The week's pick
Random Articles
Reseach Article
Performance Analysis of Region based Hybrid Compression for Medical Images
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 156 - Number 3 |
| Year of Publication: 2016 |
| Authors: Preeti V. Joshi, C. D. Rawat |
10.5120/ijca2016912402
|
Preeti V. Joshi, C. D. Rawat . Performance Analysis of Region based Hybrid Compression for Medical Images. International Journal of Computer Applications. 156, 3 ( Dec 2016), 24-29. DOI=10.5120/ijca2016912402
Abstract
The high quality of the image produced by CT scan and MRI techniques is required to be maintained in order to avoid wrong diagnosis along with reduced file size. A lossless compression technique retains the image quality but achieves low to moderate compression ratio. Lossy compression, on the other hand, provides higher compression at the cost of degraded image quality. Thus there is need of intermediate method that can satisfy both the requirements. One such approach is the region based hybrid compression in which both lossless and lossy techniques are integrated to obtained better results. Present work comprises region based hybrid compression using Huffman coding and SPIHT. First the diagnostically important region is separated from the rest of the image by a segmentation procedure. The extracted ROI is coded using lossless Huffman coding and SPIHT compression is used for rest of the image also called as background. Performance of the proposed method is evaluated in terms of full reference and no reference parameters.
References
- Wei, Wei-Yi. "An introduction to image compression." National Taiwan University, Taipei, Taiwan, ROC (2008).
- R.C. Gonzalez and R.E. Woods, Digital Image Processing, Pearson Prentice Hall, third edition, 2008.
- https://en.wikipedia.org/wiki/Redundancy_(information_theory)
- Sonal, Dinesh Kumar. "A study of various image compression techniques."COIT, RIMT-IET. Hisar (2007).
- Vijayvargiya, Gaurav, Sanjay Silakari, and Rajeev Pandey. "A Survey: Various techniques of image compression." arXiv preprint arXiv:1311.6877(2013).
- Vemuri, B. C., S. Sahni, F. Chen, C. Kapoor, C. Leonard, and J. Fitzsimmons. "Lossless image compression." (2007).
- Erickson, Bradley J. "Irreversible compression of medical images." Journal of Digital Imaging 15, no. 1 (2002): 5-14.
- Dhawan, Sachin. "A review of image compression and comparison of its algorithms." International Journal of electronics & Communication technology2, no. 1 (2011): 22-26.
- Erickson, Bradley J., Armando Manduca, Patrice Palisson, Kenneth R. Persons, F. Earnest 4th, Vladimir Savcenko, and Nicholas J. Hangiandreou. "Wavelet compression of medical images." Radiology 206, no. 3 (1998): 599-607.
- S Jayaraman, S Esakkirajan, T Veerakumar, Digital Image Processing, Tata McGraw Hill Education pvt. Ltd., Second reprint, 2010.
- Walker, James S., and Truong Q. Nguyen. "Wavelet-based image compression." Sub-chapter of CRC Press book: Transforms and Data Compression (2001).
- https://en.wikipedia.org/wiki/Medical_imaging
- Iniewski, Krzysztof, ed. Medical imaging: Principles, detectors, and Electronics. John Wiley & Sons, 2009.
- Tonarelli, Lorena. "Magnetic resonance imaging of brain tumor."CEwebsource. com (2013).
- Jangbari, Palak, and Dhruti Patel. "Review on Region of Interest Coding Techniques for Medical Image Compression." International Journal of Computer Applications 134, no. 10 (2016): 1-5.
- Brindha, B., and G. Raghuraman. "Region based lossless compression for digital images in telemedicine application." In Communications and Signal Processing (ICCSP), 2013 International Conference on, pp. 537-540. IEEE, 2013.
- Rehna, V. J., and M. K. Jeya Kumar. "Hybrid approach to image coding: A review." International Journal of Advanced Computer Science and Applications 2, no. 7 (2011).
- Saluja, Nitin, Anoop Kumar, Dr Amisha, and Rajesh Khanna. "CROPPING IMAGE IN RECTANGULAR, CIRCULAR, SQUARE AND TRIANGULAR FORM USING MATLAB."
- Somefun Olawale M., Adebayo Adewale O. “Evaluation of Dominant Text Data Compression Techniques.” International Journal of Application or Innovation in Engineering and Management 3,no.6 (2014).
- Sharma, Mamta. "Compression using Huffman coding." IJCSNS International Journal of Computer Science and Network Security 10, no. 5 (2010): 133-141.
- Mathur, Mridul Kumar, Seema Loonker, and Dheeraj Saxena. "Lossless Huffman coding technique for image compression and reconstruction using binary trees." International Journal of Computer Technology and Applications3, no. 1 (2012).
- Tavakoli, Nassrin. "Lossless compression of medical images." In Computer-Based Medical Systems, 1991. Proceedings of the Fourth Annual IEEE Symposium, pp. 200-207. IEEE, 1991.
- Sapkal, A. M., and V. K. Bairagi. "Selection of wavelets for medical image compression." International Level IEEE Explorer, IEEE Sponsored ACT2009 (28-29 Dec. 2009) (2009).
- Said, Amir, and William A. Pearlman. "A new, fast, and efficient image codec based on set partitioning in hierarchical trees." IEEE Transactions on circuits and systems for video technology 6, no. 3 (1996): 243-250.
- Wang, Zhou, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. "Image quality assessment: from error visibility to structural similarity." IEEE transactions on image processing 13, no. 4 (2004): 600-612.
- Zhang, Lin, Lei Zhang, Xuanqin Mou, and David Zhang. "FSIM: a feature similarity index for image quality assessment." IEEE transactions on Image Processing 20, no. 8 (2011): 2378-2386.
- Wang, Zhou, and Alan C. Bovik. "A universal image quality index." IEEE signal processing letters 9, no. 3 (2002): 81-84.
- Mittal, Anish, Anush K. Moorthy, and Alan C. Bovik. "Blind/referenceless image spatial quality evaluator." In 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pp. 723-727. IEEE,2011.
Index Terms
Keywords