Improved compressibility of multislice CT datasets using 3D JPEG2000 compression

Siddiqui, Khan M.; Siegel, Eliot L.; Reiner, Bruce I.; Crave, Olivier; Johnson, Jeffrey P.; Wu, Zhenyu; Bilgin, Ali; Marcellin, Michael W.; Nadar, Mariappan S.

doi:10.1016/j.ics.2004.03.153

Cited by 6 publications

(14 citation statements)

References 21 publications

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“…They showed, with radiologist observations, that thick slices (5mm) could be slightly more compressed that thin slices (0.67mm) to achieve the visually lossless threshold. Similar results were observed in two studies, [26] and [27], which used 3D JPEG200 compression in an effort to further increase compression ratios of CT scans. …”

Section: Related Worksupporting

confidence: 85%

Perceptual quantitative quality assessment of JPEG2000 compressed ct images with various slice thicknesses

Pambrun

Noumeir

2011

2011 IEEE International Conference on Multimedia and Expo

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Modern medical equipments produce huge amounts of data that need to be archived for long periods and efficiently transferred over networks. Data compression plays an essential role in reducing the amount of medical imaging data. Medical images can usually be compressed by a factor of three before any degradation appears. Higher compression levels are desirable but can only be achieved with lossy compression, thus scarifying image quality. The diagnosis value of compressed medical images has been studied and recommendations about maximum acceptable compression ratios have been provided based on qualitative visual analysis. It has been suggested, without further investigation, that CT images, with thicknesses below five mm, cannot undergo lossy compression if diagnostic value needed to be preserved. In this paper, we present an objective quantitative quality assessment of compressed CT images using Visual Signal to Noise Ratio. Our results show that visual fidelity can be significantly affected by two factors, slice thickness and exposure time, for images compressed using the same compression ratio.

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Section: Related Worksupporting

confidence: 85%

Perceptual quantitative quality assessment of JPEG2000 compressed ct images with various slice thicknesses

Pambrun

Noumeir

2011

2011 IEEE International Conference on Multimedia and Expo

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“…This can be directly applied to improve the acquisition in noisy conditions, case in which artifacts from any origin may be detected and removed at a low resolution so that the full reconstruction step can be avoided [24][25][26]. This is of particular interest in the group of pathologies associated with organs in constant movement, for example the lung or the heart.…”

Section: Discussionmentioning

confidence: 99%

“…This type of technology would allow us to acquire low-resolution prostate versions which can be mixed with the first image so that prostate changes can be better estimated and the radiation focused on the organ. Finally, mobile devices show a continuous improvement of the different displays and telemedicine applications are becoming more and more available, case in which progressive transmission in low-band channels is possible [25,26].…”

Section: Discussionmentioning

confidence: 99%

Multiresolution Reconstruction for Cone-Beam Tomography from Raw Data Projections Using 3D Ridgelets

2011

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This paper presents a novel method which reconstructs any desired 3D image resolution from raw cone-beam CT data. X-ray attenuation through the object is approximated using ridgelet basis functions which allow us to have multiresolution representation levels. Since the Radon data have preferential orientations by nature, a spherical wavelet transform is used to compute the ridgelet coefficients from the Radon shell data. The whole method uses the classical Grangeat's relation for computing derivatives of the Radon data which are then integrated and projected to a spherical wavelet representation and back-reconstructed using a modified version of the well known back-projection algorithm. Unlike previous reconstruction methods, this proposal uses a multiscale representation of the Radon data and therefore allows fast display of low-resolution data level.

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“…21 One of the most popular compression algorithms is Joint Photographic Experts Group 2000 ͑JPEG2000͒, [3][4][5][6][7][8][9][10][11][12][13][14][15][16]19 which compresses an image by exploiting the data redundancy within the image ͑or intraslice correlation͒. Recently, JPEG2000 3D ͑part 2͒ has been advocated as being more effective than JPEG2000 in compressing CT image data sets 20,[22][23][24][25] by further compressing the data sets in the third dimension through exploitation of the data redundancy between adjacent images ͑or interslice correlation͒. JPEG2000 and JPEG2000 3D have been adopted in the Digital Imaging and Communications in Medicine ͑DICOM͒ standard.…”

Section: Introductionmentioning

confidence: 99%

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

Lee

Kim

et al. 2010

Medical Physics

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Improved compressibility of multislice CT datasets using 3D JPEG2000 compression

Cited by 6 publications

References 21 publications

Perceptual quantitative quality assessment of JPEG2000 compressed ct images with various slice thicknesses

Perceptual quantitative quality assessment of JPEG2000 compressed ct images with various slice thicknesses

Multiresolution Reconstruction for Cone-Beam Tomography from Raw Data Projections Using 3D Ridgelets

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

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