[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.3745/JIPS.02.0052

Adaptive Medical Image Compression Based on Lossy and Lossless Embedded Zerotree Methods

Elhannachi, Sid Ahmed (Dept. of Computer Science, Faculty of Mathematics and Computer Science, University of Science and Technology of Oran)
Benamrane, Nacera (Dept. of Computer Science, Faculty of Mathematics and Computer Science, University of Science and Technology of Oran)
Abdelmalik, Taleb-Ahmed (Dept. of Automatic, University of Valenciennes and Hainaut-Cambresis (UVHC))

Publication Information

Journal of Information Processing Systems / v.13, no.1, 2017 , pp. 40-56 More about this Journal

Abstract

Since the progress of digital medical imaging techniques, it has been needed to compress the variety of medical images. In medical imaging, reversible compression of image's region of interest (ROI) which is diagnostically relevant is considered essential. Then, improving the global compression rate of the image can also be obtained by separately coding the ROI part and the remaining image (called background). For this purpose, the present work proposes an efficient reversible discrete cosine transform (RDCT) based embedded image coder designed for lossless ROI coding in very high compression ratio. Motivated by the wavelet structure of DCT, the proposed rearranged structure is well coupled with a lossless embedded zerotree wavelet coder (LEZW), while the background is highly compressed using the set partitioning in hierarchical trees (SPIHT) technique. Results coding shows that the performance of the proposed new coder is much superior to that of various state-of-art still image compression methods.

Keywords

LEZW; Medical Images; ROI; RDCT; SPIHT;

