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Neural Network and Spiht Coding-based Progressive Image Transmission & Reconstruction for Telemedicine Applications


H.K. Ravikiran and Dr. Paramesha
Abstract

In the last two decades, increasing demand and capability of communication has increased. Due to the rapid growth of the communication technologies, new paths for communication are provided and hence several data intensive real-time applications have been developed. In this field of data intensive application, telemedicine or biomedical image data processing is also considered as a crucial task where medical images need to be transmitted to the remote location over a wireless channel; this transmission causes more bandwidth consumption and inappropriate resource consumption, which may degrade the performance of the system. Moreover, sometimes, unnecessary data can be transmitted during continuous transmission which may not be useful for diagnosis and will cause bandwidth and resource consumption. In order to deal with these issues, we present a novel approach known as progressive image transmission with image coding schemes where data is be encoded before transmission and progressively decoded at the receiver end. This operation can be performed at a varied rate of transmission. In this field, set-partitioning in hierarchical tree (SPIHT) technique is considered as a promising technique but it fails to achieve the desired performance for complex image structures such as medical imaging. Hence, we introduce artificial intelligence-based compression and SPIHT-based transmission scheme for medical image applications. An extensive experimental study is carried out for different types of images and compared with the traditional algorithms. The proposed approach shows significant improvement in the system performance.

Volume 11 | 04-Special Issue

Pages: 67-77