Effects of JPEG and wavelet compression of spiral low-dose CT images on detection of small lung cancers

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.

The effect of JPEG2000 compression on detection of skull fractures

RATIONAL AND OBJECTIVES

To investigate the effect of the Joint Photographic Experts Group (JPEG2000) 30:1 and 60:1 lossy compression on the detection of cranial vault fractures when compared to JPEG2000 lossless compression.

MATERIALS AND METHODS

Fifty cranial computed tomography (CT) images were processed with three different level of JPEG2000 compression (lossless, 30:1 lossy, and 60:1 lossy) creating three sets of images. These were presented to five musculoskeletal specialists and five neuroradiologists. Each reader read at two of the three compression levels. Twenty-two cases contained a single fracture; the remaining 28 cases contained no fractures. Observers were asked to identify the presence or absence of a fracture, to locate its site, and rate their degree of confidence. Receiver operating characteristic (ROC), jackknife free-response receiver operating characteristic (JAFROC) and the Dorfman-Berbaum-Metz multiple reader multiple case (DBM-MRMC) analyses were used to explore differences between the lossless and lossy compressed images.

RESULTS

JPEG2000 lossless and 30:1 lossy compression demonstrated no significant difference in their performance with JAFROC and DBM-MRMC analysis (P < .416); however, JPEG2000 30:1 lossy compression demonstrated significantly better performance than 60:1 lossy compression (P < .016). A significant increase in misplaced confidence ratings was also seen with 60:1 (P < .037) over 30:1 lossy and lossless compression.

CONCLUSION

JPEG2000 60:1 compression degrades the detection of skull fractures significantly while increasing the confidence with which readers rate fractures compared with 30:1 lossy and lossless compression. JPEG2000 30:1 lossy compression does not significantly change performance when compared to JPEG2000 lossless for the detection of skull fractures on CT.

JPEG2000 for automated quantification of immunohistochemically stained cell nuclei: a comparative study with standard JPEG format

The Joint Photographic Experts Group (JPEG) standard format is one of the most widely used in image compression technologies. More recently, JPEG2000 format has emerged as a state-of-the-art technology that provides substantial improvements in picture quality at higher compression ratios. However, there has been no attempt to date to determine which of the two compression formats produces less variability in the automated evaluation of immunohistochemically stained digital images in agreement with their compression rates and complexity degrees. The evaluation of Ki67 and FOXP3 immunohistochemical nuclear markers was performed in a total of 329 digital images: 47 were captured in uncompressed Tagged Image File Format (TIFF), 141 were converted to three JPEG compressed formats (47 each with 1:3, 1:23 and 1:46 compression) and 141 were converted to three JPEG2000 compressed formats (47 each with 1:3, 1:23 and 1:46 compression). The count differences between images in TIFF versus JPEG formats were compared with those obtained between images in TIFF versus JPEG2000 formats at the three levels of compression. It was found that, using JPEG2000 compression, the results of the stained nuclei count are close enough to the results obtained with uncompressed images, especially in highly complex images at minimum and medium compression. Otherwise, in images of low complexity, JPEG and JPEG2000 had similar count efficiency to that of the original TIFF images at all compression levels. These data suggest that JPEG2000 could give rise to an efficient means of storage, reducing file size and storage capacity, without compromise on the immunohistochemical analytical quality.

Roundness variation in JPEG images affects the automated process of nuclear immunohistochemical quantification: correction with a linear regression model

