COMPRESSION OF HIGH-RESOLUTION VOXEL PHANTOMS BY MEANS OF B+ TREE
DOI:
https://doi.org/10.29121/granthaalayah.v7.i12.2019.312Keywords:
Data Structures, B Tree, Processing, Compression, Voxel Phantoms, MemoryAbstract [English]
Data structures are chosen to save space and to grant fast access to data by it’s key for a particular structural representation. The data structures surveyed are linear lists, hierarchical structures, graph structures. B+ tree is an expansion of a B tree data structure which allows efficient insertions, deletions and search operations. It is used to store a large amount of data that cannot be stored in the main memory. B+ tree leaf nodes are connected together in the form of a singly linked list to make search queries more efficient and effective. The drawback of binary tree geometry is that the decrease in memory use comes at the expense of more frequent memory access, might slow down simulation in which frequent memory access constitutes a significant part of the execution time. Processing and compression of voxel phantoms without loss of quality. Voxels are often utilized in the visualization and analysis of medical and scientific (logical) information. Voxel phantoms which comprise a set of small volume components appeared towards the end of the 1980s and improved on the first scientific models. These are the models of the human body. These phantoms are an extremely exact representation. Fetching of records in the equal number of disk accesses and to reduce the access time by reducing the height of the tree and increasing the number of branches in the node.
Downloads
References
Andreu Badal and Aldo Badano (2019), ‘Reducing the Memory Requirements of High-Resolution Voxel Phantoms by Means of a Binary Tree Data Structure’, IEEE Transactions on Radiation and Plasma Medical Sciences, Vol. 3, No. 1, pp. 76-82 DOI: https://doi.org/10.1109/TRPMS.2018.2852634
Cha-Keon Cheong, Kyung-Sik Cho, and Seok-won Lee (2000), ‘Significance Tree Image Sequence Coding With DCT-based Pyramid Structure’, IEEE International Conference on Image Processing, pp. 859-862 DOI: https://doi.org/10.1109/ICIP.2000.899847
Dan Dolonius, Erik Sintorn, Viktor K¨ampe, and Ulf Assarsson (2019), Compressing Color Data for Voxelized Surface Geometry, IEEE Transactions on Visualization and Computer Graphics, Vol. 25, No. 2, pp. 1270-1282
Debin Zhao, Ying Ki Chan, and Wen Gao (2001), ‘Low-complexity And Low-memory Entropy Coder for Image Compression’, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11, No. 10, pp. 1140-1145 DOI: https://doi.org/10.1109/76.954501
Denis V. Ivanov, Eugene P. Kuzmin, Sergey V. Burtsev (1999), ‘Progressive Image Compression Using Binary Trees’, International Conference Graphicon, Moscow, Russia.
Detlev Marpe, Heiko Schwarz, Sebastian Bosse, Benjamin Bross, Philipp Helle, Tobias Hinz, Heiner Kirchhofer, Haricharan Lakshman, Tung Nguyen, Simon Oudin, Mischa Siekmann, Karsten S¨uhring, Martin Winken, and Thomas Wiegand (2010), ‘Video Compression Using Nested Quadtree Structures, Leaf Merging, and Improved Techniques for Motion Representation and Entropy Coding’, IEEE Transactions On Circuits and Systems for Video Technology, Vol. 20, No. 12, pp. 1676-1687 DOI: https://doi.org/10.1109/TCSVT.2010.2092615
Dobie and Phill Lewis (1991), ‘Data structures for image processing in C’, Pattern Recognition Letters, Vol. 12, pp. 457-466 DOI: https://doi.org/10.1016/0167-8655(91)80009-Y
Donald Meagher (1982), ‘Geometric Modelling Using Octree Encoding’, Computer Graphics and Image Processing, Vol.19, pp.129-147 DOI: https://doi.org/10.1016/0146-664X(82)90104-6
