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Medical Image Compression Using Fuzzy C-Means Based Contourlet Transform

Author(s): M. Tamilarasi | V. Palanisamy

Journal: Journal of Computer Science
ISSN 1549-3636

Volume: 7;
Issue: 9;
Start page: 1386;
Date: 2011;
Original page

Keywords: Contourlet transform | Peak Signal to Noise Ratio (PSNR) | Compression Ratio (CR) | Directional Filter Bank (DFB) | Region Of Interest (ROI) | Laplacian Pyramid (LP) | Fuzzy C-means (FCM)

Problem statement: To meet the demand for high speed transmission of image in efficient image storage and remote medical treatment, the efficient image compression is essential. The contourlet transform along with wavelet theory has great potential in medical image compression. Approach: The significant portion of the medical image applied with Fuzzy C-means based contourlet transform. DWT applied to the rest of the image. Finally modified EZW of six symbols differing from normal EZW was applied to the whole image. This technique increases PSNR and gives better compression ratio. Results: The MATLAB simulation showed that the method of separate transforms to the two regions proves better results compared to the ordinary way of applying only single transforms to the whole image. The results revealed that proposed algorithm was simple and computationally fewer complexes based on embedded block coding with coefficient truncation. Conclusion: The compression of the proposed algorithm is superior to EZW, SPIHT. Our new method of compression algorithm can be used to improve the performance of Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR). In future this study can be extended to real time applications for video compression in medical images.

Tango Jona
Tangokurs Rapperswil-Jona

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