Ultrasound grayscale image compression with JPEG and wavelet techniques

Persons, Kenneth R.; Palisson, Patrice M.; Manduca, Armando; Charboneau, William J.; James, E M; Charboneau, Nicholas T.; Hangiandreou, Nicholas J.; Erickson, Bradley J.

doi:10.1007/bf03168337

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…We compared several sets of filters and finally retained the 10/6 filter pair described in [12, p. 127]. The corresponding symmetric components in (1) are (2) (3)…”

Section: Choice Of the Wavelet Filtersmentioning

confidence: 99%

Four-dimensional wavelet compression of arbitrarily sized echocardiographic data

Zeng

Jansen

Marsch

et al. 2002

IEEE Trans. Med. Imaging

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Wavelet-based methods have become most popular for the compression of two-dimensional medical images and sequences. The standard implementations consider data sizes that are powers of two. There is also a large body of literature treating issues such as the choice of the "optimal" wavelets and the performance comparison of competing algorithms. With the advent of telemedicine, there is a strong incentive to extend these techniques to higher dimensional data such as dynamic three-dimensional (3-D) echocardiography [four-dimensional (4-D) datasets]. One of the practical difficulties is that the size of this data is often not a multiple of a power of two, which can lead to increased computational complexity and impaired compression power. Our contribution in this paper is to present a genuine 4-D extension of the well-known zerotree algorithm for arbitrarily sized data. The key component of our method is a one-dimensional wavelet algorithm that can handle arbitrarily sized input signals. The method uses a pair of symmetric/antisymmetric wavelets (10/6) together with some appropriate midpoint symmetry boundary conditions that reduce border artifacts. The zerotree structure is also adapted so that it can accommodate noneven data splitting. We have applied our method to the compression of real 3-D dynamic sequences from clinical cardiac ultrasound examinations. Our new algorithm compares very favorably with other more ad hoc adaptations (image extension and tiling) of the standard powers-of-two methods, in terms of both compression performance and computational cost. It is vastly superior to slice-by-slice wavelet encoding. This was seen not only in numerical image quality parameters but also in expert ratings, where significant improvement using the new approach could be documented. Our validation experiments show that one can safely compress 4-D data sets at ratios of 128:1 without compromising the diagnostic value of the images. We also display some more extreme compression results at ratios of 2000:1 where some key diagnostically relevant key features are preserved.

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“…We compared several sets of filters and finally retained the 10/6 filter pair described in [12, p. 127]. The corresponding symmetric components in (1) are (2) (3)…”

Section: Choice Of the Wavelet Filtersmentioning

confidence: 99%

Four-dimensional wavelet compression of arbitrarily sized echocardiographic data

Zeng

Jansen

Marsch

et al. 2002

IEEE Trans. Med. Imaging

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“…Compression of medical images is vital in achieving a low bit rate in the representation of radiology images in order to reduce the data volume, without loss in diagnostic information [12,13]. Persons et al discussed diagnostic accuracy and reported that reconstructed medical images with a compression ratio of 9:1 do not result in visual degradation [14]. Li et al investigated the effect of JPEG and wavelet compression algorithm on medical images and concluded that compression ratio up to 10:1 is acceptable [15].…”

Section: Introductionmentioning

confidence: 99%

Medical Image Compression using Adaptive Subband Threshold

Vidhya¹

2016

Journal of Electrical Engineering and Technology

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-Medical imaging techniques such as Magnetic Resonance Imaging (MRI), ComputedTomography (CT) and Ultrasound (US) produce a large amount of digital medical images. Hence, compression of digital images becomes essential and is very much desired in medical applications to solve both storage and transmission problems. But at the same time, an efficient image compression scheme that reduces the size of medical images without sacrificing diagnostic information is required. This paper proposes a novel threshold-based medical image compression algorithm to reduce the size of the medical image without degradation in the diagnostic information. This algorithm discusses a novel type of thresholding to maximize Compression Ratio (CR) without sacrificing diagnostic information. The compression algorithm is designed to get image with high optimum compression efficiency and also with high fidelity, especially for Peak Signal to Noise Ratio (PSNR) greater than or equal to 36 dB. This value of PSNR is chosen because it has been suggested by previous researchers that medical images, if have PSNR from 30 dB to 50 dB, will retain diagnostic information. The compression algorithm utilizes one-level wavelet decomposition with threshold-based coefficient selection.

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“…In a very recent follow-up study on the compression of ultrasound images, Persons et al [10] compared JPEG-and SPIHT-compressed ultrasound images at low compression rates and concluded that for both, JPEG and SPIHT produced diagnostically acceptable images at a 10 : 1 compression ratio. However, at compression rates above 12 : 1, the SPIHT approach developed artifacts, such as lack of low-contrast detail, which impacted the diagnostic acceptability.…”

mentioning

confidence: 99%

Wavelet-based space-frequency compression of ultrasound images

Chiu

Vaisey

Atkins

2001

IEEE Trans. Inform. Technol. Biomed.

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Abstract-This paper describes the compression of grayscale medical ultrasound images using a recent compression technique, i.e., space-frequency segmentation (SFS). This method finds the rate-distortion optimal representation of an image from a large set of possible space-frequency partitions and quantizer combinations and is especially effective when the images to code are statistically inhomogeneous, which is the case for medical ultrasound images. We implemented a compression application based on this method and tested the algorithm on representative ultrasound images. The result is an effective technique that performs better than a leading wavelet-transform coding algorithm, i.e., set partitioning in hierarchical trees (SPIHT), using standard objective distortion measures. To determine the subjective qualitative performance, an expert viewer study was run by presenting ultrasound radiologists with images compressed using both SFS and SPIHT. The results confirmed the objective performance rankings. Finally, the performance sensitivity of the space-frequency codec is shown with respect to several parameters, and the characteristic space-frequency partitions found for ultrasound images are discussed.Index Terms-Space-frequency segmentation, ultrasound image compression, wavelet packets.

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Ultrasound grayscale image compression with JPEG and wavelet techniques

Cited by 11 publications

References 6 publications

Four-dimensional wavelet compression of arbitrarily sized echocardiographic data

Four-dimensional wavelet compression of arbitrarily sized echocardiographic data

Medical Image Compression using Adaptive Subband Threshold

Wavelet-based space-frequency compression of ultrasound images

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