Hybrid Algorithmic Approach for Medical Image Compression Based on Discrete Wavelet Transform (DWT) and Huffman Techniques for Cloud Computing

ASHWIN, DHIVAKAR M R and D., RAVICHANDRAN and VIJAY, DAKHA (2015) Hybrid Algorithmic Approach for Medical Image Compression Based on Discrete Wavelet Transform (DWT) and Huffman Techniques for Cloud Computing. In: Second International Conference on Advances In Computing, Control And Networking - ACCN 2015, 28-29 August, 2015, Bangkok, Thailand.

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Abstract

As medical imaging facilities move towards complete filmless imaging and also generate a large volume of image data through various advance medical modalities, the ability to store, share and transfer images on a cloud-based system is essential for maximizing efficiencies. The major issue that arises in teleradiology is the difficulty of transmitting large volume of medical data with relatively low bandwidth. Image compression techniques have increased the viability by reducing the bandwidth requirement and cost-effective delivery of medical images for primary diagnosis.Wavelet transformation is widely used in the fields of image compression because they allow analysis of images at various levels of resolution and good characteristics. The algorithm what is discussed in this paper employs wavelet toolbox of MATLAB. Multilevel decomposition of the original image is performed by using Haar wavelet transform and then image is quantified and coded based on Huffman technique. The wavelet packet has been applied for reconstruction of the compressed image. The simulation results show that the algorithm has excellent effects in the image reconstruction and better compression ratio and also study shows that valuable in medical image compression on cloud platform.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Wavelet Packet, Discrete Wavelet Transform (DWT), Lossless image compression, Medical image, Huffman coding, Cloud computing
Depositing User: Mr. John Steve
Date Deposited: 27 Apr 2019 12:26
Last Modified: 27 Apr 2019 12:26
URI: http://publications.theired.org/id/eprint/1634

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