Application of wavelet compression to digitized radiographs.

Goldberg, Mark A.; Pivovarov, M. L.; Mayo-Smith, William W.; Bhalla, Mahadev; Blickman, Johan G.; Bramson, Robert T.; Boland, Giles W.; Llewellyn, Henry; Halpern, E J

doi:10.2214/ajr.163.2.8037051

Cited by 89 publications

(27 citation statements)

References 10 publications

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“…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 corresponds to a difference of 4.8 of 256 possible values (or 1.87 %).…”

Section: Mean Squared Error and Root Mean Squared Errormentioning

confidence: 88%

Wavelet compression algorithm applied to abdominal ultrasound images

Lin

Lee²,

Pan³

et al. 2006

Radiographer

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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

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Section: Mean Squared Error and Root Mean Squared Errormentioning

confidence: 88%

Wavelet compression algorithm applied to abdominal ultrasound images

Lin

Lee²,

Pan³

et al. 2006

Radiographer

View full text Add to dashboard Cite

show abstract

“…For pixels in the interior of a class, the default orders of the classes. The result is refined iteratively to enhance the spatial connectivity of each class by using the algoof the AR and ARMA models are (3) and (3,10). For the border pixels, the default orders are (2) and (2,5).…”

Section: Fig 2 Illustration Of the Four Neighborhood Patterns Testedmentioning

confidence: 99%

“…For MR images, it has been shown coefficients in a training stage. In order to deal with the nonstationarity of the underlying signal, various adaptive that compression of 7:1 to 10:1 can be tolerated before there is a perceptible loss of quality [10,14], and up to DPCM techniques have been developed, which can in general be classified into two categories. The forward adaptive 20:1 before there is a loss of diagnostic accuracy [14].…”

Section: Introductionmentioning

confidence: 99%

Segmented Adaptive DPCM for Lossy Compression of Multispectral MR Images

Wang²,

Cahill

1997

Journal of Visual Communication and Image Representation

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“…Previous studies have indicated various acceptable compression ratios (the ratio of compressed dataset size: original), some up to 30:1 [2]. One study found that compressing chest X-rays to 40:1 did not significantly affect diagnostic accuracy [3].…”

Section: Introductionmentioning

confidence: 99%

Extreme Compression for Extreme Conditions: Pilot Study to Identify Optimal Compression of CT Images Using MPEG-4 Video Compression

et al. 2012

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This study aims to evaluate the utility of compressed computed tomography (CT) studies (to expedite transmission) using Motion Pictures Experts Group, Layer 4 (MPEG-4) movie formatting in combat hospitals when guiding major treatment regimens. This retrospective analysis was approved by Walter Reed Army Medical Center institutional review board with a waiver for the informed consent requirement. Twenty-five CT chest, abdomen, and pelvis exams were converted from Digital Imaging and Communications in Medicine to MPEG-4 movie format at various compression ratios. Three board-certified radiologists reviewed various levels of compression on emergent CT findings on 25 combat casualties and compared with the interpretation of the original series. A Universal Trauma Window was selected at −200 HU level and 1,500 HU width, then compressed at three lossy levels. Sensitivities and specificities for each reviewer were calculated along with 95 % confidence intervals using the method of general estimating equations. The compression ratios compared were 171:1, 86:1, and 41:1 with combined sensitivities of 90 % (95 % confidence interval, 79-95), 94 % (87-97), and 100 % (93-100), respectively. Combined specificities were 100 % (85-100), 100 % (85-100), and 96 % (78-99), respectively. The introduction of CT in combat hospitals with increasing detectors and image data in recent military operations has increased the need for effective teleradiology; mandating compression technology. Image compression is currently used to transmit images from combat hospital to tertiary care centers with subspecialists and our study demonstrates MPEG-4 technology as a reasonable means of achieving such compression.

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Application of wavelet compression to digitized radiographs.

Abstract: OBJECTIVE.Image data compression is an enabling tech-

Cited by 89 publications

References 10 publications

Wavelet compression algorithm applied to abdominal ultrasound images

Wavelet compression algorithm applied to abdominal ultrasound images

Segmented Adaptive DPCM for Lossy Compression of Multispectral MR Images

Extreme Compression for Extreme Conditions: Pilot Study to Identify Optimal Compression of CT Images Using MPEG-4 Video Compression

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