Australian Institute of Radiography

The Radiographer 2006: 53 (3) 11–17Review

IntroductionThe increasing development of digital radiography (DR),

computed radiography (CR), digital fluoroscopy (DF) and digital mammography (DM) has enabled the gradual replacement of con-ventional hard-copy technology. Also, there has been a significant increase in digital imaging modalities such as multi-detector rows computed tomography (MDCT), spiral CT, magnetic resonance imaging (MRI), and ultrasound (US) in the diagnostic radiology department. The development of digital imaging technologies over the past two decades has made the idea of filmless radiology feasible. This is essential for the implementation of a hospital Picture Archiving and Communication System (PACS) and tele-radiology applications at many locations around the world.1 It is estimated that the totally filmless age will have arrived by 2026.2 This in itself will produce difficulties related to storage space and image transmission speed requirements.

There are two ways to solve these problems. One is to facilitate the data transmission rate, the other way to improve image trans-mission is to reduce image data file size by using compression algorithms. This study focused on the latter approach.

Wavelet algorithmThe wavelet algorithm on compactly supported analysis func-

tions was first published by Hungarian mathematician Alfred Harr in 1910.3 Wavelet transforms (WT) have recently gained wide

Wavelet compression algorithm applied to abdominal ultrasound images

Cheng-Hsun Lin,1 Ming-Che Lee,2 Su-Feng Pan,1,3 Chin-Yuan Lu1

1Department of Radiological Technology, Central Taiwan University of Science and Technology, 11 Pu Tzi Lane, (400) Taichung City, Taiwan

2Medical Staff Department of Medical Imaging, ChungHua Christian Hospital, 135 Nanhsiao Street, (500) Chunghua, Taiwan3Medical Staff Department of Oncology, Taichung Veteran General Hospital, 160 Sec. 3, Chung-Kang Rd, Taichung, Taiwan

Correspondence email jslin@ctust.edu.tw

Abstract We sought to investigate acceptable compression ratios of lossy wavelet compression on 640 × 480 × 8 abdominal ultrasound (US) images. We acquired 100 abdominal US images with normal and abnormal findings from the view station of a 932-bed teaching hospital. The US images were then compressed at quality factors (QFs) of 3, 10, 30, and 50 followed outcomes of a pilot study. This was equal to the average compression ratios of 4.3:1, 8.5:1, 20:1 and 36.6:1, respectively. Four objective measurements were carried out to examine and compare the image degradation between original and compressed images. Receiver operating characteristic (ROC) analysis was also introduced for subjective assessment. Five experienced and qualified radiologists as reviewers blinded to corresponding pathological findings, analysed paired 400 randomly ordered images with two 17-inch thin film transistor / liquid crystal display (TFT / LCD) monitors. At ROC analysis, the average area under curve (Az) for US abdominal image was 0.874 at the ratio of 36.6:1. The compressed image size was only 2.7% for US original at this ratio. The objective parameters showed the higher the mean squared error (MSE) or root mean squared error (RMSE) values, the poorer the image quality. The higher signal-to-noise ratio (SNR) or peak signal-to-noise ratio (PSNR) values indicated better image quality. The average RMSE, PSNR at 36.6:1 for US were 4.84 ± 0.14, 35.45 dB, respectively. This finding suggests that, on the basis of the patient sample, wavelet compression of abdominal US to a ratio of 36.6:1 did not adversely affect diagnostic performance or evaluation error for radiologists’ interpretation so as to risk affecting diagnosis

Keywords: ROC analysis, ultrasound image, wavelet compression

application in many areas. Some examples in the imaging domain are noise reduction, image enhancement, edge detection, feature extraction and texture analysis.4

Image evaluationThe aim of image evaluation in medical image compression

is to understand the compression ratios that can be used before diagnostic performance is affected. The best performance of a compressed image is judged by the accuracy and ease of inter-pretation by the radiologist, with the smallest possible number of bits, thereby speeding up transmission and minimising storage requirements. Applying a lossy compression algorithm on medi-cal images is inevitable. The trade-off between maintaining image quality and minimising the image file size is a major issue for the radiological department. Lossy compression actually involves discarding information permanently, so the likelihood is that use of lossy compression schemes will affect human perception and performance with increasing significance. Image evaluation is one of the methods used to surmount this dilemma.

