Skip to main content

Advertisement

SpringerLink
Log in

An automatic segmentation method of a parameter-adaptive PCNN for medical images

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Since pre-processing and initial segmentation steps in medical images directly affect the final segmentation results of the regions of interesting, an automatic segmentation method of a parameter-adaptive pulse-coupled neural network is proposed to integrate the above-mentioned two segmentation steps into one. This method has a low computational complexity for different kinds of medical images and has a high segmentation precision.

Methods

The method comprises four steps. Firstly, an optimal histogram threshold is used to determine the parameter \(\alpha \) for different kinds of images. Secondly, we acquire the parameter \(\beta \) according to a simplified pulse-coupled neural network (SPCNN). Thirdly, we redefine the parameter V of the SPCNN model by sub-intensity distribution range of firing pixels. Fourthly, we add an offset \(A\times S_{\mathrm{off}}\) to improve initial segmentation precision.

Results

Compared with the state-of-the-art algorithms, the new method achieves a comparable performance by the experimental results from ultrasound images of the gallbladder and gallstones, magnetic resonance images of the left ventricle, and mammogram images of the left and the right breast, presenting the overall metric UM of 0.9845, CM of 0.8142, TM of 0.0726.

Conclusion

The algorithm has a great potential to achieve the pre-processing and initial segmentation steps in various medical images. This is a premise for assisting physicians to detect and diagnose clinical cases.

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

Access this article

Log in via an institution

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Hareendranathan A, Mabee M, Punithakumar K, Noga M, Jaremko J (2016) A technique for semiautomatic segmentation of echogenic structures in 3D ultrasound, applied to infant hip dysplasia. Int J Comput Assist Radiol Surg 11(1):31–42. doi:10.1007/s11548-015-1239-5

    Article  PubMed  Google Scholar 

  2. Nobel J, Boukerroui D (2006) Ultrasound image segmentation: a survey. IEEE Trans Med Imaging 25(8):987–1010. doi:10.1109/TMI.2006.877092

    Article  Google Scholar 

  3. Gupta J, Gosain B, Kaushal S (2010) A comparison of two algorithms for automated stone detection in clinical B-mode ultrasound images of the abdomen. Int J Clin Monit Comput 24(5):341–362. doi:10.1007/s10877-010-9254-0

    Article  Google Scholar 

  4. Lian J, Ma Y, Ma Y, Shi B, Liu J, Yang Z, Guo Y (2017) Automatic gallbladder and gallstone regions segmentation in ultrasound image. Int J Comput Assist Radiol Surg. doi:10.1007/s11548-016-1515-z

    Google Scholar 

  5. Yang X, Ye X, Slabaugh G (2015) Multilabel region classification and semantic linking for colon segmentation in CT colonography. IEEE Trans B Biomed Eng 62(3):948–959. doi:10.1109/TBME.2014.2374355

    Article  Google Scholar 

  6. Zou X, Li Z (2016) TV-based correction for beam hardening in computed tomography. J Med Imaging Heal Inf 6(7):1701–1707. doi:10.1166/jmihi.2016.1875

    Article  Google Scholar 

  7. Dandin O, Teomete U, Osman O, Tulum G, Ergin T, Sabuncuoglu M (2016) Automated segmentation of the injured spleen. Int J Comput Assist Radiol Surg 11(3):351–368. doi:10.1007/s11548-015-1288-9

    Article  PubMed  Google Scholar 

  8. Hanaoka S, Masutani Y, Nenoto M, Nomura Y, Miki S, Yoshikawa T, Hayashi N, Ohtomo K, Shimizu A (2017) Landmark-guided diffeomorphic demons algorithm and its application to automatic segmentation of the whole spine and pelvis in CT images. Int J Comput Assist Radiol Surg 12(3):413–430. doi:10.1007/s11548-016-1507-z

    Article  PubMed  Google Scholar 

  9. Wang Z, Zhang X, Dou W, Zhang M, Chen H, Lu M, Li S (2016) Best Window Width Determination and Glioma Analysis Application of Dynamic Brain Network Measure on Resting-State Functional Magnetic Resonance Imaging. J Med Imaging Heal Inf 6(7):1735–1740. doi:10.1166/jmihi.2016.1881

    Article  Google Scholar 

  10. Ma Y, Wang L, Ma Y, Dong M, Du S, Sun S (2016) Novel automatic segmentation of left ventricle in cardiac cine MR images. Int J Comput Assist Radiol Surg 11(11):1951–1964. doi:10.1007/s11548-016-1429-9

    Article  PubMed  Google Scholar 

  11. Faghih Roohi S, Aghaeizadeh Zoroofi R (2013) 4D statistical shape modeling of the left ventricle in cardiac MR images. Int J Comput Assist Radiol Surg 8(3):335–351. doi:10.1007/s11548-012-0787-1

    Article  PubMed  Google Scholar 

  12. Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–165. doi:10.1117/1.1631315

    Article  Google Scholar 

  13. Lee S, Chung S, Park R (1990) A comparative performance study of several global thresholding techniques for segmentation. Comput Vis Graph Image Process 52(2):171–190. doi:10.1016/0734-189X(90)90053-X

    Article  Google Scholar 

  14. Feng Y, Zhao H, Li X, Zhang X, Li H (2016) A multi-scale 3D Otsu thresholding algorithm for medical image segmentation. Digit Signal Process 60:186–199. doi:10.1016/j.dsp.2016.08.003

    Article  Google Scholar 

  15. Yang Z, Dong M, Guo Y, Gao X, Wang K, Shi B, Ma Y (2016) A new method of micro-calcifications detection in digitized mammograms based on improved simplified PCNN. Neurocomputing. doi:10.1016/j.neucom.2016.08.068

