Lossless compression of medical images using hierarchical autoregressive models

2002 ◽  
Author(s):  
M. Das ◽  
C. Lin
Keyword(s):  
Medical Images ◽  
Lossless Compression ◽  
Autoregressive Models

scholarly journals Lossless Compression of Medical Image Sequences Using a Resolution Independent Predictor and Block Adaptive Encoding

2019 ◽  
Vol 9 (2) ◽  
pp. 69-79 ◽  
Author(s):  
Urvashi Sharma ◽  
Meenakshi Sood ◽  
Emjee Puthooran
Keyword(s):  
Medical Images ◽  
Block Size ◽  
Lossless Compression ◽  
Experimental Tests ◽  
Threshold Value ◽  
Image Resolution ◽  
Coding Efficiency ◽  
Volumetric Medical Images ◽  
Adaptive Encoding ◽  
Block Based

The proposed block-based lossless coding technique presented in this paper targets at compression of volumetric medical images of 8-bit and 16-bit depth. The novelty of the proposed technique lies in its ability of threshold selection for prediction and optimal block size for encoding. A resolution independent gradient edge detector is used along with the block adaptive arithmetic encoding algorithm with extensive experimental tests to find a universal threshold value and optimal block size independent of image resolution and modality. Performance of the proposed technique is demonstrated and compared with benchmark lossless compression algorithms. BPP values obtained from the proposed algorithm show that it is capable of effective reduction of inter-pixel and coding redundancy. In terms of coding efficiency, the proposed technique for volumetric medical images outperforms CALIC and JPEG-LS by 0.70 % and 4.62 %, respectively.

scholarly journals Lossless Compression of Medical Images based on the Differential Probability of Images

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Medical Image ◽  
Large Scale ◽  
Medical Images ◽  
Lossless Compression ◽  
Differential Method ◽  
Huffman Coding ◽  
Difference Matrix ◽  
Differential Probability ◽  
Compression Effect ◽  
The Difference

Lossless compression is crucial in the remote transmission of large-scale medical image and the retainment of complete medical diagnostic information. The lossless compression method of medical image based on differential probability of image is proposed in this study. The medical image with DICOM format was decorrelated by the differential method, and the difference matrix was optimally coded by the Huffman coding method to obtain the optimal compression effect. Experimental results obtained using the new method were compared with those using Lempel–Ziv–Welch, modified run–length encoding, and block–bit allocation methods to verify its effectiveness. For 2-D medical images, the lossless compression effect of the proposed method is the best when the object region is more than 20% of the image. For 3-D medical images, the proposed method has the highest compression ratio among the control methods. The proposed method can be directly used for lossless compression of DICOM images.

scholarly journals OFCS: Optimized Framework of Compressive Sensing for Medical Images in Bottleneck Network Condition

2018 ◽  
Vol 8 (5) ◽  
pp. 2829
Author(s):  
Lakshminarayana M ◽  
Mrinal Sarvagya
Keyword(s):  
Compressive Sensing ◽  
Medical Image ◽  
Medical Images ◽  
Region Of Interest ◽  
Lossless Compression ◽  
Cost Effective ◽  
Signal Quality ◽  
Effective Solution ◽  
The Cost

Compressive sensing is one of teh cost effective solution towards performing compression of heavier form of signals. We reviewed the existing research contribution towards compressive sensing to find that existing system doesnt offer any form of optimization for which reason the signal are superiorly compressed but at the cost of enough resources. Therefore, we introduce a framework that optimizes the performance of the compressive sensing by introducing 4 sequential algorithms for performing Random Sampling, Lossless Compression for region-of-interest, Compressive Sensing using transform-based scheme, and optimization. The contribution of proposed paper is a good balance between computational efficiency and quality of reconstructed medical image when transmitted over network with low channel capacity. The study outcome shows that proposed system offers maximum signal quality and lower algorithm processing time in contrast to existing compression techniuqes on medical images.

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