There are two major classes of image evaluation methods cur-rently used in the medical image domain; objective and subjective evaluations.4,5,6

Objective evaluation Objective evaluation for compression / decompression systems,

The Radiographer 12

the analysis of error images (differences between original and decompressed images) could enable the assessment of both the system’s efficiency and the compromise between image quality and compression ratio performance. The parameters such as mean squared error (MSE), root mean squared error (RMSE), signal to noise ratio (SNR), and peak signal to noise ratio (PSNR) are the most popular methods for measuring image difference. Objective measurement cannot be used as the only determinant of image quality.5 A drawback of squared error parameter is that a slight spatial shift of an image will cause a large numerical distortion but no visual distortion. Conversely, a small average distortion can result in a damaging visual artefact if all the error is concen-trated in a small but important region, like a white dot presenting in a black background image. This dot may take only one pixel change, the image remains high value of objective measurement, yet the human visual system detects it easily.

Subjective evaluation Subjective evaluation of reconstructed images can be judged in

many ways. One of the current application methods is Receiver Operating Characteristic (ROC) analysis. The most common means of measuring diagnostic accuracy is based on the ROC analysis.7 The ROC analysis has also been widely applied in the

evaluation of medical imaging modalities over the last decades in radiology communities, and is used as a single quantitative index of diagnostic accuracy.8 Many researchers applied this method to examine their image quality on different set of images with differ-ent number of reviewers and different types of diseases.9,10,11

Materials and methods

Image acquisition The 100 abdominal ultrasound (US) images we obtained from

the view station of the 932-bed teaching hospital were archived. All images were downloaded in a Microsoft Windows BitMap format (BMP) file and written to compact discs with the permis-sion of the ethical committee of the hospital. Each US image consisted of 640 × 480 pixels and 24 bits per pixel (bpp) in depth. One image file was 921,600 bytes. US images were produced using digital ultrasound scanner (LOGIC IQ 700, General Electric, Wisconsin, USA). Although a US image has a pixel depth of 24 bpp, the make up of each pixel in Red, Green, and Blue (RGB) channels are not different as every colour image has a grey scale component that can be separated out from the hue and saturation components. This study focused on an 8 bpp grey scale image compression. Therefore, every US image was converted from 24 bpp into 8 bpp with Photoshop® 5.0 software (Adobe systems, California, USA) before applying compression. Each converted image size was 307,200 bytes, one-third of the original.

One hundred images were selected from 20 patients. If these, 38 images were normal and 62 were abnormal. Some images contained multiple findings.

Image compression The study was carried out using the wavelet-based program

developed by Compression Engine® (CE, Compression Engine Technologies Inc., South Carolina, USA) to compress the abdom-inal US images. A quality factor (QF) range was selected by the user. Four compression ratios were determined by a pilot study prior to the main study. The pilot study invited three qualified radiologists to determine the acceptable image quality at 10 dif-ferent levels of quality factors. A five-point system was used for image quality assessment using the following scale: 1, 2, 3, 4, and 5 represents excellent, good, acceptable, unacceptable, and

Figure 1 Bar graph shows the combined results for the US image size and compression ratio. The average wavelet sizes (kB) were 71.1, 36.3, 15.5, and 8.6 at mean compression ratios of 4.3:1, 8.5:1, 20:1, and 36.6:1. Error bars = 95% confidence intervals.

Table 1 Pilot study for US image compression ratio determination*

QFAverage

compressedsize

Average compressionratio

The mode values of three reviewers’ rating

A B C

3 71672 4.3 1 1 1

5 64092 4.8 1 1 1

10 38513 8.0 1 1 1

20 23893 12.9 1 and 2 1 1

30 16815 18.3 2 2 2

40 12512 24.7 3 3 3

50 9414 33.0 4 3 4

60 7368 42.2 5 5 5

70 5878 53.1 5 5 5

80 4813 65 5 5 5

*Three qualified radiologists determined the critical compression ratios in a pilot study. They were asked to determine the acceptable image quality by means of a five-point system. The point of 1, 2, 3, 4, and 5 represented excellent, good, acceptable, unacceptable and poor, respectively.