The volume of digital image (DI) storage continues to be an important problem in computer-assisted pathology. DI compression enables the size of files to be reduced but with the disadvantage of loss of quality. Previous results indicated that the efficiency of computer-assisted quantification of immunohistochemically stained cell nuclei may be significantly reduced when compressed DIs are used. This study attempts to show, with respect to immunohistochemically stained nuclei, which morphometric parameters may be altered by the different levels of JPEG compression, and the implications of these alterations for automated nuclear counts, and further, develops a method for correcting this discrepancy in the nuclear count. For this purpose, 47 DIs from different tissues were captured in uncompressed TIFF format and converted to 1:3, 1:23 and 1:46 compression JPEG images. Sixty-five positive objects were selected from these images, and six morphological parameters were measured and compared for each object in TIFF images and those of the different compression levels using a set of previously developed and tested macros. Roundness proved to be the only morphological parameter that was significantly affected by image compression. Factors to correct the discrepancy in the roundness estimate were derived from linear regression models for each compression level, thereby eliminating the statistically significant differences between measurements in the equivalent images. These correction factors were incorporated in the automated macros, where they reduced the nuclear quantification differences arising from image compression. Our results demonstrate that it is possible to carry out unbiased automated immunohistochemical nuclear quantification in compressed DIs with a methodology that could be easily incorporated in different systems of digital image analysis.

Effects of image compression on automatic count of immunohistochemically stained nuclei in digital images

This study investigates the effects of digital image compression on automatic quantification of immunohistochemical nuclear markers. We examined 188 images with a previously validated computer-assisted analysis system. A first group was composed of 47 images captured in TIFF format, and other three contained the same images converted from TIFF to JPEG format with 3x, 23x and 46x compression. Counts of TIFF format images were compared with the other three groups. Overall, differences in the count of the images increased with the percentage of compression. Low-complexity images (< or =100 cells/field, without clusters or with small-area clusters) had small differences (<5 cells/field in 95-100% of cases) and high-complexity images showed substantial differences (<35-50 cells/field in 95-100% of cases). Compression does not compromise the accuracy of immunohistochemical nuclear marker counts obtained by computer-assisted analysis systems for digital images with low complexity and could be an efficient method for storing these images.

Prediction of perceptible artifacts in JPEG2000 compressed abdomen CT images using a perceptual image quality metric

RATIONALE AND OBJECTIVES

To test a perceptual quality metric (high-dynamic range visual difference predictor, HDR-VDP) in predicting perceptible artifacts in Joint Photographic Experts Group 2000 compressed thin- and thick-section abdomen computed tomography images.

MATERIALS AND METHODS

A total of 120 thin (0.67 mm) and corresponding thick (5 mm) sections were compressed to ratios from 4:1 to 15:1. Peak signal-to-noise ratio (PSNR), HDR-VDP results (paired t-tests), and five radiologists' pooled responses for the presence of artifacts (exact tests for paired proportions) were compared between the thin and thick sections. For three subsets of 120 thin- (subset A), 120 thick- (subset B), and 60 thin- and 60 thick-section compressed images (subset C), receiver operating curve analysis was performed to compare PSNR and HDR-VDP in predicting the radiologists' responses. Using the cutoff values where the sum of sensitivity and specificity was the maximum in subset C, visually lossless thresholds (VLTs) were estimated for the 240 original images and the estimation accuracy was compared (McNemar test).

RESULTS

Thin sections showed more artifacts in terms of PSNR, HDR-VDP, and radiologists' responses (p < .0001). HDR-VDP outperformed PSNR for subset C (area under the curve: 0.97 versus 0.93, p = 0.03), whereas they did not differ significantly for subset A or B. Using the cutoff values, PSNR and HDR-VDP predicted the VLT accurately for 124 (51.7%) and 183 (76.3%) images, respectively (p < .0001).

CONCLUSIONS

HDR-VDP can predict the perceptible compression artifacts, and therefore can be potentially used to estimate the VLT for such compressions.

JPEG 2000 Compression of Abdominal CT: Difference in Tolerance Between Thin- and Thick-Section Images

OBJECTIVE

The purpose of our study was to compare the tolerance of Joint Photographic Experts Group (JPEG) 2000 compression between thin- and thick-section abdominal CT images.

