Skip to main content
Log in

Performance analysis of a new fractal compression method for medical images based on fixed partition

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Applications of image compression have been gaining importance over the years in medical fields. The medical imaging modalities produced a large amount of information on all levels of hospital care. This information, in the form of images, needs to be stored for future references. While compressing the medical images, it is necessary to maintain high diagnostic quality with a high compression rate. Several image compression approaches have been developed towards the direction of the storage space problem in such a way that the doctors accurately and reliably diagnose the patient’s diseases from the reconstructed image after decompression. A high compression ratio is required to reduce the storage space due to the large size of medical images. Fractal image compression is a lossy technique to compress the image in a coded form instead of pixels and is differentiated by its long encoding time with a high compression ratio, resolution-independent, fast decoding, and self-similarity. The main purpose of this paper is to present a comparative performance study of the three coding schemes of fractal compression for grayscale medical images based on fixed partition. The first two coding schemes are based on the pixel-pattern measure and the third scheme is a proposed method based on the fractal dimension for complexity measure of range and domain blocks. The comparative study included encoding time, peak signal to noise ratio, and compression ratio, as a result, has been accomplished.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Barnsley MF (1993) Fractal everywhere, 2nd edn. Academic Press, New York

    Google Scholar 

  2. Jacquin AE (1992) Image coding based on a fractal theory of iterated contractive image. IEEE Trans Image Process 1(1):18–30

    Article  Google Scholar 

  3. Frigaard C, Frigaard GC, Frigaard FC (1995) Fast fractal 2D/3D image compression. Institute of Electronic System Aalborg University, Laboratory of Image Analysis

    MATH  Google Scholar 

  4. Huang ZK, Li PW, Wang SQ, Hou LY (2010) Using FCM for color texture segmentation based multi-scale image fusion, IEEE Proc. Int. Conf. on e-Education, e-Business, e-Management, and e-Learning: IC4E 2010, Sanya, China

  5. Li L, Li M, Lu YM (2009) Texture classification and segmentation based on bi-dimensional empirical mode decomposition and fractal dimension. IEEE Proc 1st Int Workshop Edu Technol Comput Sci: ETCS’ 09, Wuhan, China 2:574–577

    Google Scholar 

  6. Liu Y, Zhang M, Yuan F (2010) Fast fractal image retrieval algorithm based on contiguous-matches. IEEE Proc Int Conf Mach Learn Cybern: ICMLC 2010, Qingdao, China 4:2047–2052

    Google Scholar 

  7. Tan T, Yan H (2000) Object recognition using fractal neighbor distance: eventual convergence and recognition rates. IEEE Proc 15th Int Conf Pattern Recognit: ICPR 2000, Barcelona, Spain 2:785–788

    Article  Google Scholar 

  8. Yao M, Yi WS, Shen B, Dai HH (2003) An image retrieval system based on fractal dimension. J Zhejiang Univ Sci 4(4):421–425

    Article  Google Scholar 

  9. Chaudhuri BB, Sarkar N (1995) Texture segmentation using fractal dimension. IEEE Trans Pattern Anal Mach Intell 17(1):72–77

    Article  Google Scholar 

  10. Hsu T, Hu KJ (2008) Multi-resolution texture segmentation using fractal dimension. IEEE Int Conf Comp Sci Softw Eng 6:201–204

    Google Scholar 

  11. Nadia MG, Al-Saidi NM, Aqeel HA (2017) Towards enhancing of fractal image compression performance via block complexity sign in or purchase. Annu Conf New Trends Inf Commun Technol Appl (NTICT)

  12. Nandi U, Mandal JK, Santra S, Nandi S (2015) Fractal image compression with quadtree partitioning and a new fast classification strategy. Third Int Conf Comput, Commun, Control Inf Technol (C3IT):1–4

  13. Ismail BM, Reddy TB, Reddy BE (2016) Spiral architecture based hybrid fractal image compression. Int Conf Electr, Electron, Commun, Comput Optim Tech (ICEECCOT):21–26

  14. Chaurasia V, Gumasta RK, Kurmi Y (2017) Fractal image compression with optimized domain pool size. Int Conf Innov Electron, Signal Process Commun (IESC):209–212.

