Toward practical guideline for design of image compression algorithms for biomedical applications |
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| Affiliation: | 1. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran;2. Emergency Medicine Department, University of Michigan, Ann Arbor, MI, USA;3. Electrical and Computer Engineering Department, McMaster University, Hamilton, ON, Canada;4. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA;1. Pondicherry University, India;2. Velammal Engineering College, India;3. Madurai Medical College, India;1. University of Guanajuato, Engineering Division, Campus Irapuato–Salamanca, Carr. Salamanca–Valle de Santiago km 3.5 + 1.8 km, Comunidad de Palo Blanco, C.P. 36885 Salamanca, Guanajuato, Mexico;2. Universidad Industrial de Santander, Carrera 27 - Calle 9, C.P. 680002 Bucaramanga, Colombia;3. Department of Neurosurgery, University of Leipzig, University Hospital, Germany;4. Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Germany;1. Department of Industrial and Systems Engineering, Faculty of Engineering, University of Florida, Gainesville, United States;2. Department of Industrial Engineering, Faculty of Management, Istanbul Technical University, Istanbul, Turkey;1. Department of Radiology, Duke University School of Medicine, Durham, NC, United States;2. Schepens Eye Research Institute, Harvard Medical School, Boston, MA, United States;3. Department of Computer Science, Lamar University, Beaumont, TX, United States;1. The School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Republic of Korea;2. Institut National de Recherche en Informatique et en Automatique, Ecole Normale Superieure, WILLOW Team, CNRS/ENS/INRIA UMR 8548, Paris, France |
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| Abstract: | Improvements in medicine and healthcare are accelerating. Information generation, sharing, and expert analysis, play a great role in improving medical sciences. Big data produced by medical procedures in hospitals, laboratories, and research centers needs storage and transmission. Data compression is a critical tool that reduces the burden of storage and transmission. Medical images, in particular, require special consideration in terms of storage and transmissions. Unlike many other types of big data, medical images require lossless storage. Special purpose compression algorithms and codecs could compress variety of such images with superior performance compared to the general purpose lossless algorithms. For the medical images, many lossless algorithms have been proposed so far. A compression algorithm comprises of different stages. Before designing a special purpose compression method we need to know how much each stage contributes to the overall compression performance so we could accordingly invest time and effort in designing different stages. In order to compare and evaluate these multi-stage compression techniques and to design more efficient compression methods for big data applications, in this paper the effectiveness of each of these compression stages on the total performance of the algorithm is analyzed. |
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