MATERIALS AND METHODS

One hundred 0.67-mm-thick and corresponding 5-mm-thick images were compressed to four different levels: reversible and irreversible 6:1, 10:1, and 15:1. Five radiologists determined if the compressed images were distinguishable from the originals. The percentage of distinguishable pairs and peak signal-to-noise ratio (PSNR) were compared between the thin and thick sections. The visually lossless threshold was estimated by comparing the percentages of the distinguishable pairs between each irreversible compression and the reversible compression. Paired Student's t tests and exact tests for paired proportions were used.

RESULTS

Thin sections had smaller PSNRs at each compression level (p < 0.001). According to the pooled responses, the percentages of distinguishable pairs for the thin and thick sections, respectively, were 0% (0/100) and 0% at reversible compression, 27% and 0% at 6:1 (p < 0.001), 100% and 80% at 10:1 (p < 0.001), and 100% and 100% at 15:1. Artifacts increased significantly (p < 0.001) at 6:1 or more for the thin sections and at 10:1 and 15:1 for the thick sections, indicating that the visually lossless thresholds were below 6:1 and between 6:1 and 10:1, respectively.

CONCLUSION

Thin-section abdominal CT images are less tolerant of compression, and a lower compression level should be used for the visually lossless threshold.

Quality of compressed medical images

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 ( approximately 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 x 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.

Irreversible JPEG 2000 compression of abdominal CT for primary interpretation: assessment of visually lossless threshold

To estimate the visually lossless threshold for Joint Photographic Experts Group (JPEG) 2000 compression of contrast-enhanced abdominal computed tomography (CT) images, 100 images were compressed to four different levels: a reversible (as negative control) and irreversible 5:1, 10:1, and 15:1. By alternately displaying the original and the compressed image on the same monitor, six radiologists independently determined if the compressed image was distinguishable from the original image. For each reader, we compared the proportion of the compressed images being rated distinguishable from the original images between the reversible compression and each of the three irreversible compressions using the exact test for paired proportions. For each reader, the proportion was not significantly different between the reversible (0-1%, 0/100 to 1/100) and irreversible 5:1 compression (0-3%). However, the proportion significantly increased with the irreversible 10:1 (95-99%) and 15:1 compressions (100%) versus reversible compression in all readers (P < 0.001); 100 and 95% of the 5:1 compressed images were rated indistinguishable from the original images by at least five of the six readers and all readers, respectively. Irreversibly 5:1 compressed abdominal CT images are visually lossless and, therefore, potentially acceptable for primary interpretation.

Computer-aided detection of solid lung nodules in lossy compressed multidetector computed tomography chest exams

RATIONALE AND OBJECTIVES

To assess the effect of three-dimensional (3D) lossy image compression of multidetector computed tomography chest scans on computer-aided detection (CAD) of solid lung nodules greater than 4 mm in size.

MATERIALS AND METHODS

A total of 120 cases, acquired with 1.25-mm collimation, were collected from 5 different sites, of which 66/120 were low-dose cases. Two chest radiologists established that 37 cases had no actionable lung nodules; the remaining 83 cases contained 169 nodules (range 3.8-35.0 mm, mean 5.8 mm +/- 3.0 [SD]). All cases were compressed using the 3D Set Partitioning in Hierarchical Trees algorithm to 24:1, 48:1, and 96:1 levels. A study of the effect of compression on computer-aided detection (CAD) sensitivity was performed at operating points of 2.5 false marks (FM), 5 FM, and 10 FM per case using McNemar's test. Logistic regression models were used to evaluate the impact on CAD sensitivity by compression level on nodule and image characteristics.

RESULTS

Compared with no compression, there was no significant degradation in CAD sensitivity found at any of the studied compression levels and operating points. However, between compression levels, there was marginal association with sensitivity. Specifically, 24:1 level was significantly better than 96:1 at all operating points, and occasionally better than no compression at 10 FM/case. Based on multivariate analysis, nodule location was found to be a significant predictor (P = .01) with a lower sensitivity associated with juxtapleural nodules. Nodule size, dose, reconstruction filter, and contrast medium were not significant predictors.