  15. Sunil H, Hiremath SGA (2018) Combined scheme of pixel and block level splitting for medical image compression and reconstruction. Elsevier Alexandria Eng J 57(2):767–772

    Article  Google Scholar 

  16. Pal SK, King RA (1981) Image enhancement using smoothing with fuzzy sets. IEEE Trans Syst, Man, Cybern 11(7):494–501

    Article  Google Scholar 

  17. Bas OY, Erkmen AM (1995) A new approach to the fractal based description of natural textures: the fuzzy fractal dimension. IEEE Int Conf Syst, Man Cybern, Intell Syst 21st Century 4:3232–3237

    Google Scholar 

  18. Choy SK, Lam SY, Yu KW, Lee WY, Leung KT (2017) Fuzzy model-based clustering and its application in image segmentation. Pattern Recogn 68:141–157

    Article  Google Scholar 

  19. Panigrahy M, Chakrabarti I, Dhar AS (2016) Low-delay parallel architecture for fractal image compression. Springer Sci, Circuits Syst Signal Process 35:897–917

    MathSciNet  MATH  Google Scholar 

  20. Liu S, Zhang Z, Qi L, Ma M (2016) A fractal image encoding method based on statistical loss used in agricultural image compression. Multimed Tools Appl 75:15525–15536

    Article  Google Scholar 

  21. Chaurasia V, Chaurasia V (2016) Statistical feature extraction based technique for fast fractal image compression. J Vis Commun Image Represent 41:87–95

    Article  Google Scholar 

  22. Jaferzadeh K, Moon I, Gholami S (2017) Enhancing fractal image compression speed using local features for reducing search space. Pattern Anal Appl 20:1119–1128

    Article  MathSciNet  Google Scholar 

  23. Li G, Li S (2017) Fast fractal image encoding algorithm based on coefficient of variation feature. Smart Graph: 13th Int Symp 9317:175–183

    Article  Google Scholar 

  24. Roy SK, Kumar S, Chanda B, Chaudhuri BB, Banerjee S (2018) Fractal image compression using upper bound on scaling parameter. Chaos Solitons Fractals 106:16–22

    Article  MathSciNet  Google Scholar 

  25. Sheeba K, Rahiman MA (2019) Gradient based fractal image compression using cayley table. Measurement 140:126–132

    Article  Google Scholar 

  26. Han JS (2007) Fast fractal image compression by pixel peaks and valleys classification. J Commun Comput 4(3):69–75

    Google Scholar 

  27. Biswas AK, Karmakar S, Sharma S, Kowar MK (2013) Fast fractal image compression by pixels pattern using fuzzy c-means. Int J Eng Res Kuwait 1(3):109–121

    Google Scholar 

  28. Saupe D (1996) The futility of square isometric in fractal image compression conference. Proc Int Conf Image Process 1:161–164

    Article  Google Scholar 

  29. Biswas AK, Karmakar S, Sharma S (2016) Fuzzy based fractal compression for medical images by using pixel pattern. Int J Imaging Robot 16(2):58–65

    Google Scholar 

  30. Biswas AK, Karmakar S, Sharma S (2020) Effectiveness of the fractal dimension based classification methods for fractal compression of medical images. 2020 First Int Conf Power Control Comput Technol, Raipur, India 24:155–160

    Google Scholar 

  31. Wang G, Bovic AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  32. Jaferzadeh K, Kaini K, Mozaffari S (2012) Accelerating of fractal image compression using fuzzy clustering and discrete-cosine transform-based metric. IET Image Proc 6(7):1024–1030

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sanjeev Karmakar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Biswas, A.K., Karmakar, S. & Sharma, S. Performance analysis of a new fractal compression method for medical images based on fixed partition. Int. j. inf. tecnol. 14, 411–419 (2022). https://doi.org/10.1007/s41870-020-00598-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-020-00598-3

Keywords

Navigation