Digital Image Processing

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Comparative Analysis of PCA, SPIHT and Haar Methods in Medical Image Compression


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1 F.E.T, SSTC, SSGI, Junwani, Bhilai, India
     

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Compression of medical image has acquired great attention attributable to its raising need to decrease the picture size while not compromising the diagnostically crucial medical data exhibited on the picture. PCA algorithmmay be used to help in image compression. Here PCA algorithm is characterized in two forms i.e. Standard PCA and Block-Based PCA. The block based PCA has 2 extended-PCA algorithms that manipulate the block data of the image are evaluated. The 1st algorithm is referred to as block-by-block PCA wherestandard PCA algorithm is utilized on every block of the picture. In the next algorithm- the block-to-row PCA, all of block data are initially concatenated into a row before the standard PCA algorithm is thereforeutilizedin the remodelled matrix. In this paper, the block based PCA and SPIHT primarily applied on the ROI region whereas General PCA and HAAR wavelet were applied to non-ROI region. An arbitrary shaped segmentation (Manual segmentation) is employed to trace the specified ROI on the image.The SPIHT is being compared with the block based PCA methods in terms of image quality and compression ratio while selecting either general PCA or HAAR wavelet on Non ROI. With this work, it’s observed that block-based PCA performs superior to the SPIHTwith regards toimage quality, producingsimilar compression ratio.


Keywords

Medical Image Compression, Principal Component Analysis (PCA), Block-Based PCA, Compression Ratio, Image Quality, HAAR, SPIHT.
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  • Lim, S. T., Yap, D. F. W., & Manap, N. A. (2014, September). Medical image compression using block-based PCA algorithm. In Computer, Communications, and Control Technology (I4CT), 2014 International Conference on (pp. 171-175). IEEE.

  • Lim, S. T., Yap, D. F. W., & Manap, N. A. (2014, August). A gui system for region-based image compression using principal component analysis. InComputational Science and Technology (ICCST), 2014 International Conference on (pp. 1-4). IEEE.

  • Santo, R. D. E. (2012). Principal Component Analysis applied to digital image compression. Einstein (São Paulo), 10(2), 135-139.

  • Mofarreh-Bonab, M., & Mofarreh-Bonab, M. (2012). A new technique for image compression using PCA. International Journal of Computer Science & Communication Networks IJCSCN (2249-5789), 2(1), 111-116.

  • Stolevski, S. (2010). Hybrid PCA algorithm for image compression. In 18th Telecommunication forum TELFOR (pp. 685-687).

  • Dwivedi, A., Tolambiya, A., Kandula, P., Bose, N. S. C., Kumar, A., & Kalra, P. K. (2006). Color image compression using 2-dimensional principal component analysis (2DPCA). A A, 2, 1.

  • Costa, S., & Fiori, S. (2001). Image compression using principal component neural networks. Image and vision computing, 19(9), 649-668.

  • Gokturk, S. B., Tomasi, C., Girod, B., & Beaulieu, C. (2001). Medical image compression based on region of interest, with application to colon CT images. In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE (Vol. 3, pp. 2453-2456). IEEE.

  • Taur, J. S., & Tao, C. W. (1996, September). Medical image compression using principal component analysis. In Image Processing, 1996. Proceedings., International Conference on (Vol. 1, pp. 903-906). IEEE.

  • Sadashivappa, G., Jayakar, M., & Babu, K. A. (2010, February). Analysis of SPIHT Algorithm Using Tiling Operations. In Signal Acquisition and Processing, 2010. ICSAP'10. International Conference on (pp. 332-336). IEEE.

  • Jiang, C., & Yin, S. (2010, October). A Hybrid Image Compression Algorithm Based on Human Visual System. In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010) (Vol. 9, pp. V9-170). IEEE.

  • Zhu, L., & Yang, Y. M. (2011, September). Embedded Image Compression Using Differential Coding and Optimization Method. In Wireless Communications, Networking and Mobile Computing (WiCOM), 2011 7th International Conference on (pp. 1-4). IEEE.

  • Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4), 600-612.

  • Ting, L. S., Weng, D. Y. F., & Manap, N. B. A. (2015). A Novel Approach for Arbitrary-Shape ROI Compression of Medical Images Using Principal Component Analysis (PCA). Trends in Applied Sciences Research, 10(1), 68.

  • Haar, A. (1910). Zur theorie der orthogonalen funktionensysteme.Mathematische Annalen, 69(3), 331-371.

  • Claypoole, R. L., Davis, G., Sweldens, W., & Baraniuk, R. G. (1997, November). Adaptive Wavelet Transforms for Image Coding. In Asilomar Conference on Signals, Systems, and Computers.

  • Munoz, A., Ertle, R., & Unser, M. (2002). Continuous wavelet transform with arbitrary scales and O (N) complexity. Signal Processing, 82(5), 749-757.

  • Zeng, L., Jansen, C. P., Marsch, S., Unser, M., & Hunziker, P. R. (2002). Four-dimensional wavelet compression of arbitrarily sized echocardiographic data. IEEE transactions on medical imaging, 21(9), 1179-1187.


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  • Comparative Analysis of PCA, SPIHT and Haar Methods in Medical Image Compression

Abstract Views: 242  |  PDF Views: 4

Authors

Chandrashekhar Kamargaonkar
F.E.T, SSTC, SSGI, Junwani, Bhilai, India
Monisha Sharma
F.E.T, SSTC, SSGI, Junwani, Bhilai, India

Abstract


Compression of medical image has acquired great attention attributable to its raising need to decrease the picture size while not compromising the diagnostically crucial medical data exhibited on the picture. PCA algorithmmay be used to help in image compression. Here PCA algorithm is characterized in two forms i.e. Standard PCA and Block-Based PCA. The block based PCA has 2 extended-PCA algorithms that manipulate the block data of the image are evaluated. The 1st algorithm is referred to as block-by-block PCA wherestandard PCA algorithm is utilized on every block of the picture. In the next algorithm- the block-to-row PCA, all of block data are initially concatenated into a row before the standard PCA algorithm is thereforeutilizedin the remodelled matrix. In this paper, the block based PCA and SPIHT primarily applied on the ROI region whereas General PCA and HAAR wavelet were applied to non-ROI region. An arbitrary shaped segmentation (Manual segmentation) is employed to trace the specified ROI on the image.The SPIHT is being compared with the block based PCA methods in terms of image quality and compression ratio while selecting either general PCA or HAAR wavelet on Non ROI. With this work, it’s observed that block-based PCA performs superior to the SPIHTwith regards toimage quality, producingsimilar compression ratio.


Keywords


Medical Image Compression, Principal Component Analysis (PCA), Block-Based PCA, Compression Ratio, Image Quality, HAAR, SPIHT.

References