), and field of view (0018, 0090) among the DICOM tags. Using the training set, an optimal subset of independent variables was determined by backward stepwise selection in a four-fold cross-validation scheme. The MLR and ANN models were constructed with the determined independent variables using the training set. The models were then evaluated on the testing set by using receiver-operating-characteristic (ROC) analysis regarding the radiologists' pooled responses as the reference standard and by measuring Spearman rank correlation between the model prediction and the number of radiologists who rated the two images as distinguishable.

RESULTS

The CR and section thickness were determined as the optimal independent variables. The areas under the ROC curve for the MLR and ANN predictions were 0.91 (95% CI; 0.86, 0.95) and 0.92 (0.87, 0.96), respectively. The correlation coefficients of the MLR and ANN predictions with the number of radiologists who responded as distinguishable were 0.76 (0.69, 0.82, p < 0.001) and 0.78 (0.71, 0.83, p < 0.001), respectively.

CONCLUSIONS

The MLR and ANN models constructed using the DICOM header information offer promise in predicting the fidelity of JPEG2000 compressed abdomen CT images.

Advantage in image fidelity and additional computing time of JPEG2000 3D in comparison to JPEG2000 in compressing abdomen CT image datasets of different section thicknesses

PURPOSE

This study aimed to assess the advantage of the Joint Photographic Experts Group 2000 (JPEG2000) 3D (part 2) over JPEG2000 in compressing abdomen computed tomography (CT) image data sets of different section thicknesses (STs).

METHODS

Twenty CT scans were reconstructed with six STs (0.67, 1, 2, 3, 4, and 5 mm) and were then compressed to seven compression ratios (CRs) (reversible, 6:1, 8:1, 10:1, 12:1, 14:1, and 16:1) using JPEG2000 and JPEG2000 3D algorithms. Computing (encoding and decoding) times were measured. The image fidelity of the compressed images was quantitatively measured with two computerized image fidelity metrics, peak signal-to-noise ratio (PSNR) and multiscale structural similarity (MS-SSIM). For 120 selected case-relevant images (20 patients x one image per patient x 6 STs), five radiologists independently compared original and compressed images and assessed the fidelity of the compressed images on a four-grade scale. Wilcoxon signed-rank tests and Friedman tests with post hoc Dunn tests were used for the comparisons between the two compressions and among the six STs, respectively

RESULTS

For each combination of the ST and irreversible CR, JPEG2000 3D showed higher image fidelity than JPEG2000 in terms of PSNR (p < 0.0001), MS-SSIM (p < 0.0001), and five radiologists' grading (p-values ranged from <0.0001 to 0.003). At each CR, the advantage of JPEG2000 3D in image fidelity, measured as the differences in the two computerized image fidelity metrics (PSNR and MS-SSIM), significantly increased as the ST increased from 0.67 to 2 mm, and then slowly decreased as the ST increased from 2 to 5 mm. Similar trends were observed in visual analyses of 120 selected images by five radiologists. At each CR, the 3D-to-2D encoding-time ratio significantly decreased (p < 0.001) as the ST increased from 0.67 to 2 mm, and then slowly increased (p < 0.001) as the ST increased from 2 to 5 mm. The 3D-to-2D decoding-time ratio at each CR did not show a notable biphasic trend across the ST.

CONCLUSIONS

In compressing abdomen CT image data sets of different STs, the advantage of JPEG2000 3D over JPEG2000 increases as the ST increases from 0.67 to 2 mm, and then slowly decreases as the ST increases from 2 to 5 mm. The practical advantage of JPEG2000 3D is limited for a submillimeter ST due to its greater computing time with only a marginal improvement in image fidelity.

A comparison of three image fidelity metrics of different computational principles for JPEG2000 compressed abdomen CT images

This study aimed to evaluate three image fidelity metrics of different computational principles--peak signal-to-noise ratio (PSNR), high-dynamic range visual difference predictor (HDR-VDP), and multiscale structural similarity (MS-SSIM)--in measuring the fidelity of JPEG2000 compressed abdomen computed tomography images from a viewpoint of visually lossless compression. Three hundred images with 0.67- or 5-mm section thickness were compressed to one of five compression ratios ranging from reversible compression to 15:1. The fidelity of each compressed image was measured by five radiologists' visual analyses (distinguishable or indistinguishable from the original) and the three metrics. The Spearman rank correlation coefficients of the PSNR, HDR-VDP, and MS-SSIM values with the number of readers responding as indistinguishable were 0.86, 0.94, and 0.86, respectively. Using the pooled readers' responses as the reference standard, the area under the receiver-operating-characteristic curve for the HDR-VDP (0.99) was significantly greater than that for the PSNR (0.95) (p < 0.001) and for the MS-SSIM (0.96) (p = 0.003), and there was no significant difference between the PSNR and MS-SSIM (p = 0.70). In measuring the image fidelity, the HDR-VDP outperforms the PSNR and MS-SSIM, and the MS-SSIM and PSNR are comparable.