Medical Image Compression Approach Based on Image Resizing, Digital Watermarking and Lossless Compression

Amri, Hédi; Khalfallah, Ali; Gargouri, Malek; Nebhani, Naïma; Lapayre, Jean-Christophe; Bouhlel, Med Salim

doi:10.1007/s11265-016-1150-5

Cited by 33 publications

(14 citation statements)

References 29 publications

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“…Lossy techniques with compression ratios of about 10:1 are commonly adopted for picture archiving and communication system (PACS) for use in primary diagnosis and clinical review, using Digital Imaging and Communication in Medicine (DICOM), namely, DICOMJPEG or JPEG-2000 compression algorithms [95]. Lossy image compression research in medical imaging has traditionally concentrated on transform methods, especially the two-dimensional DCT [101], [102] or wavelet/subband coding methods [103].…”

Section: Image Compression In Medical Imagingmentioning

confidence: 99%

Image compression techniques: A survey in lossless and lossy algorithms

2018

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The bandwidth of the communication networks has been increased continuously as results of technological advances. However, the introduction of new services and the expansion of the existing ones have resulted in even higher demand for the bandwidth. This explains the many efforts currently being invested in the area of data compression. The primary goal of these works is to develop techniques of coding information sources such as speech, image and video to reduce the number of bits required to represent a source without significantly degrading its quality. With the large increase in the generation of digital image data, there has been a correspondingly large increase in research activity in the field of image compression. The goal is to represent an image in the fewest number of bits without losing the essential information content within. Images carry three main type of information: redundant, irrelevant, and useful. Redundant information is the deterministic part of the information, which can be reproduced without loss from other information contained in the image. Irrelevant information is the part of information that has enormous details, which are beyond the limit of perceptual significance (i.e., psychovisual redundancy). Useful information, on the other hand, is the part of information, which is neither redundant nor irrelevant. Human usually observes decompressed images. Therefore, their fidelities are subject to the capabilities and limitations of the Human Visual System.

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Section: Image Compression In Medical Imagingmentioning

confidence: 99%

Image compression techniques: A survey in lossless and lossy algorithms

2018

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“…Experimental results showed that the support prediction method improved compression ratio and maintained marginal PSNR and minimum absolute error. The new method proposed by Amri et al [31] combined image reduction and extension techniques, digital watermarking and lossless compression standards such as JPEG-LS (JLS) and TIFF formats. Zhang et al [32] developed a novel machine learning system that automatically diagnoses Alzheimer's disease from MRI images.…”

Section: B Non-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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“…Whereas text compressions strongly recommend lossless because nobody wants to have some meaningless or even sometimes horrible messages instead of correct ones. Data compression ratio [5][6][7] is the criteria to know reduction size of the com-pressed file over uncompressed file. The paper [8] attempts to improve LZW with three techniques include redundancy Encoding String Indexes, Estimating Probabilities for String Numbers, Exploiting Possibilities for Adaptive Loading of the Dictionary.…”

Section: Related Papersmentioning

confidence: 99%

“…Three input text files [7] are taken for compress with the original LZW. The results are shown below in Table.III.…”

Section: Fig2 Comparison Of All Techniques Compression Ratiosmentioning

confidence: 99%

“…Now, we getting better compression ratio compare to original LZW compression ratio. Here used bench mark text files [7] for calculating the performance between enhanced LZW and original LZW. The results are shows the enhanced LZW and the plotted graph is showing the difference of the original LZW and Modified LZW compression ratio as in Table. IV.…”

Section: Fig2 Comparison Of All Techniques Compression Ratiosmentioning

confidence: 99%

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Intensification of Lempel-ZIV-Welch Algorithm

2019

IJITEE

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There is a necessity to reduce the consumption of exclusive resources. This is achieved using data compression. The data compression is one well known technique which can reduce the file size. A plethora of data compression algorithms are available which provides compression in various ratios. LZW is one of the powerful widely used algorithms. This paper attempts to propose and apply some enhancements to LZW, hence comes out with an efficient lossless text compression scheme that can compress a given file at better compression ratio. The paper proposes three approaches which practically enhances the original algorithm. These approaches try to gain better compression ratio. In approach1, it exploits the notion of using existing string code with odd code for a newly encounter string which is reverse of existing. In approach2 it uses a choice of code length for the current compression, so avoiding the problem of dictionary overflow. In approach3 it appends some selective set of frequently encountered string patterns. So the intensified LZW method provides better compression ratio with the inclusion of the above features.

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Medical Image Compression Approach Based on Image Resizing, Digital Watermarking and Lossless Compression

Cited by 33 publications

References 29 publications

Image compression techniques: A survey in lossless and lossy algorithms

Image compression techniques: A survey in lossless and lossy algorithms

A Fast Fractal Based Compression for MRI Images

Intensification of Lempel-ZIV-Welch Algorithm

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