Cheng-Hsun Lin, Ming-Che Lee, Su-Feng Pan, Chin-Yuan Lu

The Radiographer 13

poor, respectively. The critical compression ratio for acceptance of the US image was determined at QF40 (Table 1). It equals an average compression ratio of 24.7. Therefore, 100 abdominal US images were compressed at four different levels of QF 3,10,30 and 50. Accordingly, 400 compressed abdominal US images were produced by this method.

Objective measurements Error measures were calculated to quantify differences between

the original and the compressed images. In this study, four mea-sures, MSE, RMSE, SNR, and PSNR, of image quality were applied to all compressed images. Consequently, 400 compressed images were measured.

Review panel membersFive reviewers were invited to take part in this study. In this

review panel all participants were consultant radiologists, with at least six years experience of interpretating of US images. The participants were all told that the ROC evaluation is tedious and time-consuming work and participation in this study was purely voluntary. No financial or academic benefits were offered or received for participation. The radiologists were free to withdraw consent and discontinue participation at any time without notice. The reviewers were also asked to sign a consent paper, provided by the authors, to verify that the work was voluntary. None of the participants withdrew during the course of the review process.

Results

Image size and compression ratioThe 400 abdominal US images were compressed using the

wavelet algorithm. Figure 1 reveals the image size reduction and average compression ratios of US image. Each US original image size had a dimension of 640 × 480 was 307.2 kB. The average

compressed image size (wavelet size) of 71.1 kB, 36.3 kB, 15.5 kB and 8.6 kB at the QFs of 3, 10, 30, and 50, expressed as an average percentage was 23%, 12%, 5% and 2.7%, respectively.

Table 2 shows the summary of compression ratios and wavelet sizes with minimum, maximum, mean and standard deviation of all compressed images. The mean compression ratios were 4.3 ± 0.17, 8.5 ± 0.57, 20 ± 2.29, and 36.6 ± 5.46 at the QFs of 3, 10, 30, and 50, respectively. The standard deviation for average compression increased with increasing compression ratio, while it decreased for wavelet size with increasing compression ratios.

Mean squared error and root mean squared errorThe variation of average MSE and RMSE increased with

decreasing compression ratio as expressed in bpp. Figures 2 and 3 show the plot of MSE and RMSE, respectively. The average MSE value for QF3, 10, 30 and 50 were respectively 0.34 ± 0.17, 1.94 ± 0.05, 11.16 ± 0.44, and 23.38 ± 1.35. Similarly, average RMSE for QF3, 10, 30 and 50 were 0.57 ± 0.09, 1.39 ± 0.02, 3.34 ± 0.08 and 4.84 ± 0.14, respectively.

Signal to noise ratio and peak signal to noise ratioThe traditional manner for comparing the performance of dif-

ferent lossy compression systems is to plot difference or SNR versus bit rate curves and / or PSNR versus bit rate.11 Figures 4 and 5 respectively show scatter plots of the SNR and PSNR bit rate pairs. Both curves increased gradually with increasing compres-sion ratio as defined by bpp. The average SNR value of 40.85 ± 1.36, 33.12 ± 1.33, 25.53 ± 1.17, and 22.31 ± 1.06 at QF of 3, 10, 30, and 50, respectively. Average PSNR was 53 ± 0.97 at QF of 3, 45.27 ± 0.14 at 10, 37.66 ± 0.18 at 30, and 34.36 ± 1.03 at 50.

Acceptable compression ratio and ROC valueThe original images were reviewed by five reviewers with the

average Az being 0.916. For compressed images the average Az

Figure 2 Quantitative error measurement with MSE for US image at QF3, 10, 30, and 50 were 0.34 ± 0.17, 1.94 ± 0.05, 11.16 ± 0.44, and 23.38 ± 1.35, respectively.