    Google Scholar 

  16. Musrrat A, Ch W, Pant M (2013) Multi-level image thresholding by synergetic differential evolution. Appl Soft Comput 17(3):1–11. doi:10.1016/j.asoc.2013.11.018

    Google Scholar 

  17. Guo Y, Dong M, Yang Z, Gao X, Wang K, Luo C, Ma Y, Zhang J (2016) A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN. Comput Methods Progam Biomed 130:31–45. doi:10.1016/j.cmpb.2016.02.019

    Article  Google Scholar 

  18. Zhan K, Shi J, Wang H, Xie Y, Li Q (2016) Computational mechanisms of pulse-coupled neural networks: a comprehensive review. Arch Comput Methods Eng. doi:10.1007/s11831-016-9182-3

    Google Scholar 

  19. Ma H, Cheng X (2014) Automatic image segmentation with PCNN algorithm based on grayscale correlation. Int J Signal Process 7(5):249–258. doi:10.14257/ijsip.2014.7.5.22

    Google Scholar 

  20. Zhuang H, Low K, Yau W (2012) Multichannel Pulse-Coupled-Neural-Network-Based Color Image Segmentation for Object Detection. IEEE Trans Ind Electron 59(8):3299–3308. doi:10.1109/TIE.2011.2165451

    Article  Google Scholar 

  21. Zheng W, Pu T, Chen J, Zeng H (2012) Image contrast enhancement by contour let transform and PCNN. In: Audio lang image process (ICALIP) international conference, pp 735–739. doi:10.1109/ICALIP.2012.6376711

  22. Xu G, Li C, Zhao J, Lei B (2014) Multiplicative decomposition based image contrast enhancement method using PCNN factoring model. In : Intelligent control and automation (WCICA), pp 1511–1566. doi:10.1109/WCICA.2014.7052943

  23. Yu B, Zhang L (2004) Pulse-coupled neural networks for contour and motion matchings. IEEE Trans Neural Netw 15(5):1186–1201. doi:10.1109/TNN.2004.832830

    Article  PubMed  Google Scholar 

  24. Chen Y, Ma Y, Park S (2015) Region-based object recognition by color segmentation using a simplified PCNN. IEEE Trans Neural Netw Learn Syst 26(8):1682–1697. doi:10.1109/TNNLS.2014.2351418

    Article  PubMed  Google Scholar 

  25. Berg H, Olsson R, Lindblad T, Chilo J (2008) Automatic design of pulse coupled neurons for image segmentation. Neurocomputing 71(10):1980–1993. doi:10.1016/j.neucom.2007.10.018

    Article  Google Scholar 

  26. Ma Y, Qi C (2006) Study of automated PCNN system based on genetic algorithm. J Syst Simul 18(3):722–725

    Google Scholar 

  27. Chen Y, Park S, Ma Y, Ala R (2011) A new automatic parameter setting method of a simplified PCNN for image segmentation. IEEE Trans Neural Netw 22(6):880–892. doi:10.1109/TNN.2011.2128880

    Article  PubMed  Google Scholar 

  28. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66. doi:10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  29. Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, Ricketts I, Stamatakis E, Cerneaz N, Kok S (1994) The mammographic image analysis society digital mammogram database. In :Excerpta medica international congress series, pp 375–378

  30. Zhan K, Zhang H, Ma Y (2009) New spiking cortical model for invariant texture retrieval and image processing. IEEE Trans Neural Netw 20(12):1980–1986. doi:10.1109/TNN.2009.2030585

    Article  PubMed  Google Scholar 

  31. Zhan K, Shi J, Li Q, Teng J (2015) Image segmentation using fast linking SCM. Int Jt Confere Neural Netw (IJCNN). doi:10.1109/IJCNN.2015.7280579

  32. Kittler J, Illingworth J (1986) Minimum error thresholding. Pattern Recognit 19(1):41–47. doi:10.1016/0031-3203(86)90030-0

    Article  Google Scholar 

  33. Sahoo P, Soltani S, Wong A (1988) A survey of thresholding techniques. Comput Graph Vis Image Process 41(2):233–260. doi:10.1016/0734-189X(88)90022-9

    Article  Google Scholar 

  34. Levine M, Naxif A (1985) Dynamic measurement of computer generated image segmentation. IEEE Transactions Pattern Anal Mach Intell 7(2):155–164. doi:10.1109/TPAMI.1985.4767640

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank all the reviewers for their valuable comments, which further improved the quality of the paper. This study was funded National Natural Science Foundation of China (Grant Numbers 61175012 & 61201422), Natural Science Foundation of Gansu Province of China (Grant Number 148RJZA044) and Youth Foundation of Lanzhou Jiaotong University of China (Grant Numbers 2013004 & 2014005).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China

    Jing Lian & Yide Ma

  2. Equipment Management Department, Gansu Provincial Hospital, Lanzhou, 730000, Gansu, China

    Bin Shi

  3. Biology Department, Lanhua No.1 High School, Lanzhou, 730060, Gansu, China

    Mingcong Li

  4. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, Gansu, China

    Ziwei Nan

Authors
  1. Jing Lian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingcong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ziwei Nan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yide Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yide Ma.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lian, J., Shi, B., Li, M. et al. An automatic segmentation method of a parameter-adaptive PCNN for medical images. Int J CARS 12, 1511–1519 (2017). https://doi.org/10.1007/s11548-017-1597-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-017-1597-2

Keywords

Search

Navigation