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Implementation and Performance Assessment of Biomedical Image Compression and Reconstruction Algorithms for Telemedicine Applications: Compressive Sensing for Biomedical Images
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Author(s): Charu Bhardwaj (Jaypee University of Information Technology, India), Urvashi Sharma (Jaypee University of Information Technology, India), Shruti Jain (Jaypee University of Information Technology, India) and Meenakshi Sood (Jaypee University of Information Technology, India)
Copyright: 2019
Pages: 29
Source title:
Medical Data Security for Bioengineers
Source Author(s)/Editor(s): Butta Singh (Guru Nanak Dev University, India), Barjinder Singh Saini (Dr. B. R. Ambedkar National Institute of Technology, India), Dilbag Singh (Dr. B. R. Ambedkar National Institute of Technology, India) and Anukul Pandey (Dumka Engineering College, India)
DOI: 10.4018/978-1-5225-7952-6.ch003
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Abstract
Compression serves as a significant feature for efficient storage and transmission of medical, satellite, and natural images. Transmission speed is a key challenge in transmitting a large amount of data especially for magnetic resonance imaging and computed tomography scan images. Compressive sensing is an optimization-based option to acquire sparse signal using sub-Nyquist criteria exploiting only the signal of interest. This chapter explores compressive sensing for correct sensing, acquisition, and reconstruction of clinical images. In this chapter, distinctive overall performance metrics like peak signal to noise ratio, root mean square error, structural similarity index, compression ratio, etc. are assessed for medical image evaluation by utilizing best three reconstruction algorithms: basic pursuit, least square, and orthogonal matching pursuit. Basic pursuit establishes a well-renowned reconstruction method among the examined recovery techniques. At distinct measurement samples, on increasing the number of measurement samples, PSNR increases significantly and RMSE decreases.
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