Figure 3 Quantitative error measurement with RMSE for US image at QF of 3, 10, 30, and 50 were 0.57± 0.09, 1.39 ± 0.02, 3.34 ± 0.08, and 4.84 ± 0.14, respectively.

Wavelet compression algorithm applied to abdominal ultrasound images

Table 2 Summary of US image compression at four different quality factors

QF Compression ratio Wavelet size

Minimum Maximum Mean ± SD* Minimum Maximum Mean ± SD

3 4 4.84 4.3 ± 0.17 63418 76764 71122 ± 264010 7.33 10.18 8.5 ± 0.57 30184 41926 36340 ± 232530 15.92 26.36 20.0 ± 2.29 11654 19302 15524 ± 1687

50 26.38 52.64 36.6 ± 5.46 5836 11643 8564 ± 1226

Original size 307,200 bytes, *SD Standard deviation

The Radiographer 14

values were 0.915, 0.909, 0.892, and 0.874 at QF3, 10, 30, and 50, respectively. The overall average Az for US compressed images was 0.897. The Az for compressed images was inversely propor-tional to the compression ratio. The higher the compression ratio the lower the Az values. However, the trend was not found in two reviewers at different QFs. An Az of 0.893 at QF50 was found in reviewer B to be higher than that of 0.872 at QF30. Whereas a Az of 0.907 at QF10 was lower than that of 0.911 at QF30 in reviewer D. The combination of QFs and average Az was demonstrated

in Figure 6. The combination for each reviewer’s performance on different QF levels was also demonstrated in Figure 7, the closer the ROC curves to the top left-hand corner of the graph the higher the diagnostic accuracy. According to the results, reviewer A demonstrated the highest accuracy. Interestingly, the Az of original images was not entirely higher than compressed images at QF3 found in reviewers A, C and D. Similarly, some review-ers could not distinguish the minor changes between original and compressed images at QF3. The acceptable compression ratio was

Figure 6 ROC curves for all US images combined for uncompressed images (A), QF3 (B), QF10 (C), QF30 (D), and QF50 (E). The curve tends inversely proportional to the increasing of compression ratios.

Figure 7 ROC curves for all US images combined for five reviewers (A), (B), (C), (D), and (E). The highest average Az was found in reviewer A with average Az of 0.913 whereas the lowest average Az was revealed in reviewer E with mean Az of 0.878. The average of all Az value was 0.897.

Cheng-Hsun Lin, Ming-Che Lee, Su-Feng Pan, Chin-Yuan Lu

Figure 4 Quantitative error measurement with SNR for US image at QF of 3, 10, 30, and 50 were 40.85 ± 1.36, 33.12±1.13, 25.53 ± 1.17, and 22.31 ± 1.06, respectively.

Figure 5 Quantitative error measurement with PSNR for US image at QF 3, 10, 30, and 50 were 53.0 ± 0.97, 45.27 ± 0.14, 37.66 ± 0.18, and 34.36 ± 1.03, respectively.

The Radiographer 15

the ratio of 36.6:1 with the average Az value of 0.874. In order to investigate the different Az values between com-

pressed images and original ones, the significant test for Az values was carried out by using paired-sample Student’s t-test and also tabulated in Table 3. The significance t-test showed no difference between original and compressed images in all QFs but in QF60 at the 0.05 significant level. This indicated that the compressed images were recognised as same as the original ones in terms of ROC analysis. The QF60 corresponds to the compression ratio of 36.6:1 was not seemed as the same group from others.

Discussion

Acceptable compression ratioThe average ROC Az of compressed images at four QFs for

US was 0.987. Swets12 recommended those Az values between 0.5 and 0.7 represent a rather low accuracy – the true positive frac-tion is not much greater than the false positive fraction anywhere along the curve. Values of Az between about 0.7 and 0.9 represent accuracies that are useful for some purposes, and higher values may represent even higher accuracy. The average Az of 0.874 at QF50 corresponding to a compression ratio of 36.6:1 was accept-able for US compressed images. It is clear that readings may, from a statistical point of view, lie outside the significant range but from an image quality point of view be acceptable for clinical interpretation. For US images, the compression ratios of 36.6:1 is acceptable for these types of images

