Near lossless medical image compression using block BWT–MTF and hybrid fractal compression techniques

Devadoss, C. Peter; Sankaragomathi, B.

doi:10.1007/s10586-018-1801-3

Cited by 24 publications

(12 citation statements)

References 14 publications

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“…They are a comparison of single-layer image compression and continuous layer image compression, respectively. In addition, the algorithm of this paper is compared with the BWT-MTF algorithm [36]- [37]. In these experiments, size of range block is selected as 4 × 4, and size of domain block is 8 × 8, and the horizontal step and the vertical step are both 8.…”

Section: Experimental Results and Analysismentioning

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: Experimental Results and Analysismentioning

confidence: 99%

A Fast Fractal Based Compression for MRI Images

et al. 2019

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“…In this paper, the value of  is 1. Hence, near-lossless image compression has been proposed as trade-off between low compression ratio of lossless image compression algorithm and high distortion of lossy image compression algorithm [18][19][20][21][22][23].…”

Section: Review Of Lossy Lossless and Near-lossless Compression Methodsmentioning

confidence: 99%

Near-lossless image compression using an improved edge adaptive hierarchical interpolation

Sinshahw

2020

IJEECS

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<span>In medical and scientific imaging, lossless image compression is recommended because the loss of minor details subject to medical diagnosis can lead to wrong diagniosis. On the other hand, lossy compression of medical images is required in the long run because a huge quantity of medical data needs remote storage. This, in turn, takes long time to search and transfer an image. Instead of thinking lossless or lossy image compression methods, near-loss image compression mehod can be used to compromise the two conflicting requirements. In the previous work, an edge adaptive hierarchical interpolation (EAHINT) was proposed for resolution scalable lossless compression of images. In this paper, it was enhanced for scalable near-less image compression. The interpolator of this arlgorithm swiches among one-directional, multi-directional and non-directional linear interpolators adaptively based on the strength of the edge in a 3x3 local casual context of the current pixel being predicted. The strength of the edge in local window was estimated using the variance of the the pixels in the local window. Although the actual predictors are still linear functions, the switching mechanism tried to deal with non-linear structures like edges. Simulation results demonstrate that the improved interpolation algorithm has better compression ratio over the the exsisting the original EAHINT algorithm and JPEG-Ls image compression standard. </span>

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“…Peter Devadoss, as well as B. Sankaragomathi, use block Burrow-Wheeler transform-move to front Transform (BWT-MTF) with Huffman encoding and hybrid fractal encoding to efficiently transmit medical images. [21]. This study was conducted using an open-source MedPix database using the ultrasound, MRI, CT scan, and XRay image.…”

Section: Various Roi-based Hybrid Medical Image Compression Algorithmsmentioning

confidence: 99%

“…The researcher [16] not considered the quality of the output image; therefore, he did not consider the PSNR value. The researcher [21,23] aim for the quality of the output image. Thus, the compression ratio is not measured by them.…”

Section: Telkomnika Telecommun Comput El Controlmentioning

confidence: 99%

A review on region of interest-based hybrid medical image compression algorithms

Aziz

Sam

Mohamed³

et al. 2020

TELKOMNIKA

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Digital medical images have become a vital resource that supports decision-making and treatment procedures in healthcare facilities. The medical image consumes large sizes of memory, and the size keeps on growth due to the trend of medical image technology. The technology of telemedicine encourages the medical practitioner to share the medical image to support knowledge sharing to diagnose and analyse the image. The healthcare system needs to ensure distributes the medical image accurately with zero loss of information, fast and secure. Image compression is beneficial in ensuring that achieve the goal of sharing this data. The region of interest-based hybrid medical compression algorithm plays the parts to reduce the image size and shorten the time of medical image compression process. Various studies have enhanced by combining numerous techniques to get an ideal result. This paper reviews the previous works conducted on a region of interest-based hybrid medical image compression algorithms.

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Near lossless medical image compression using block BWT–MTF and hybrid fractal compression techniques

Cited by 24 publications

References 14 publications

A Fast Fractal Based Compression for MRI Images

A Fast Fractal Based Compression for MRI Images

Near-lossless image compression using an improved edge adaptive hierarchical interpolation

A review on region of interest-based hybrid medical image compression algorithms

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