Abstract
To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively.
Similar content being viewed by others
References
Wiegand T, Sullivan GJ, Bjøntegaard G et al (2003) Overview of the H. 264/AVC video coding standard. IEEE Trans Circuits Syst Video Technol 13(7):560–576
Escoda OD, Yin P, Dai C, Li X (2007) Geometry-adaptive block partitioning for video coding. In: ICASSP 2007, IEEE international conference on, pp. 657–660
Yuan Y, Kim IK, Zheng X et al (2012) Quadtree based non-square block structure for inter frame coding in high efficiency video coding. IEEE Trans Circuits Syst Video Technol 22(12):1707–1719
Kondo S, Sasai H (2005) A motion compensation technique using sliced blocks in hybrid video coding. In: ICIP 2005, IEEE international conference on, 2: II-305–8
Hung EM, De Queiroz RL, Mukherjee D (2006) On macroblock partition for motion compensation. In: ICIP 2006, IEEE international conference on, 1697–1700
Wu X, Memon N (1997) Context-based, adaptive, lossless image coding. IEEE Trans Commun 45(4):437–444
Weinberger MJ, Seroussi G, Sapiro G (2000) The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS. IEEE Trans Image Process 9(8):1309–1324
Li X, Orchard MT (2001) Edge-directed prediction for lossless compression of natural images. IEEE Trans Image Process 10(6):813–817
Tiwari AK, Kumar RR (2008) Least squares based optimal switched predictors for lossless compression of images. In: Multimedia and expo 2008, IEEE international conference on, pp: 1129–1132
Li X (2006) Least-square prediction for backward adaptive video coding. EURASIP J Appl Signal Process, pp: (1):090542.
Li X (2007) Video processing via implicit and mixture motion models. IEEE Trans Circuits Syst Video Technol 17(8):953–963
Matsumura S, Maezawa T, Takago D et al (2007) Least-square-based block adaptive prediction approach for lossless image coding. In: ECCTD 2007, 18th European conference on, pp: 188–191
Muhit AA, Pickering MR, Frater MR et al (2010) Video coding using elastic motion model and larger blocks. IEEE Trans Circuits Syst Video Technol 20(5):661–672
Lee JO, Jang SK, Chen QS et al (2007) An efficient frame rate up-conversion method for mobile phone with projection functionality. IEEE Trans Consum Electr 53(4):1615–1621
Song X, Huang Q, Chang S et al (2016) Novel near-lossless compression algorithm for medical sequence images with adaptive block-based spatial prediction. J Digit Imaging 29(6):706–715
Muhit AA, Pickering MR, Frater MR et al (2012) Video coding using fast geometry-adaptive partitioning and an elastic motion model. J Vis Commun Image Repersent 23(1):31–41
Wang Q, Ji X, Sun MT et al (2013) Complexity reduction and performance improvement for geometry partitioning in video coding. IEEE Trans Circuits Syst Video Technol 23(2):338–352
Ferreira RU, Hung EM, De Queiroz RL et al (2009) Efficiency improvements for a geometric-partition-based video coder. In: ICIP 2009, IEEE international conference on, pp: 1009–1012
Gonzalez RC, Woods RE (2002) Digital image processing, Prentice Hall
Kau LJ, Lin YP (2007) Least-squares-based switching structure for lossless image coding. IEEE Trans Circuits Syst I: Regul Pap 54(7):1529–1541
Computer Vision Group [online]. Available at: http://decsai.ugr.es/cvg/index2.php. Accessed Aug 2011
Aiazzi B, Alparone L, Baronti S (2002) Near-lossless image compression by relaxation-labelled prediction. Signal Process 82(11):1619–1631
Matsuda I, Shirai N, Itoh S (2003) Lossless coding using predictors and arithmetic code optimized for each image. Visual Content Processing and Representation. Springer, Berlin Heidelberg, pp 199–207
Skodras A, Christopoulos C, Ebrahimi T (2001) The JPEG 2000 still image compression standard. IEEE Signal Process Mag 18(5):36–58
Kau LJ, Lin YP (2005) Adaptive lossless image coding using least squares optimization with edge-look-ahead. IEEE Trans Circuits Syst II: Express Briefs 52(11):751–755
Khormuji MK, Bazrafkan M (2016) A novel sparse coding algorithm for classification of tumors based on gene expression data. Med Biol Eng Compu 54(6):869–876
Funding
This work was supported by the National Natural Science Foundation of China (61404094, 61574102, 61471275, 61625305); the Natural Science Foundation of Hubei Province, China (2014CFB694); and the Fundamental Research Fund for the Central Universities, Wuhan University (2042015kf0174).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Song, X., Huang, Q., Chang, S. et al. Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction. Med Biol Eng Comput 56, 957–966 (2018). https://doi.org/10.1007/s11517-017-1741-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-017-1741-8