Feiyu Chen, Predrag R. Bakic, Andrew D. A. Maidment, Shane T. Jensen, Xiquan Shi, and David D. Pokrajac (2015), ‘Description and Characterization of a Novel Method for Partial Volume Simulation in Software Breast Phantoms’, IEEE Transactions On Medical Imaging, Vol. 34, No. 10, pp. 2146-2161 DOI: https://doi.org/10.1109/TMI.2015.2424854
Gyu Sang Choi, Byung-won On, and Ingyu Lee (2015), ‘PB+-tree: PCM-aware B+-tree’, IEEE Transactions on Knowledge and Data Engineering, Vol. 27, No. 9, pp. 2466-2479 DOI: https://doi.org/10.1109/TKDE.2015.2419660
Jiangtao Cui, Bin Xiao, Zhiliang Yin (2010), Speed up linear scan in high-dimensions using extended B+-tree, International Conference on Database Technology and Applications, pp. 1-4
Jingqi Ao, Sunanda Mitra, Brian Nutter (2014), ‘Fast and Efficient Lossless Image Compression Based on CUDA Parallel Wavelet Tree Encoding’, IEEE International Conference on Image Analysis and Interpretation, pp. 21-24 DOI: https://doi.org/10.1109/SSIAI.2014.6806019
John Alan Robinson, A. Druet, and N. Gosset (2000), ‘Video Compression with Binary Tree Recursive Motion Estimation and Binary Tree Residue Coding’, IEEE Transactions on Image Processing, Vol. 9, No. 7, pp. 1288-1292 DOI: https://doi.org/10.1109/83.847841
LINDA Shapiro (1979), ‘Data Structures for Picture Processing: A Survey’, Computer Graphics and Image Processing, Vol. 11, pp. 162-184 DOI: https://doi.org/10.1016/0146-664X(79)90065-0
Li Wern Chew, Li-Minn Ang, and Kah Phooi Seng (2008), ‘New Virtual SPIHT Tree Structures for Very Low Memory Strip-Based Image Compression’, IEEE Signal Processing Letters, Vol. 15, pp. 389-392 DOI: https://doi.org/10.1109/LSP.2008.920515
Marc Alzina, Wojciech Szpankowski, and Ananth Grama (2002), ‘2D-Pattern Matching Image and Video Compression: Theory, Algorithms, and Experiments’, IEEE Transactions on Image Processing, Vol. 11, No. 3, pp. 318-331 DOI: https://doi.org/10.1109/83.988964
Philippe Salembier, and Luis Garrido (2000), ‘Binary Partition Tree as an Efficient Representation for Image Processing, Segmentation, and InformationRetrieval’, IEEE Transactions on Image Processing, Vol. 9, No. 4, pp. 561-576 DOI: https://doi.org/10.1109/83.841934
Riccardo Distasi, Michele Nappi, and Sergio Vitulano (1997), ‘Image Compression by B-Tree Triangular Coding’, IEEE Transactions On Communications, Vol. 45, No. 9, pp. 1095-1099 DOI: https://doi.org/10.1109/26.623074
Sho Tanimoto And Theodosios Pavlidis (1975), ‘A Hierarchical Data Structure for Picture Processing’, Computer Graphics and Image Processing, Vol. 4, pp. 104-119 DOI: https://doi.org/10.1016/S0146-664X(75)80003-7
Steven Leslie Horowitz And Theodosios Pavlidis (1976), ‘Picture Segmentation by A Tree Traversal Algorithm’, Journal of The Association for Computing Machinery. Vol. 23, No. 2, pp. 368-388. DOI: https://doi.org/10.1145/321941.321956
Xiaolin Wu, and Yonggang Fang (1995), ‘A Segmentation-Based Predictive Multiresolution Image Coder’, IEEE Transactions on Image Processing, Vol. 4, No. 1, pp. 34-47 DOI: https://doi.org/10.1109/83.350815
Zixiang Xiong, Xiaolin Wu, Samuel Cheng, Jianping Hua (2003), ‘Lossy-to-lossless Compression of Medical Volumetric Data Using Three-dimensional Integer Wavelet Transforms’, IEEE Transactions on Medical Imaging, Vol. 22, No. 3, pp. 459-470 DOI: https://doi.org/10.1109/TMI.2003.809585
Downloads
Published
How to Cite
Issue
Section
License
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
