Evaluation of JPEG and Wavelet Compression of Body CT Images for Direct Digital Teleradiologic Transmission

Kalyanpur, Arjun; Neklesa, Vladimir P.; Taylor, Caroline; Daftary, Aditya; Brink, James A.

doi:10.1148/radiology.217.3.r00nv22772

Cited by 63 publications

(41 citation statements)

References 23 publications

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“…The mean square error (MSE) is used to calculate the PSNR that is given in the equation 4. The coding results were compared with those obtained applying the original SPIHT algorithm and the standards JPEG [39] and JPEG2000 [40] on the whole image. The SPIHT algorithm and JPEG2000 had been utilized with the irreversible 9/7 biorthogonal filters [41].…”

Section: Resultsmentioning

confidence: 99%

Adaptive Medical Image Compression Based on Lossy and Lossless Embedded Zerotree Methods

Elhannachi¹,

Benamrane²,

Abdelmalik³

2017

J Inf Process Syst

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Since the progress of digital medical imaging techniques, it has been needed to compress the variety of medical images. In medical imaging, reversible compression of image's region of interest (ROI) which is diagnostically relevant is considered essential. Then, improving the global compression rate of the image can also be obtained by separately coding the ROI part and the remaining image (called background). For this purpose, the present work proposes an efficient reversible discrete cosine transform (RDCT) based embedded image coder designed for lossless ROI coding in very high compression ratio. Motivated by the wavelet structure of DCT, the proposed rearranged structure is well coupled with a lossless embedded zerotree wavelet coder (LEZW), while the background is highly compressed using the set partitioning in hierarchical trees (SPIHT) technique. Results coding shows that the performance of the proposed new coder is much superior to that of various state-of-art still image compression methods.

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Section: Resultsmentioning

confidence: 99%

Adaptive Medical Image Compression Based on Lossy and Lossless Embedded Zerotree Methods

Elhannachi¹,

Benamrane²,

Abdelmalik³

2017

J Inf Process Syst

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“…Visualization systems cannot display such a wide range, but these images can be dynamically adapted using VOI transformations [21] that can be manipulated by the clinician in order to explore a different grayscale window. Papers such as [4,11,22,23] on diagnostic image quality have used fixed values of interest for their evaluation. However, diagnosis may require unpredictable settings [3] and a narrower window can make distortions more apparent, while a wide widow can mask them.…”

Section: Previous Workmentioning

confidence: 99%

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

Pambrun

Noumeir

2015

J Digit Imaging

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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.

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“…This area is a smooth region and the results correspond well to that from a previous study. A report [16] evaluated image quality using two observers after image compression and transmission. They mentioned that with wavelet compression, the high-contrast region did not decrease at lossy compression for CR lower than 20.…”

Section: Grc Applicationmentioning

confidence: 99%

A Novel Medical Image Quality Index

Lin¹,

Lin

Feng

et al. 2010

J Digit Imaging

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A novel medical image quality index using grey relational coefficient calculation is proposed in this study. Three medical modalities, DR, CT and MRI, using 30 or 60 images with a total of 120 images used for experimentation. These images were first compressed at ten different compression ratios (10 ∼ 100) using a medical image compression algorithm named JJ2000. Following that, the quality of the reconstructed images was evaluated using the grey relational coefficient calculation. The results were shown consistent with popular objective quality metrics. The impact of different image aspects on four grey relational coefficient methods were further tested. The results showed that these grey relational coefficients have different slopes but very high consistency for various image areas. Nagai's grey relational coefficient was chosen in this study because of higher calculation speed and sensitivity. A comparison was also made between this method and other windows-based objective metrics for various window sizes. Studies found that the grey relational coefficient results are less sensitive to window size changes. The performance of this index is better than some windows-based objective metrics and can be used as an image quality index.

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Evaluation of JPEG and Wavelet Compression of Body CT Images for Direct Digital Teleradiologic Transmission

Abstract: Lossy 10:1 compression is suitable for on-call electronic transmission of body CT images as long as original images are subsequently reviewed.

Cited by 63 publications

References 23 publications

Adaptive Medical Image Compression Based on Lossy and Lossless Embedded Zerotree Methods

Adaptive Medical Image Compression Based on Lossy and Lossless Embedded Zerotree Methods

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

A Novel Medical Image Quality Index

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