How does this compare with the literature? A compression ratio of 20:1 using Joint Photographic Experts Group (JPEG) format for echocardiographic images was reported by Karson et al.13 Persons et al.14 showed that a compression ratio of approxi-mately 9:1 using a JPEG for both grey scale and colour US could be recommended for clinical use. Wu and Tai15 developed a strategy to raise the compression ratio by exploiting spectra similarity while preserving good decoded quality. They achieved a compression ratio of 14.5:1 for US image at 8 bpp pixel depth. Very similar work was carried out by Puniene et al.16 They applied wavelet technique to compress ultrasound images and reported that the image could be interpreted at a compression ratio of 35:1. Unfortunately since some of these authors did not mention image size and pixel depth, it is difficult to make direct comparisons. Chiu et al.17 compressed US images using the modified wavelet technique. They used a US image size of 640 × 480 × 8 as has been used in this study. They reported that a compression ratio of 20:1 could be used. Wu18 also reported a compression ratio of 20:1 with the use of an adaptive sampling algorithm for US images at 8 bpp pixel depth.

According to previous work on US images using either JPEG, wavelet or modified algorithms, the compression ratios were between 9:1–35:1. For the US image in this study, the accepted compression ratio of 36.6:1 on 640 × 480 × 8 image size is better,

from an image quality point of view, than those suggested in the literature.

Mean squared error and root mean squared error The most basic measurement of compression fidelity is

computation of the mean pixel error (MPE) for the compressed image.4 This is both familiar and simple, but it fails to measure local degradations that can lead to loss of important information.4 Normalised mean-square error (NMSE) is another quantitative method to measure the differences between the original and the reconstructed image and is only recognised as a standard mea-surement for compressed image by the 1993 PACS Guidance Document from US FDA.19 This organisation allowed medical device manufacturers to report the NMSE as a measure of image degradation when lossy compression techniques are used.1 NMSE is a global measurement of the quality of reconstructed images; it does not provide information on the local measurement (e.g., spatial location or spatial frequency). It is obvious that the NMSE is a function of the compression ratio. A high compression ratio will yield a high NMSE value.19

The key issue of this study is not so much whether lossy com-pression should be used, but rather how many bits are needed to ensure sufficient accuracy for a particular use. To answer this question one needs acceptable quantitative measures of image quality and protocols for careful study of the trade-off of quality against bit rate in specific applications. The most direct method of evaluating image quality, in the sense of fidelity to the original object. This involves computing the average of the square of the difference between the output image of the system and the image that a perfect system would have provided of the same object over the image format. This represents the degree of difference between the compressed image and the original. The RMSE is the square root of the average squared difference between every pixel in the compressed image and its counterpart in the original image. The higher the RMSE value the poorer the image quality.20 The average RMSE was 0.57 at ratio of 4.3, 1.39 at 8.5, 3.34 at 20 and 4.84 at 36.6 and corresponding to the bit rates of 1.85, 0.94, 0.40, and 0.22, respectively. Given that each pixel contains 8 bits of data, the maximum average RMSE at a ratio of 36.6:1 corre-sponds to a difference of 4.8 of 256 possible values (or 1.87 %). The RMSE values decreased with decreasing compression ratios and increasing bit rates.

Signal to noise ratio and peak signal to noise ratioOther methods of measuring the fidelity of the reconstructed

images to the original are to examine the SNR or PSNR. The PSNR is very similar to the NMSE and based on the RMSE of the reconstructed image. PSNR has been accepted as a widely used quality measurement in the field of image compression.15,18 A common alternative normalisation when input is itself an r-bit discrete variable is to replace the variance or energy by the maxi-mum input symbol energy (2r-1)2, for example, an 8 bpp image type, (28-1)2 is equal to 2552. Therefore, the PSNR value is always higher than SNR value. The average PSNR was 53 at a ratio of 4.3:1, 45.27 at 8.5:1, 37.66 at 20:1 and 34.36 at 36.6:1 corre-sponding to the bit rates of 1.85, 0.94, 0.40, and 0.22, respectively. The PSNR values decreased with increased compression ratios and decreased of the bit rates. The lower PSNR value represents poorer compressed image quality.

