Lossless compression of grayscale medical images: effectiveness of traditional and state-of-the-art approaches

2000 ◽  
Author(s):  
David A. Clunie
Keyword(s):  
State Of The Art ◽  
Medical Images ◽  
Lossless Compression

scholarly journals Robust Visibility Surface Determination in Object Space via Plücker Coordinates

2021 ◽  
Vol 7 (6) ◽  
pp. 96
Author(s):  
Alessandro Rossi ◽  
Marco Barbiero ◽  
Paolo Scremin ◽  
Ruggero Carli
Keyword(s):  
Finite Number ◽  
State Of The Art ◽  
Lossless Compression ◽  
3D Models ◽  
Optimal Result ◽  
Object Space ◽  
Plücker Coordinates ◽  
Determination Methods

Industrial 3D models are usually characterized by a large number of hidden faces and it is very important to simplify them. Visible-surface determination methods provide one of the most common solutions to the visibility problem. This study presents a robust technique to address the global visibility problem in object space that guarantees theoretical convergence to the optimal result. More specifically, we propose a strategy that, in a finite number of steps, determines if each face of the mesh is globally visible or not. The proposed method is based on the use of Plücker coordinates that allows it to provide an efficient way to determine the intersection between a ray and a triangle. This algorithm does not require pre-calculations such as estimating the normal at each face: this implies the resilience to normals orientation. We compared the performance of the proposed algorithm against a state-of-the-art technique. Results showed that our approach is more robust in terms of convergence to the maximum lossless compression.

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 Representation of k-mer sets using spectrum-preserving string sets

2020 ◽  
Author(s):  
Amatur Rahman ◽  
Paul Medvedev
Keyword(s):  
Lower Bound ◽  
State Of The Art ◽  
Lossless Compression ◽  
De Bruijn Graph ◽  
Compact Set ◽  
Greedy Method ◽  
Bioinformatics Analyses ◽  
Path Cover ◽  
Order Of Magnitude ◽  
Index Size

AbstractGiven the popularity and elegance of k-mer based tools, finding a space-efficient way to represent a set of k-mers is important for improving the scalability of bioinformatics analyses. One popular approach is to convert the set of k-mers into the more compact set of unitigs. We generalize this approach and formulate it as the problem of finding a smallest spectrum-preserving string set (SPSS) representation. We show that this problem is equivalent to finding a smallest path cover in a compacted de Bruijn graph. Using this reduction, we prove a lower bound on the size of the optimal SPSS and propose a greedy method called UST that results in a smaller representation than unitigs and is nearly optimal with respect to our lower bound. We demonstrate the usefulness of the SPSS formulation with two applications of UST. The first one is a compression algorithm, UST-Compress, which we show can store a set of k-mers using an order-of-magnitude less disk space than other lossless compression tools. The second one is an exact static k-mer membership index, UST-FM, which we show improves index size by 10-44% compared to other state-of-the-art low memory indices. Our tool is publicly available at: https://github.com/medvedevgroup/UST/.

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.

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