https://orcid.org/0000-0001-6023-5838
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Abstract
Today, telemedicine has become very essential, because it provides the possibility for the health centers, hospitals, and research centers to exchange medical and diagnostic data, via IoT. Since the volume of the generated medical information in IoT is very high, transmitting this data via channels of limited bandwidth is time-consuming; thus, the information should be compressed before transmission. Todays, some of the methods compress data significantly, but quality of the restored data in these methods is very low. Considering the importance of images and information for diagnostic and medical applications, desired quality is of great importance. Thus, this study tries to present a hybrid medical information compression technique such that quality of the restored information is desired and compression is efficient. This compression technique combines two lossless compression methods, including Huffman encoding and Lempel-Ziv-Welch (LZW) that rearranges information. In this combination, a binary information arrangement is used between Hoffman and LZW techniques so that the integration of binary information reaches an information mapping and, while simplifying the mapping, is completely different for each piece of information and makes this article stand out. The simulation results of the proposed method show that medical information including signal, text, and image is reduced by an average of 37.85%. Remarkably, quality of the restored image is not degraded.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
Hossein Mohammadi
Hossein Mohammadi is a PhD student of computer software engineering at the Islamic Azad University of Sanandaj, Sanandaj, Iran. Earlier, he received the MSc degree in computer science from the Islamic Azad University of Sari, Sari, Iran, in 2014, and the BSc degree in computer engineering from the University of Urmia, Urmia, Iran, in 2009. Currently, he is working as a computer science teacher in Boukan. His current research areas include the internet of things (IoT), cloud computing and big data. Email: [email protected]
Abdulbaghi Ghaderzadeh
Abdulbaghi Ghaderzadeh received his BS in computer science from the University of Tabriz in 2004, MS in information technology from Iran University of Science and Technology (IUST) in 2006 and PhD in software engineering from the Islamic Azad University, Science and Research Branch in 2016. He is currently the head of the Department of Software Engineering at the Islamic Azad University, Sanandaj Branch. His research focuses on the design, analysis and control of telecommunication networks and embedding distributed intelligence in pure P2P systems, cloud computing and the internet of things.
Amir Sheikh Ahmadi
Amir Sheikh Ahmadi received the PhD degree in computer engineering from the University of Isfahan in July 2016. Earlier, he received the MSc degree in computer science from the Sharif University of Technology, Tehran, Iran, in 2007, and the BS degree in computer engineering from the University of Isfahan, Isfahan, Iran, in 2002. Currently, he is working as an assistant professor in the Department of Computer Engineering, the Islamic Azad University of Sanandaj, Sanandaj, Iran. His current research areas include complex networks, social network analysis, evolutionary algorithms and data mining. Email: [email protected]