Multimedia Tools and Applications

, Volume 76, Issue 3, pp 3537–3555 | Cite as

Accelerating compute intensive medical imaging segmentation algorithms using hybrid CPU-GPU implementations

  • Mohammad A. Alsmirat
  • Yaser Jararweh
  • Mahmoud Al-Ayyoub
  • Mohammed A. Shehab
  • Brij B. Gupta
Article

Abstract

Medical image processing is one of the most famous image processing fields in this era. This fame comes because of the big revolution in information technology that is used to diagnose many illnesses and saves patients lives. There are many image processing techniques used in this field, such as image reconstructing, image segmentation and many more. Image segmentation is a mandatory step in many image processing based diagnosis procedures. Many segmentation algorithms use clustering approach. In this paper, we focus on Fuzzy C-Means based segmentation algorithms because of the segmentation accuracy they provide. In many cases, these algorithms need long execution times. In this paper, we accelerate the execution time of these algorithms using Graphics Process Unit (GPU) capabilities. We achieve performance enhancement by up to 8.9x without compromising the segmentation accuracy.

Keywords

Fuzzy C-Means Possibilistic C-Means CUDA Medical image processing Image segmentation 

References

  1. 1.
    Cudafy.net (2015). https://cudafy.codeplex.com/
  2. 2.
    Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T (2002) A modified fuzzy c-means algorithm for bias field estimation and segmentation of mri data. IEEE Trans Med Imaging 21(3):193– 199CrossRefGoogle Scholar
  3. 3.
    Al-Ayyoub M, Abu-Dalo AM, Jararweh Y, Jarrah M, Al Sa’d M (2015) A gpu-based implementations of the fuzzy c-means algorithms for medical image segmentation. J Supercomput 71(8):3149–3162CrossRefGoogle Scholar
  4. 4.
    Alawneh K, Al-dwiekat M, Alsmirat M, Al-Ayyoub M (2015) Computer-aided diagnosis of lumbar disc herniation. In: 6th International Conference on Information and Communication Systems (ICICS). IEEE, pp 286–291Google Scholar
  5. 5.
    Bezdek JC, Ehrlich R, Full W (1984) Fcm: The fuzzy c-means clustering algorithm. Comput Geosci 10(2-3):191–203CrossRefGoogle Scholar
  6. 6.
    Cook S (2012) CUDA programming: a developer’s guide to parallel computing with GPUs NewnesGoogle Scholar
  7. 7.
    Eklund A, Dufort P, Forsberg D, LaConte SM (2013) Medical image processing on the gpu–past, present and future. Med Image Anal 17(8):1073–1094CrossRefGoogle Scholar
  8. 8.
    Hwang C, Rhee FCH (2007) Uncertain fuzzy clustering: interval type-2 fuzzy approach to c-means. IEEE Trans Fuzzy Syst 15(1):107–120CrossRefGoogle Scholar
  9. 9.
    İçer S (2013) Automatic segmentation of corpus collasum using gaussian mixture modeling and fuzzy c means methods. Computer Methods Programs Biomed 112 (1):38–46CrossRefGoogle Scholar
  10. 10.
    Ji Z, Xia Y, Sun Q, Chen Q, Feng D (2014) Adaptive scale fuzzy local gaussian mixture model for brain mr image segmentation. Neurocomputing 134:60–69CrossRefGoogle Scholar
  11. 11.
    Krishnapuram R, Keller JM (1996) The possibilistic c-means algorithm: insights and recommendations. IEEE Trans Fuzzy Syst 4(3):385–393CrossRefGoogle Scholar
  12. 12.
    Lee VW, Kim C, Chhugani J, Deisher M, Kim D, Nguyen AD, Satish N, Smelyanskiy M, Chennupaty S, Hammarlund P, Singhal R, Dubey P (2010) Debunking the 100x gpu vs. cpu myth: An evaluation of throughput computing on cpu and gpu. In: Proceedings of the 37th Annual International Symposium on Computer Architecture, ISCA ’10. ACM, NY, USA, pp 451–460. doi:10.1145/1815961.1816021
  13. 13.
    Lukas L, Devos A, Suykens JA, Vanhamme L, Howe FA, Majós C, Moreno-Torres A, Van der Graaf M, Tate AR, Arús C et al (2004) Brain tumor classification based on long echo proton mrs signals. Artif Intell Med 31(1):73–89CrossRefGoogle Scholar
  14. 14.
    Pan L, Gu L, Xu J (2008) Implementation of medical image segmentation in cuda. In: 2008 International Conference on Information Technology and Applications in Biomedicine. IEEE, pp 82–85Google Scholar
  15. 15.
    Qiu C, Xiao J, Yu L, Han L, Iqbal MN (2013) A modified interval type-2 fuzzy c-means algorithm with application in mr image segmentation. Pattern Recogn Lett 34(12):1329–1338CrossRefGoogle Scholar
  16. 16.
    Rhee FCH, Hwang C (2001) A type-2 fuzzy c-means. Clustering Algorithm 4:1926–1929Google Scholar
  17. 17.
    Rowińska Z, Gocławski J (2012) Cuda based fuzzy c-means acceleration for the segmentation of images with fungus grown in foam matrices. Image Process Commun 17(4):191–200Google Scholar
  18. 18.
    Rubio E, Castillo O (2014) Interval type-2 fuzzy clustering algorithm using the combination of the fuzzy and possibilistic c-mean algorithms. In: IEEE Conference on Norbert Wiener in the 21st Century (21CW). IEEE, pp 1–6Google Scholar
  19. 19.
    Shehab MA, Al-Ayyoub M, Jararweh Y (2015) Improving fcm and t2fcm algorithms performance using gpus for medical images segmentation. In: 6th International Conference on Information and Communication Systems (ICICS). IEEE, pp 130–135Google Scholar
  20. 20.
    Sikka K, Sinha N, Singh PK, Mishra AK (2009) A fully automated algorithm under modified fcm framework for improved brain mr image segmentation. Magn Reson Imaging 27(7):994–1004CrossRefGoogle Scholar
  21. 21.
    Tan KS, Isa NAM (2011) Color image segmentation using histogram thresholding–fuzzy c-means hybrid approach. Pattern Recogn 44(1):1–15MATHCrossRefGoogle Scholar
  22. 22.
    Tang J (2010) A color image segmentation algorithm based on region growing. In: 2nd international conference on Computer engineering and technology (iccet), vol 6. IEEE, pp V6–634Google Scholar
  23. 23.
    Walters JP, Balu V, Kompalli S, Chaudhary V (2009) Evaluating the use of gpus in liver image segmentation and hmmer database searches. In: IPDPS 2009. IEEE International Symposium on Parallel and Distributed Processing. IEEE, pp 1–12Google Scholar
  24. 24.
    Wang H, Fei B (2009) A modified fuzzy c-means classification method using a multiscale diffusion filtering scheme. Med Image Anal 13(2):193–202MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Departement of Computer ScienceJordan University of Science and TechnologyIrbidJordan
  2. 2.National Institute of Technology KurukshetraHaryanaIndia

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