Implementation of region based medical image compression for telemedicine application

Sophia, P. Eben; Anitha, J.

doi:10.1109/iccic.2014.7238321

Cited by 11 publications

(9 citation statements)

References 14 publications

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“…Generally, the ROI segment implements the lossless algorithm and non-ROI compressed using the lossy algorithm. The selection of techniques and algorithms for compress image depends on the objective of the research conducted by the appropriate researcher [22]. Then the resulting compression image for both ROI and non-ROI is reconstructed.…”

Section: Discussionmentioning

confidence: 99%

“…The compression method's performance suggested showing an improvement in the compression rate between 10% and 25% compared to standard image compression techniques used in the medical industry. P. Eben Sophia and J. Anitha have to propose the compression technique of MRI images with utilised ROI based compression, which increases the compression ratio compared to the block-based method [22]. They are locating ROI with split and merge technique and ROI mask generation.…”

Section: Various Roi-based Hybrid Medical Image Compression Algorithmsmentioning

confidence: 99%

“…Figure 4 and Figure 5 shows the trend result of the compression image using CR and PSNR for the particular ROI-based hybrid medical image compression by previous studies. The graph shows that the researcher [20,22,24] are using both CR and PSNR as the evaluation matrix to measure the algorithm. The best result is by the research [24], which generate the highest CR and PSNR compress image.…”

Section: Telkomnika Telecommun Comput El Controlmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Various Roi-based Hybrid Medical Image Compression Algorithmsmentioning

confidence: 99%

Section: Telkomnika Telecommun Comput El Controlmentioning

confidence: 99%

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

Aziz

Sam

Mohamed³

et al. 2020

TELKOMNIKA

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“…The algorithm used the region growing method to separate ROI and BG in an image, and then used the proposed CVQ scheme to compress the two parts separately. In the algorithm proposed by Sophia and Anitha [5], the ROI part of an image is losslessly compressed by run-length coding, Huffman coding or arithmetic coding, and the BG part is subjected to lossy compression based on vector quantization.…”

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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“…Sophia P. and Anitha J. have considered the region of interest of the physician in compressing of MR images [5]. ROI has been compressed by lossless approach, while the Non-ROI is compressed as lossy.…”

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confidence: 99%

Medical Image Compression for Telemedicine Applications

Nasıfoğlu¹,

Eroğul²

2019

ABEB

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High resolution medical images obtained by different imaging modalities stored in PACS needs higher storage space and bandwidth because of requiring much space in memory. In this sense, compression of medical images is important for efficient use of database. The main purpose of image compression is to reduce the number of bits representing the image while preserving the image quality and the intensity level of the pixels as much as possible depending on grayscale or RGB image [1]. Since medical images also contain diagnostic information about a disease or an artifact, less or no loss of detail in terms of quality is desired while compressing the significant areas. Otherwise, there may be difficulties or misdiagnosis in the treatment planning. With the lossless image compression technique, it is possible to preserve the entire pixel data while reducing the image size. The disadvantage of this technique is that it does not gain high memory size due to the low compression performance. On the other hand, higher compression ratios can be obtained by compromising redundant data with the lossy compression technique. Loss of image quality seems to be a risky condition in terms of correct diagnosis, but it is possible to reach acceptable compression rates and control data loss by setting appropriate parameters without losing diagnostic information. Adaptive image compression (AIC) is a hybrid technique that combines both lossless and lossy image compression techniques [2,3]. When applying AIC, it is primarily necessary to detect regions of interest in order to determine which regions will be compressed as lossy or lossless. After determining the focused or noticeable regions of radiologist on graph, it is possible to sort and adaptively compress these regions by importance. If the first order region is considered as the area containing the most information for the diagnosis, lossless compression can be applied here so as to avoid loss of detail. If there are second, third, and continuing order regions of interest, it may be preferable to adaptively compress these areas with little loss. Non-ROI (Non-Region of Interest) parts can be considered as less important or healthy areas in the diagnostic sense. Therefore, higher compression ratios can be achieved by compromising more details for these regions. After applying AIC, reconstructed image should be evaluated as sufficient and acceptable by the physician in terms of diagnostic information.Thus, the compressed images are recommended to be evaluated with subjective criteria in addition to objective criteria.The most common used objective criteria parameters for evaluating the compression performance are Compression Ratio (CR), Bits per Pixel (BPP), Mean Square Error (MSE) and the Peak Signal to Noise Ratio (PSNR). Compression ratio is defined as the

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Implementation of region based medical image compression for telemedicine application

Cited by 11 publications

References 14 publications

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

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

A Fast Fractal Based Compression for MRI Images

Medical Image Compression for Telemedicine Applications

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