Citations & Related Records

Reference

1	D. V. Babu and N. R. Alamelu, "Wavelet based medical image compression using ROI EZW," International Journal of Recent Trends in Engineering, vol. 1, no. 3, pp. 97-100, 2009.
2	H. Jiang, Y. Zhang, S. Shi, Z. Ma, A. Dong, Q. Tong, B. Yang, and L. Zhang, "An image ROI compression algorithm based on hybrid fractal model," Journal of Information & Computational Science, vol. 11, no. 4, pp. 1201-1208, 2014. DOI
3	T. M. Rajkumar and M. V. Latte, "An efficient ROI encoding based on LSK and fractal image compression," The International Arab Journal of Information Technology, vol. 12, no. 3, pp. 220-228, 2015.
4	J. Mielikainen and P. Toivanen, "Clustered DPCM for the lossless compression of hyperspectral images," IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 12, pp. 2943-2946, 2003. DOI
5	X. Wu and N. Memon, "Context-based lossless interband compression-extending CALIC," IEEE Transactions on Image Processing, vol. 9, no. 6, pp. 994-1001, 2000. DOI
6	E. Magli, G. Olmo, and E. Quacchio, "Optimized onboard lossless and near-lossless compression of hyperspectral data using CALIC," IEEE Geoscience and Remote Sensing Letters, vol. 1, no. 1, pp. 21-25, 2004. DOI
7	A. Ouafi, A. T. Ahmed, Z. E. Baarir, and A. Zitouni, "A modified embedded zerotree wavelet (MEZW) algorithm for image compression," Journal of Mathematical Imaging and Vision, vol. 30, no. 3, pp. 298-307, 2008. DOI
8	N. M. A. Ahmed and A. M. A. Brifcani, "A new modified embedded zerotree wavelet approach for image coding (NMEZW)," International Journal of Scientific & Engineering Research, vol. 4, no. 3, pp. 1-11, 2013.
9	A. Sattar, "The medical image compression with embedded zerotree wavelet," International Journal of Science, Spirituality, Business and Technology, vol. 1, no. 2, pp. 67-70, 2013.
10	Z. Xiong, O. G. Guleryuz, and M. T. Orchard, "A DCT-based embedded image coder," IEEE Signal Processing Letters, vol. 3, no. 11, pp. 289-290, 1996. DOI
11	D. M. Monro and G. J. Dickson, "Zerotree Coding of DCT coefficients," in Proceedings of the IEEE International Conference on Image Processing, Santa Barbara, CA, 1997, pp. 625-628.
12	Y. A. Jeong and C. K. Cheong, "A DCT-based embedded image coder using wavelet structure of DCT for very low bit rate video codec," IEEE Transactions on Consumer Electronics, vol. 44, no. 3, pp. 500-507, 1998. DOI
13	C. K. Cheong, K. S. Cho, and S. W. Lee, "Significance tree image sequence coding with DCT-based pyramid structure," in Proceedings of the IEEE International Conference on Image Processing, Vancouver, Canada, 2000, pp. 859-862.
14	G. Plonka and M. Tasche, "Invertible integer DCT algorithms," Applied and Computational Harmonic Analysis, vol. 15, no. 1, pp. 70-88, 2003. DOI
15	V. Britanak, P. C. Yip, and K. R. Rao, Discrete Cosine and Sine Transforms: General Properties, Fast Algorithms and Integer Approximations. New York: Academic Press, 2007.
16	K. Komatsu and K. Sezaki, "Reversible discrete cosine transform," in Proceedings of the International Conference on Acoustic, Speech, Signal Processing, Seattle, WA, 1998, pp. 1769-1772.
17	Z. He and M. Bystrom, "Improved conversion from DCT blocks to integer cosine transform blocks in H.264/AVC," IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 6, pp. 851-857, 2008. DOI
18	J. Liang and T. D. Tran, "Fast multiplierless approximations of the DCT with the lifting scheme," IEEE Transactions on Signal Processing, vol. 49, no. 12, pp. 3032-3044, 2001. DOI
19	W. Sweldens, "The lifting scheme: a custom-design construction of biorthogonal wavelets," Applied and Computational Harmonic Analysis, vol. 3, no. 2, pp. 186-200, 1996. DOI
20	S. Fukuma, K. Ohyama, M. Iwahashi, and N. Kambayashi, "Lossless 8-point fast discrete cosine transform using lossless Hadamard transform," Technical Report No. IEICE-DSP-99-103, 1999.
21	Y. J. Chen, S. Oranintara, and T. Nguyen, "Integer discrete cosine transform (IntDCT)," in Proceedings of the 2nd International Conference on Information, Communications and Signal Processing, Singapore, 1999.
22	T. D. Tran, "The BinDCT: fast multiplierless approximation of the DCT," IEEE Signal Processing Letters, vol. 7, no. 6, pp. 141-144, 2000. DOI
23	J. Liang and T. D. Tran, "Fast multiplierless approximations of the DCT with the lifting scheme," IEEE Transactions on Signal Processing, vol. 49, no. 12, pp. 3032-3044, 2001. DOI
