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Comparison between different Compression and Decompression Techniques on MRI Scan Images

Author(s): Prateek Verma, Praveen Verma, Amrita Sahu, Sonam Sahu, Neha Sahu

Journal: International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
ISSN 2278-1323

Volume: 1;
Issue: 7;
Start page: 109;
Date: 2012;
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Keywords: Compression | Diagnostics | PACS | Wavelet Function

The main objective of this paper is to distribute the medical images to different hospitals and among the staff of the same medical centre within short span of time and efficiently. A lot of hospitals handle their medical image data with computers. The use of computers and a network makes it possible to distribute the image data among the staff efficiently. As the health care is computerized new techniques and applications are developed, among them are the MR and CT techniques. MR and CT produce sequence of images (image stacks) each along the cross-section of an object. The amount of data produced by these techniques is vast and this might be a problem when sending the data over a network. To overcome this problem image compression has been introduced in the field of medical. Medical image compression plays a key role as hospitals, move towards film- less imaging and go completely digital compression. Image compression will allow Picture Archiving and Communication Systems (PACS) to reduce the file sizes on their storage requirements while maintaining relevant diagnostic information. To achieve higher degree of compression we have to selected lossy compression technique. This project is an approach to improve the performance of medical image compression while satisfying both the medical team who need to use it, and the legal team who need to defend the hospital against any malpractice resulting from misdiagnosis owing to faulty compression of medical images. This paper is focused on selecting the most appropriate wavelet function for a given type of biomedical image compression. In this project we studied the behavior of different type of wavelet function with different type of biomedical images and suggested the most appropriate wavelet function that can perform optimum compression for a given type of biomedical image. The wavelet function that gives the maximum compression for a specific type of biomedical image will be the most appropriate wavelet for that type of biomedical image compression.
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