Toward practical guideline for design of image compression algorithms for biomedical applications

作     者：Karimi, Nader Samavi, Shadrokh Soroushmehr, S. M. Reza Shirani, Shahram Najarian, Kayvan 

作者机构：Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran Univ Michigan Dept Emergency Med Ann Arbor MI 48109 USA McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada Univ Michigan Dept Computat Med & Bioinformat Ann Arbor MI 48109 USA 

出 版 物：《EXPERT SYSTEMS WITH APPLICATIONS》 (专家系统及其应用)

年 卷 期：2016年第56卷

页      面：360-367页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Medical data compression Compression performance analysis Lossless compression Big data compression 

摘      要：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. (C) 2016 Elsevier Ltd. All rights reserved.