24	A. Said and W. A. Pearlman, "A new fast efficient image codec based on set partitioning in hierarchical trees," IEEE Transactions on Circuits and System Video Technology, vol. 6, no. 3, pp. 243-250, 1996. DOI
25	S. Udomsiri and M. Iwahashi, "Comparative study on recent integer DCTs," World Academy of Science, Engineering and Technology, International Scholarly and Scientific Research & Innovation, vol. 2, no. 12, pp. 2656-2662, 2008.
26	J. M. Shapiro, "Embedded image coding using zerotrees of wavelet coefficients," IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3445-3462, 1993. DOI
27	V. J. Rehna and M. K. Jeya Kumar, "Wavelet based image coding schemes: a recent survey," International Journal on Soft Computing, vol. 3, no. 3, pp. 101-118, 2012. DOI
28	M. D. Baylon and S. J. Lim, "Transform/subband analysis and synthesis of signals," Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, Technical Report No. RLE-559, 1990.
29	A. Kaluanpur, V. P. Neklesa, C. R. Taylor, A. R. Daftary, and J. A. Brink, "Evaluation of JPEG and wavelet compression of body CT images for direct digital teleradiologic transmission," Radiology, vol. 217, no. 3, pp. 772-779, 2000. DOI
30	T. V. Ramabadran and K. Chen, "Efficient compression of medical images through arithmetic coding," in Proceedings of SPIE 1234: Medical Imaging. Bellingham, WA: International Society for Optics and Photonics, 1990, pp. 761-775.
31	H. Yang, M. Long, and H. M. Tai, "Region-of-interest image coding based on EBCOT," IEEE Proceedings-Visual Image Signal Process, vol. 152, no. 5, pp. 590-596, 2005. DOI
32	J. Askelof, M. L. Carlander, and C. Christopoulos, "Regions of interest coding in JPEG2000," Signal Processing: Image Communication, vol. 17, no. 1, pp. 105-111, 2002. DOI
33	M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies, "Image coding using wavelet transform," IEEE Transactions on Image Processing, vol. 1, no. 2, pp. 205-220, 1992. DOI
34	M. J. Weinberger, G. Seroussi, and G. Sapiro, "From LOCO-I to the JPEG-LS standard," in Proceedings of the International Conference on Image Processing, Kobe, Japan, 1992, pp. 68-72.
35	A. Skodras, C. Christopoulos, and T. Ebrahimi, "The JPEG2000 still image compression standard," IEEE Signal Processing Magazine, vol. 18, no. 5, pp. 36-58, 2001. DOI
36	D. Taubman, "EBCOT: embedded block coding with optimized truncation," Technical Report N1020R, ISO/IEC JTC1/SC29/WG1, 1998.
37	P. G. Tahoces, J. R. Varela, M. J. Lado, and M. Souto, "Image compression: maxshift ROI encoding options in JPEG2000," Computer Vision and Image Understanding, vol. 109, no. 2, pp. 139-145, 2008. DOI
38	H. Lin, J. Si, and G. P. Abousleman, "Knowledge-based hierarchical region-of-interest detection," in Proceedings of the International Conference on Acoustics, Speech and Signal Processing, Orlando, FL, 2002, pp. 13-17.
39	M. Haindl and S. Mikes, "Texture segmentation benchmark," in Proceedings of the 19th International Conference on Pattern Recognition, Tampa, FL, 2008, pp. 1-4.
40	D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics," in Proceedings of the 8th International Conference on Computer Vision, Sydney, Australia, 2001, pp. 416-423.
41	G. P. Nguyen and M. Worring, "An user based framework for salient detail extraction," in Proceedings of the IEEE International Conference on Multimedia and Expo, Taipei, Taiwan, 2004, pp. 1831-1834.
42	JPEG2000 Part I Final Committee draft version 1.0 (ISO/IEC JTC 1/SC 29/WG 1 (ITU-T SG8) [Online]. Available: https://www.ics.uci.edu/-dan/class/267/papers/jpeg2000.pdf.
43	H. Min, C. Zhang, J. Lu, and B. Zhou, "A multi ROIs medical image compression with edge feature preserving," in Proceedings of the 3rd International Conference on Intelligent System and Knowledge Engineering, Beijing, China, 2008, pp. 1075-1080.
44	N. D. Londhe and S. Chawre, "Region based coding of liver cancer CT images," Canadian Journal on Image Processing and Computer Vision, vol. 4, no. 1, pp. 9-15, 2013.
45	M. Firoozbakht, J. Dehmeshki, M. Martini, Y. Ebrahimdoost, H. Amin, M. Dehkordi, A. Youannic, and S. D. Qanadli, "Compression of digital medical images based on multiple regions of interest," in Proceedings of the 4th International Conference on Digital Society, St. Maarten, Netherlands Antilles, 2010, pp. 260-263.
46	R. F. Larico,Y. Iano, R. S. Higa, R. Arthur, and O. Saotome, "Generalized region of interest coding applied to SPIHT," Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Telecommunications (JSAT), vol. 2012, pp. 23-31, 2012.