Implementation and Performance Assessment of Biomedical Image Compression and Reconstruction Algorithms for Telemedicine Applications

Medical Data Security for Bioengineers Pub Date : 1900-01-01 DOI:10.4018/978-1-5225-7952-6.CH003

C. Bhardwaj, Urvashi Sharma, Shruti Jain, M. Sood

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引用次数: 2

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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远程医疗应用中生物医学图像压缩与重建算法的实现与性能评估

压缩是有效存储和传输医疗、卫星和自然图像的重要特征。传输速度是传输大量数据的关键挑战，特别是对于磁共振成像和计算机断层扫描图像。压缩感知是一种基于优化的选择，它使用子奈奎斯特准则获取稀疏信号，只利用感兴趣的信号。本章探讨压缩感知对临床图像的正确感知、获取和重建。本章利用最优的三种重构算法:基本寻优算法、最小二乘算法和正交匹配寻优算法，对医学图像评价的峰值信噪比、均方根误差、结构相似指数、压缩比等独特的综合性能指标进行了评估。基本追求建立了一种著名的重建方法在审查的恢复技术。在不同测量样本下，随着测量样本数量的增加，PSNR显著增加，RMSE降低。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Medical Data Security for Bioengineers

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