Basically, higher PSNR values represent higher image qual-ity. The human visual ability cannot differentiate between origi-nal and compressed images if the PSNR value is over 30 dB.21

Wavelet compression algorithm applied to abdominal ultrasound images

Table 3 Paired-sample Student's t test for Az values of original and compressed US images

Images95% confidence

intervalt Sig. (2-tailed)

Original v QF3 .021–.024 .120 .910

Original v QF10 .017–.032 .829 .454

Original v QF30 .008–.057 2.085 .105

Original v QF50 .018–.067 4.830 .008*

Asignificant different at 0.05 level

The Radiographer 16

Bangerter and Sabataitis22 reported that a PSNR value of 30–31 dB is suitable to preserve diagnostic utility and this value is suf-ficient for current applications such as wall motion assessment for cardiological imaging. But in a real life situation, the higher PSNR value does not always denote the best image quality. For instance, when adjusting a few pixels, as when changing from black and white to colour, the PSNR value remains high due to the fact that only a few pixels have been altered. This change is identified easily by human visual perception. This is the reason why an objective measurement can not be implemented alone as a measurement of image quality evaluation. Images must also be subjectively analysed to give a full appreciation.

ROC analysis In this study, a compression ratio of 4.3:1 has the highest

average Az of 0.915, and PSNR value of 53.0 ± 0.97 and the lowest RMSE value of 0.57 ± 0.09. Aside from the compression algorithms, image types and sizes chosen, some studies reported acceptable compression ratios with ROC analysis Az values under 0.8. For example, an Az value of 0.7232 at a compression ratio of 40:1 for the detection of scaphoid fracture of the wrist was reported.9 Aiazzi et al.23 proposed a compression ratio of 25:1 for the detection of nodules in lung images with a ROC Az of 0.778. Zalis et al.24 reported an Az of 0.69 for a ratio of 20:1 for CT colonography image compression. Most literature indicates that an Az value over 0.8 could be more confidently recommended for clinical use. Goldberg et al.25 reported work on images of hepatic lesion using a compression ratio of 20:1 with Az of 0.968. A simi-lar work was reported by Lin et al.26 that abdominal MR images could be compressed to a ratio of 18.3:1 without sacrificing diagnostic value at the Az of 0.876. Mori and Nakata27 proposed a ratio of 25:1 with Az of 0.883 for chest image compression. Aberle et al.28 and Beall et al.11 conducted similar works to detect lung nodules in compressed chest images and reported ratios of 20:1 and 10:1 at Az of 0.882 and 0.926, respectively.

Reviewer workload and the length of experience reading US images could both affect the outcome of a ROC analysis. Radiologists are often extremely busy performing patients’ exam-inations, image interpretation and meeting for case conferences. The evaluation work was interrupted sometimes when physicians from other departments needed to consult or to arrange examina-tions for urgent cases. The radiologists can only use ROC analysis when they were off duty and this was usually in the evening time as they were tired after a hard day’s work. This may well have affected their interpretive judgments.

On the other hand, length of experience in reading images is another factor influencing ROC outcome. In this study, the lon-gest experience was 25 years (reviewer B) and the shortest was 11 years (reviewer C). The average Az for reviewer with 25 experi-ence of reading US image was 0.895, the highest average Az was 0.913 in reviewer A, who had 15 years of experience. There was no statistical difference between reviewer A and B. This implies that the two radiologists concerned had equal performance in reading the US images. The number of years of experience which related most closely to the average Az value referred to radiolo-gists A and D who had 15 and 16 years experience, respectively.

