Wavelet-based space-frequency compression of ultrasound images

Chiu, Eddy; Vaisey, J.; Atkins, M. Stella

doi:10.1109/4233.966105

Cited by 39 publications

(14 citation statements)

References 22 publications

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“…One study concluded that wavelet-based methods such as SPIHT are subjectively superior to JPEG compressed at a moderately high bit-rate (Chiu et al, 2001). However, SPIHT developed ringing artifacts at compression rates above 12:1, impacting diagnostic acceptability.…”

Section: Thresholdingmentioning

confidence: 99%

Medical images compression for remote diagnosis using modified SPIHT data organization and fidelity enhancement filter

Chen

2007

Int J Imaging Syst Tech

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In this work, an 8 3 8 Discrete Cosine Transform (DCT) approach is adopted to perform DCT shrinkage, followed by modified set partitioning in hierarchical trees (SPIHT) data organization and fidelity enhancement filter for reducing the memory required to store a remote diagnosis and rapidly transmit it. The DCT shrinkage has the ability to retain the detailed characteristics of an image. By means of a simple transformation to gather the DCT spectrum data with the same frequency domain, the DCT shrinkage exploits all the characteristics of individual blocks to a global framework. In this scheme, insignificant DCT coefficients that correspond to the same spatial location in the high-frequency sub-bands can be used to reduce the redundancy by a combined function proposed in association with the modified SPIHT. Meanwhile, quad-tree decomposition and a set of morphological filters for reducing the artifacts are presented. This set of filters employs 8 predefined morphological operations, namely 4 structuring elements (SE), each of which includes both dilation and erosion operations. The voting strategy is used to select the most suitable morphological filter for each block. Simulation results show that the image compression reduced the computational complexity to only a half of the wavelet based sub-band decomposition and improved the quality of the reconstructed medical image in terms of both the peak signal-to-noise ratio (PSNR) and the perceptual results close to JPEG2000 and the original SPIHT at the same bit rate.

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Section: Thresholdingmentioning

confidence: 99%

Medical images compression for remote diagnosis using modified SPIHT data organization and fidelity enhancement filter

Chen

2007

Int J Imaging Syst Tech

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“…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.…”

Section: Acceptable Compression Ratiomentioning

confidence: 99%

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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“…Recently, the subband or wavelet coding has proven to be a very useful framework for loss compression of medical images (Gupta, N., Swamy, M.N.S., & Plotkin, E., 2005;Chiu, E., Vaisey, J., & Atkins, S. 2001;Li, X., Hu, G., & Gao, S., 1999).…”

Section: Introductionmentioning

confidence: 99%

Medical Images Compression Using Modified SPIHT Algorithm and Multiwavelets Transformation

Al-Sammaraie¹

2011

CIS

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Recently, the wavelet transform has emerged as a cutting edge technology within the field of image compression research. Wavelet methods involve overlapping transforms with varying-length basis functions. This overlapping nature of the transform alleviates blocking artifacts, while the multi-resolution character of the wavelet decomposition leads to superior energy compaction and perceptual quality of the decompressed image. Over the past decade, the success of wavelets in solving many different problems has contributed to its unprecedented popularity. Due to implementation constraints scalar wavelets do not posses all the properties which are needed for better performance in compression. New class of wavelets called 'Multiwavelets' which posses more than one scaling filters overcomes this problem. The objective of this paper is to develop an efficient compression scheme and to obtain better quality and higher compression ratio through multiwavelet transform and embedded coding of multiwavelet coefficients through Set Partitioning In Hierarchical Trees algorithm (SPIHT) algorithm. A comparison of the best known multiwavelets is made to the best known scalar wavelets. An adaptive image-coding algorithm for compression of medical images in the wavelet domain is presented. Both quantitative and qualitative measures of performance are examined for Medical images. The objective (based on PSNR) and subjective (perceived image quality) results of these simulations are presented.

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Wavelet-based space-frequency compression of ultrasound images

Cited by 39 publications

References 22 publications

Medical images compression for remote diagnosis using modified SPIHT data organization and fidelity enhancement filter

Medical images compression for remote diagnosis using modified SPIHT data organization and fidelity enhancement filter

Wavelet compression algorithm applied to abdominal ultrasound images

Medical Images Compression Using Modified SPIHT Algorithm and Multiwavelets Transformation

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