An Improved Method for CT Image Coding Using PSNR Prediction Model Based on ELM

Abstract:

In telemedicine, medical images are always considered very important telemedicine diagnostic evidences. High transmission delay in a bandwidth limited network becomes an intractable problem because of its large size. It’s important to achieve a quality balance between Region of Interest (ROI) and Background Region (BR) when ROI-based image encoding is being used. In this paper, a research made on balancing method of LS-SVM based ROI/BR PSNR prediction model to optimize the ROI encoding shows it’s much better than conventional methods but with very high computational complexity. We propose a new method using extreme learning machine (ELM) with lower computational complexity to improve encoding efficiency compared to LS-SVM based model. Besides, it also achieves the same effect of balancing ROI and BR.

• Periodical: Applied Mechanics and Materials (Volumes 738-739)
• Pages: 598-601
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.738-739.598
• Online since: March 2015
• Authors: Han Yang Zhu*, Xin Yu Jin, Jian Feng Shen
• Keywords: CT Image, Elm, Image Coding, PSNR, ROI

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