Implementation of prioritised ROI coding for medical image archiving using JPEG2000

Sridhar, K.

doi:10.1109/icses.2008.4673403

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…Schelkens et al [7] combined the embedded zerotree wavelet (EZW) coding algorithm with the multi-ROI general protocol to improve scalability of the embedded code stream. Sridhar [8] proposed a dual ROI coding algorithm for medical images in which the lesion area was considered to be the ROI, and the surrounding lesion (usually susceptible area) was the secondary ROI, giving the two ROIs different levels of priority. Hu et al [9] proposed a multi-ROI medical image compression algorithm based on edge feature protection.…”

Section: A Roi Codingmentioning

confidence: 99%

A Fast Fractal Based Compression for MRI Images

et al. 2019

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Magnetic resonance imaging (MRI), which assists doctors in determining clinical staging and expected surgical range, has high medical value. A large number of MRI images require a large amount of storage space and the transmission bandwidth of the PACS system in offline storage and remote diagnosis. Therefore, high-quality compression of MRI images is very research-oriented. Current compression methods for MRI images with high compression ratio cause loss of information on lesions, leading to misdiagnosis; compression methods for MRI images with low compression ratio does not achieve the desired effect. Therefore, a fast fractal-based compression algorithm for MRI images is proposed in this paper. First, three-dimensional (3D) MRI images are converted into a two-dimensional (2D) image sequence, which facilitates the image sequence based on the fractal compression method. Then, range and domain blocks are classified according to the inherent spatiotemporal similarity of 3D objects. By using self-similarity, the number of blocks in the matching pool is reduced to improve the matching speed of the proposed method. Finally, a residual compensation mechanism is introduced to achieve compression of MRI images with high decompression quality. The experimental results show that compression speed is improved by 2-3 times, and the PSNR is improved by nearly 10. It indicates the proposed algorithm is effective and solves the contradiction between high compression ratio and high quality of MRI medical images.

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Section: A Roi Codingmentioning

confidence: 99%

A Fast Fractal Based Compression for MRI Images

et al. 2019

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“…In some cases, such as medical field, it is essential to maintain high image quality only in the region of interest, i.e. in diagnostically significant regions and the remaining region can be encoded with low quality (lossy) [1]. In addition, information within an image will be lost when transmitting on unreliable wireless channel and ROI of image has more influence on the image quality than the BG.…”

Section: Introductionmentioning

confidence: 99%

An Effective Method of Image Quality Protection Based on ROI Extraction

Zhao

Wei

et al. 2012

AMR

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Direct compression of original map can’t meet users' request on image quality. In order to harmonize image quality and bandwidth of wireless transmission, a protect scheme of UEP (Unequal Error Protection) with JEPG 2000 coder and Channel coding based on RCPC (Rate Compatible Punctured Convolutional) was proposed to improve ROI-based image quality. Firstly, ROI (region-of-interest) was extracted from Saliency Map based on visual attention model. Secondly, ROI and BG (background region) in image which transmitted in wireless channel respectively, were protected by different encoding bit rates and RCPC Code rates. Finally, the PSNR of image was calculated after transmitting in wireless channel using computer simulations. Experimental results show that ROI-based images with high-level protection get a higher quality than image without ROI while not increasing bandwidth.

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