A Fast Fractal Based Compression for MRI Images

Liu, Shuai; Bai, Weiling; Zeng, Nianyin; Wang, Shuihua

doi:10.1109/access.2019.2916934

Cited by 204 publications

(25 citation statements)

References 34 publications

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“…The general scheme of our compression approach is shown in Figure 1. The 3D image contains z slices [6][7][8] (one slice represents a 2D image (x, y)), in the compression phase, in the first step, a quincunx transformation is applied on the slice i (i = 1 to z). Second, we enter the result of the transformation in the progressive encoder SPIHT (set of partitioning in hierarchical trees) is a compression algorithm for the compression of wavelet transform coefficients.…”

Section: Research Approachmentioning

confidence: 99%

3D Medical image compression using the quincunx wavelet coupled with SPIHT

Benlabbes

2020

IJEECS

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<p>Medical imaging is a growing field due to the development of digital technologies that produce 3D and even 4D data. The counterpart to the resolution offered by these voluminal images resides in the amount of gigantic data, hence the need for compression. This article presents a new coding scheme dedicated to 3D medical images. The originality of our approach lies in the application of the Quinqunx wavelet transform coupled with the SPIHT encoder on a database of medical images. This approach achieves much higher compression rates, while maintaining a very acceptable visual quality.</p>

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Section: Research Approachmentioning

confidence: 99%

3D Medical image compression using the quincunx wavelet coupled with SPIHT

Benlabbes

2020

IJEECS

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“…Therefore, the regional feature c(i, j) is used as the measurement data of sensor [17]. Similarity α 1, 2, ⋯, N of this pixel point in the plurality of fused images is obtained by using Eq.…”

Section: Regional Featuresmentioning

confidence: 99%

Regularized super-resolution restoration algorithm for single medical image based on fuzzy similarity fusion

2019

J Image Video Proc.

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Medical images are blurred and noised due to various reasons in the acquirement, transmission and storage. In order to improve the restoration quality of medical images, a regular super-resolution restoration algorithm based on fuzzy similarity fusion is proposed. Based on maintained similarity in multiple scales, the fused similarity of the medical images is computed by fuzzy similarity fusion. First, fuzzy similarity is determined by the regional features. The images with certain similarity are obtained according to the maximum value, and the fused image is obtained by all obvious regional features. Then, an adaptive regularized restoration algorithm is employed. In order to ensure the objective function has a global optimal solution, regularized parameters of the global minimum solution of nonlinear function are solved iteratively. Finally, experimental results show that mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the restored image are visibly improved. The restored image also has an obvious improvement in the burr of local edge. Moreover, the algorithm has good stability with significantly enhanced PSNR.

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“…Among the most-effective image compression methods, FIC method is simple and has unique features such as resolution independent and high compression rate [1]- [3]. Due to these features, FIC has been not only used for compression ordinary images, but also for compression images like magnetic-resonance-imaging (MRI) [4], [5] and hyperspectral types [6], [7]. In general, fractal algorithm is a lossy compression method which is reliant on portioned iteration function system (PIFS).…”

Section: Introductionmentioning

confidence: 99%

“…This is considered as the main drawback for FIC [9]. Therefore, many researchers seek to develop new fractal-based compression methods which can reduce the number of searches as means for speeding-up the encoding time [4]. So far, various schemes and speed-up methods have been proposed [10]- [13].…”

Section: Introductionmentioning

confidence: 99%

Impact of Spatial Dynamic Search With Matching Threshold Strategy on Fractal Image Compression Algorithm Performance: Study

et al. 2020

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Fractal image compression (FIC) is a very popular technique in image compression applications due to its simplicity and superior performance. However, it has a major drawback, which is the long encoding time. This is due to the requirement of performing huge similarity search for encoding each small portion in the image. Thus, reducing the search time of FIC while keeping the quality of reconstructed images at acceptable level is still an active research topic. Therefore, this paper has focused on studying the search problem of the conventional full-search FIC algorithm and the impact of employing a spatial dynamic search technique instead with the matching threshold strategy. Unlike the conventional full-search method that is a spatially static where the search starts from a fixed position (normally from the top-left corner of the image to the bottom-right corner) regardless of the position of the range block being encoding, the idea of the dynamic search method is simple, but effective, and it is based on starting the search from the closest domain block to the range block that needs to be encoded. These two search schemes are tested under different matching threshold values, in which the search is terminated whenever a domain block with an acceptable matching level is found. To make the study comprehensive, the test is performed for different image sizes and types, range block and partitioning step sizes, and quantization levels. The experimental results show the significant impact of using the dynamic search method instead of the conventional search method specifically when the threshold is large. For the best encoding parameters, the improvement amount that can be achieved is near to 90 % in terms of search reduction and 1.6 dB PSNR in terms of image quality. INDEX TERMS Fractal image compression, fast search method, full search scheme.

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A Fast Fractal Based Compression for MRI Images

Cited by 204 publications

References 34 publications

3D Medical image compression using the quincunx wavelet coupled with SPIHT

3D Medical image compression using the quincunx wavelet coupled with SPIHT

Regularized super-resolution restoration algorithm for single medical image based on fuzzy similarity fusion

Impact of Spatial Dynamic Search With Matching Threshold Strategy on Fractal Image Compression Algorithm Performance: Study

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