CONCLUSION

CAD detection performance of solid lung nodules did not suffer until 48:1 compression.

JPEG2000 Compression of Thin-Section CT Images of the Lung: Effect of Compression Ratio on Image Quality

PURPOSE

To assess retrospectively the effect of the Joint Photographic Experts Group 2000 (JPEG2000) compression ratio on the quality of thin-section computed tomographic (CT) images.

MATERIALS AND METHODS

In this institutional review board-approved investigation (protocol 238/2004), thin-section CT images were subjected to irreversible JPEG2000 compression by using five compression ratios (3:1, 5:1, 7:1, 9:1, and 11:1). Three radiologists independently evaluated 60 thin-section CT images, of various diseases, that were obtained with single-detector (weighted dose index, 14.4 mGy) and multidetector (weighted dose index, 9.8 mGy) CT. Toggling between the original and compressed images, readers had to identify the original image by using a forced-choice two-alternative model and to subjectively rank the quality of what they believed to be the compressed image. To assess the reader's ability to distinguish the compressed from the original image, a binomial test was used. Bonferroni correction was applied for all multiple tests.

RESULTS

Images compressed with a ratio of 3:1 were not distinguishable from original images (P > .2 for all readers). With use of the 5:1 ratio, minor differences in appearance between the compressed and original images were seen by one of the three readers. With use of higher compression ratios (>/=7:1), all readers (P < .001) recognized the original image. The quality of more than 90% of the images compressed with a 7:1 or higher ratio was substantially degraded. Single-detector and multidetector CT results were not significantly different.

CONCLUSION

The highest ratio that yielded visually lossless compression of thin-section CT images was 3:1. With the 5:1 ratio, there was minor image quality loss, while use of higher compression ratios (>/=7:1) caused substantial degradation of image quality and potential loss of diagnostic information.

Effect of CT image compression on computer-assisted lung nodule volume measurement

PURPOSE

To evaluate the effect of two-dimensional wavelet-based computed tomographic (CT) image compression according to the Joint Photographic Experts Group (JPEG) 2000 standard on computer-assisted assessment of nodule volume.

MATERIALS AND METHODS

This HIPAA-compliant study was approved by the research board at the authors' institution; patients' informed consent was not required. Fifty-one nodules in 23 patients (seven men, 16 women; mean age, 59 years; age range, 39-75 years) were selected on low-dose CT scans that were compressed to levels of 10:1, 20:1, 30:1, and 40:1 by using a two-dimensional JPEG 2000 wavelet-based image compression method. Nodules were classified according to size (< or = 5 mm or > 5 mm in diameter), location (central, peripheral, or abutting pleura or fissures), and attenuation (solid, calcified, or subsolid). Regions of interest were placed on the original images and transposed onto compressed images. Nodule volumes on original (noncompressed) and compressed images were measured by using a computer-assisted method. A mixed-model analysis of variance was conducted for statistical evaluation.

RESULTS

Nodule volumes averaged 388.1 mm3 (range, 34-3474 mm3). There were three calcified, 33 solid noncalcified, and 15 subsolid nodules (13 with ground-glass attenuation). Average volume decreased with increasing compression level, to 383 mm3 (10:1), 370 mm3 (20:1), 360 mm3 (30:1), and 354 mm3 (40:1). No significant difference was identified between measurements obtained on original images and those compressed to a level of 10:1. Significant differences were noted, however, between original images and those compressed to a level of 20:1 or greater (P < .05). Compression level significantly interacted with nodule size, location, and attenuation (P < .001). The effect of compression was greater for nodules with ground-glass attenuation than for those with higher attenuation values. The difference in mean volumes between original images and those compressed to a level of 20:1 was 34.9 mm3 for nodules with ground-glass attenuation, compared with 8.3 mm3 for higher-attenuation nodules, a 4.2-fold difference.

CONCLUSION

Nodule volumes measured on images compressed to a level of 20:1 differed significantly from those measured on noncompressed images, especially for nodules with ground-glass attenuation. This difference could affect the assessment of nodule change in size as measured with computer-assisted methods.