Compressed image v. image qualityIn general, compression of medical images reduces the image

quality and may result in poorer image perception. This is possi-bly, but not definitely, true. Image processing during compression could increase contrast and lesion detection by eliminating visual

effect of high frequency noise, particularly at lower compression ratios.25 For example, Baudin et al.9 carried out compression on wrist radiographs. The Az value after reconstruction was higher than that computed on the evaluation of the diagnostic quality of the original images. In wavelet compression, information is lost during the quantisation step, as the exact values of the coefficients are replaced by approximate values. At lower compression ratio, the effect of this approximation is very similar to that of standard statistical wavelet de-noising algorithms.29 Because the human visual system is less sensitive to high frequencies, the errors in the image compression at higher frequencies will have less effect on the perception of the image. However, there is a limit to this. If carried too far, important details in the image begin to disap-pear. For example, the detail of trabecular bone structure could be missed.30

The wavelet compression increases the reduction of image SNR at sufficiently low compression ratio, and therefore the result is to make the image more appealing to the reviewers’ eye without degrading diagnostically relevant details such as tissue texture or anatomic structure information.

ConclusionThe objective of this thesis was to investigate the acceptable

compression ratio, with the wavelet algorithm, for 640 × 480 × 8 US images using objective and subjective measurements. The acceptable compression ratio for US was 36.6:1. The compressed image size was only 2.7% for US original.

The lossy compressed image has to be examined by both objective and subjective measurements. Four parameters were introduced to assess the compressed images against the uncom-pressed originals. The lower values of MSE or RMSE mean higher image quality. The lower values of SNR or PSNR represent poorer image quality. The higher the bit rate per pixel, the lower the MSE or RMSE values and the higher the SNR or PSNR values but the lower the compression ratio.

ROC analysis is widely accepted as method of evaluating compression of medical images. The method is time consuming, labour intensive and tedious, comparing image quality on the basis of whether pathology on the original image can be detected with more or less confidence on the compressed image. ROC analysis can be influenced by several factors. These are ‘missing’ lesion, original image pathology being confirmed by other means or not, the radiologist’s busy schedule, number of images, number of reviewers, image display type and viewing conditions.

The effect of length of experience that the reviewer had had of reading US images is also discussed. There was no statistical difference in the readings between those radiologists with the longest and the shortest length of experience in reading the US images.

AcknowledgementsThe work took place under a grant from the Ulster University

at Jordanstown, United Kingdom. The ROC analysis was carried out by Dr YJ Ho, Dr CH Hsu, Dr JL Chuang, Dr HH Wu, Dr KL Wang, Dr YS Tyne, Dr TW Chai, Dr SW Hung, Dr Joseph Huang and Dr WH Chen.

References1 Savcenko V, Erickson BJ, Palisson PM, Persons KR, Manduca A, Hartman

TE. Detection of subtle abnormalities on chest radiographs after irreversible compression. Radiology 1998; 206: 609–16.

2 Siegel E, Reiner B. Conventional x-ray still the champ, but not forever. Diagnostic Imaging 2001; 23 (6): 29–33, 79.

Cheng-Hsun Lin, Ming-Che Lee, Su-Feng Pan, Chin-Yuan Lu

The Radiographer 17

3 Schomer DF, Elekes ÁA, Hazel JD, Huffman JC, Thompson SK, Chui CK. Introduction to wavelet-based compression of medical images: Radiographics 1998; 18: 469–81.

4 Erickson BJ, Manduca A, Palisson P, Persons KR, Earnest IV F, Savcenko V. Wavelet compression of medical images. Radiology 1998; 206: 599–607.

5 Cosman PC, Grey RM, Olshen RA. Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic quality: Proc IEEE 1994; 82 (6) 919–32.

6 Kofidis E, Kolokotronis N, Vassilarakou A, Theodoridis S, Cavouras D. In Marsh A. et al. Eds. Medical image compression: Advanced Infrastructures for Future Healthcare. IOS Press; 2000.

7 Swets JA. ROC analysis applied to the evaluation of medical imaging tech-niques. Invest Radiol 1979; 14 (2): 109–21.

8 Hanley JA, McNeil BJ. The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve. Radiology 1982; 143 (1): 29–36.

9 Baudin O, Baskurt A, Moll T, Prost R, Revel D, Ottes F. ROC assessment of compressed wrist radiographs. Eur J Radiol 1996; 22: 228–31.

10 Xu Z, Yamauchi K, Ikeda M, Yang S, Hasegawa Y. Evaluation of a PC-based teleconferencing system for reading chest radiographs. J Telemed Telecare 1999; 5: 122–25.