Fourier and wavelet analyses of dental radiographs detect trabecular changes in osteoporosis

OBJECTIVES

Osteoporosis results in loss of bone mass and microarchitectural deterioration. Dental radiographs potentially offer a means of screening for osteoporosis as they are commonly made on adults. Spatial frequency analyses are well suited to detect subtle changes in image patterns. We hypothesize that individuals with osteoporosis exhibit an altered radiographic trabecular pattern that can be detected by spatial frequency and strut analysis.

STUDY DESIGN

Maxillary and mandibular periapical radiographs of 26 women with osteoporosis and 23 controls were examined using one-dimensional discrete Fourier and wavelet analyses in both jaws to measure the spatial frequency distributions of trabecular structures. A strut analysis was also performed.

RESULTS

Individuals with osteoporosis revealed an altered trabecular pattern compared to controls. Using Fourier and strut variables allows classification of subjects with 92% sensitivity, 96% specificity, and a 22% cross-validation error rate. Wavelet analysis was also useful but did not perform better than Fourier analysis for subject classification.

CONCLUSIONS

Spatial frequency analysis of digitized dental radiographs, especially Fourier analysis, and strut analysis provide value for identifying individuals with osteoporosis.

Wavelet compression of low-dose chest CT data: effect on lung nodule detection

PURPOSE

To assess the effect of using a lossy Joint Photographic Experts Group standard for wavelet image compression, JPEG2000, on pulmonary nodule detection at low-dose computed tomography (CT).

MATERIALS AND METHODS

One hundred sets of lung CT data ("cases") were compressed to 30:1, 20:1, and 10:1 levels by using a wavelet-based JPEG2000 method, resulting in 400 test cases. Each case consisted of nine 1.25-mm sections that had been obtained with 20-40 mAs. Four thoracic radiologists independently interpreted the test case images. Performance was measured by using area under the receiver operating characteristic (ROC) curve (Az) and conventional sensitivity and specificity analyses.

RESULTS

There were 51 cases with and 49 without lung nodules. Az values were 0.984, 0.988, 0.972, 0.921, respectively, for original and 10:1, 20:1, and 30:1 compressed images. Az values decreased significantly at 30:1 (P =.014) but not at 10:1 compression, with a trend toward significant decrease at 20:1 (P =.051). Specificity values were unaffected by compression (>98.0% at all compression levels). Sensitivity values were 86.3% (176 of 204 test cases with nodules), 77.9% (159 of 204 cases), 76.5% (156 of 204 cases), and 70.1% (143 of 204 cases), respectively, for original and 10:1, 20:1, and 30:1 compressed images. Results of logistic regression model analysis confirmed the significant effects of compression rate and nodule attenuation, size, and location on sensitivity (P <.05).

CONCLUSION

While no reduction in nodule detection at 10:1 compression levels was demonstrated by using ROC analysis, a significant decrease in sensitivity was identified. Further investigation is needed before widespread use of image compression technology in low-dose chest CT can be recommended.

Application of adaptive image processing technique to real-time spatial compound ultrasound imaging improves image quality

RATIONALE AND OBJECTIVES

To assess the impact of adaptive filter postprocessing on quality of ultrasound images.

METHODS

Ultrasound images acquired with real-time spatial compound imaging (SonoCT imaging) were subsequently processed with an adaptive real time algorithm (XRES imaging). Conventional and XRES-processed images from abdominal, pediatric or small parts ultrasound explorations were compared. The delineation of borders, tissue contrast, amount of noise, and overall image quality were evaluated.

RESULTS

Delineation of borders and tissue contrast were improved on all images (P < 0.05). The amount of noise was reduced (P < 0.05). The overall image quality was improved for abdominal, pediatric and small parts ultrasound explorations (P < 0.05). No image degradation was found.

CONCLUSIONS

Adaptive processing provided better image quality without loss of clinically useful information.