11 Beall DP, Shelton PD, Kinsey TV, Horton MC, Fortman BJ, Schenbach S. Image compression and chest radiograph interpretation: image perception comparison between uncompressed chest radiographs and chest radiographs stored using 10:1 JPEG compression. J Digit Imaging 2000; 13 (2) suppl 1: 33–8.

12 Swets JA. Measuring the accuracy of diagnostic systems. In Swets JA Ed. Signal detection theory and ROC analysis in psychology and diagnostics: col-lected papers, Fig 1,104, Ch4 1996.

13 Karson TH, Chandra S, Morehead AJ, Stewart WJ, Nissen SE, Thomas JD. JPEG compression of digital echocardiographic image quality. Impact on image quality. J Am Soc Echocardiogr 1995; 8: 306–18.

14 Persons KR, Hangiandreou NJ, Charboneau NT, Charboneau JW, James EM, Douglas BR, Salmon AP, Knudsen JM, Erickson BJ. Clinical evaluation of irreversible compression of ultrasound images using the JPEG algorithm at approximately 9:1. J Digit Imaging 2000; 13 (2, suppl 1): 191–92.

15 Wu YG, Tai SC. Medical image compression by discrete cosine transform spectral similarity strategy. IEEE Trans Inform Tech Biomed 2001; 5 (3): 236–43.

Wavelet compression algorithm applied to abdominal ultrasound images

16 Puniene J, Punys V, Punys J. Ultrasound and angio image compression by cosine and wavelet transforms. Int J of Med Informatics 2001; 64: 473–81.

17 Chiu E, Vaisey J, Atkin MS. wavelet-based space-frequency compression of ultrasound images. IEEE Trans Inform Tech Biomed 2001; 5 (4): 300–10.

18 Wu YG. Medical image compression by sampling DCT coefficients. IEEE Trans Inform Tech Biomed 2002; 6 (1): 86–94.

19. Huang HK: PACS-basic principle and applications. Wley-Liss Press; 1999

20 Goldberg MA, Pivovarov M, Mayo-Smith WW, Bhalla MP, Blickman JG, Bramson RT. Application of wavelet compression to digitised radiographs. AJR 1994; 163: 463–68.

21 Parker JR. Algorithms for image processing and computer vision. John Wiley & Sons Press; 1996.

22 Bangerter NK Sabataitis JC. Compression of real-time cardiac MRI video sequence: Available at http://www.standford.edu / class / ee386b / presenta-tions / html

23 Aiazzi B, Alparone L, Baronti S, Chirò G, Lotti F, Moroni M. A pyramid-based error-bounded encoder: an evaluation on x-ray chest images. Signal Processing 1997; 59: 173–87.

24 Zalis ME, Hahn PF, Arellano RS, Gazelle GS, Mueller PR. CT colonography with teleradiology: effect of lossy wavelet compression on polyp detection-initial observation. Radiology 2001; 220: 387–92.

25 Goldberg MA, Gazelle GS, Boland GW, Hahn PF, Mayo-Smith WW, Pivovarov M. Focal hepatic lesions: effect of three-dimensional wavelet com-pression on detection at CT. Radiology 1997; 202: 159–65.

26 Lin ChengHsun, Catherine O’Neill, KwoWhei Lee, MingChe Lee. Wavelet Lossy Compression on Magnetic Resonance Images with Subjective and Objective Evaluations. Chunghua J Med 2005; 10: 145–57.

27 Mori T, Nakata H. Irreversible data compression in chest imaging using com-puted radiography: an evaluation. J Thoracic Imaging 1994; 9: 23–30.

28 Aberle DR, Gleeson F, Sayre JW, Brown K, Batra P, Young DA. The effect of irreversible image compression on diagnostic accuracy in thoracic imaging. Invest Radial 1993; 28 (5): 398–403.

29 Donoho DI, Johnstone IM. Ideal denoising in an orthonormal basis chosen from a library of bases: Comptes Rendus Acad Sci 1994; 319: 1317–22.

30 Goldburgh M. Image compression. In Filmless radiology: Edited by Siegel EL, Kolodner RM. Springer Press; 2001.