Advertisement

Fuzzy entropy based on differential evolution for breast gland segmentation

  • Yuling Fan
  • Peizhong LiuEmail author
  • Jianeng Tang
  • Yanmin Luo
  • Yongzhao Du
Special Issue Article
  • 71 Downloads

Abstract

For the diagnosis and treatment of breast tumors, the automatic detection of glands is a crucial step. The true segmentation of the gland is directly related to effective treatment effect of the patient. Therefore, it is necessary to propose an automatic segmentation algorithm based on mammary gland features. A segmentation method of differential evolution (DE) fuzzy entropy based on mammary gland is proposed in the paper. According to the image fuzzy entropy, the evaluation function of image segmentation is constructed in the first step. Then, the method adopts DE, the image fuzzy entropy parameter is regard as the initial population of individual. After the mutation, crossover and selection of three evolutionary processes to search for the maximum fuzzy entropy of parameters, the optimal threshold of the segmented gland is achieved. Finally, the mammary gland is segmented by the threshold method of maximum fuzzy entropy. Eight breast images with four tissue types are tested 100 times, with accuracy (Acc), sensitivity (Sen), specificity (Spe), positive predictive value (PPV), negative predicted value (NPV), and average structural similarity (Mssim) to measure the segmentation result. The Acc of the proposed algorithm is 98.46 ± 8.02E−03%, 95.93 ± 2.38E−02%, 93.88 ± 6.59E−02%, 94.73 ± 1.82E−01%, 96.19 ± 1.15E−02%, and 97.51 ± 1.36E−02%, 96.64 ± 6.35E−02%, and 94.76 ± 6.21E−02%, respectively. The mean Mssim values of the 100 tests were 0.985, 0.933, 0.924, 0.907, 0.984, 0.928, 0.938, and 0.941, respectively. Our proposed algorithm is more effective and robust in comparison to the other fuzzy entropy based on swarm intelligent optimization algorithms. The experimental results show that the proposed algorithm has higher accuracy in the segmentation of mammary glands, and may serve as a gold standard in the analysis of treatment of breast tumors.

Keywords

Image segmentation Differential evolution Maximum fuzzy entropy Threshold method 

Abbreviations

DE

Differential evolution

CT

Computed tomography

CAD

Computer aided diagnosis

PSOFE

Fuzzy entropy segmentation based on particle swarm algorithm

BFOFE

Fuzzy entropy segmentation based on bacterial foraging optimization algorithm

ABCFE

Fuzzy entropy segmentation based on artificial bee colony algorithm

BATFE

Fuzzy entropy segmentation based on bat algorithm

FireflyFE

Fuzzy entropy segmentation based on firefly algorithm

Notes

Acknowledgements

This work is supported by National Natural Science Foundation of China Nos. 61231002 and 51075068, by the Foundation of the Fujian Education Department under Grant No. JA15035, by the Foundation of Quanzhou under Grant Nos. 2014Z103 and 2015Z114, by the 2016 Postgraduate Innovation Ability Cultivating Projects No. 1611422002.

Compliance with ethical standards

Conflict of interest

The author declares that there is 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. This article does not contain any studies with animals performed by any of the authors.

Informed consent

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

Research involving human and animal participants

This article does not contain any studies with animals performed by any of the authors.

References

  1. 1.
    Jemal A, Bray F, Center MM (2012) Global cancer statistics. Cancer J Clin 61(2):69–90CrossRefGoogle Scholar
  2. 2.
    Lina C (2016) 90 cases of breast cancer patients. Chin Rem Clin 16(8):1240–1242Google Scholar
  3. 3.
    Peifang L (2007) Breast imaging diagnosis. People’s Military Medical Press, BeijingGoogle Scholar
  4. 4.
    Giger ML (2002) Computer-aided diagnosis in radiology. Acad Radiol 9(1):1–3CrossRefGoogle Scholar
  5. 5.
    Jinshan T, Rangayyan RM, Jun X (2009) Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Trans Inf Technol Biomed 13(2):236–251CrossRefGoogle Scholar
  6. 6.
    Giger ML (2000) Computer-aided diagnosis of breast lesions in medical images. Comput Sci Eng 2(5):39–45CrossRefGoogle Scholar
  7. 7.
    Kitter J, Illingworth J (1985) Threshold selection based on a simple image statistic. Comput Vis Graph Image Process 30:125–147CrossRefGoogle Scholar
  8. 8.
    Petrick N, Chan HP, Sahiner B (1999) Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms. Med Phys 26(8):1642–1654CrossRefGoogle Scholar
  9. 9.
    Al-Faris AQ, Ngah UK, Isa NA (2014) Computer-aided segmentation system for breast MRI tumour using modified automatic seeded region growing (BMRI-MASRG). J Digit Imaging 27(1):133–144CrossRefGoogle Scholar
  10. 10.
    Bertrand G (2005) On topological watersheds. Math Imaging Vis 22(5):217–230CrossRefGoogle Scholar
  11. 11.
    Grau V, Mewes A (2004) Improved watershed transform for medical image segmentation using prior information. IEEE Trans Med Image 23(4):447–458CrossRefGoogle Scholar
  12. 12.
    Jiang Z-Y, Chen X-L (2009) Watershed transform based on morphological reconstruction. J Image Graph 14(12):2527–2533Google Scholar
  13. 13.
    Huang YL, Chen DR (2004) Watershed segmentation for breast tumor in 2-D sonography. Ultrasound Med Biol 30(5):625–632CrossRefGoogle Scholar
  14. 14.
    Feng Y, Dong F, Xia X (2017) An adaptive fuzzy C-means method utilizing neighboring information for breast tumor segmentation in ultrasound images. Med Phys 44(7):3752–3760CrossRefGoogle Scholar
  15. 15.
    Moftah HM, Azar AT, Al-Shammari ET (2014) Adaptive k-means clustering algorithm for MR breast image segmentation. Neural Comput Appl 24(7–8):1917–1928CrossRefGoogle Scholar
  16. 16.
    Cheng J, Sun X (2012) Medical image segmentation with improved gradient vector flow. Res J Appl Sci Eng Technol 4(20):3951–3957Google Scholar
  17. 17.
    Malek J, Sebri A, Mabrouk S (2007) Automated breast cancer diagnosis based on GVF-snake segmentation, wavelet features extraction and fuzzy classification. J Sig Process Syst 55(1–3):49–66Google Scholar
  18. 18.
    Mustafa M, Rashid NAO, Samad R (2015) Breast cancer segmentation based on GVF snake. Biomed Eng Sci 928–931Google Scholar
  19. 19.
    Zhang WW (2007) Maximum fuzzy entropy and particle swarm optimization (PSO) based infrared image segmentation. Chin J Electron Devices 5:1736–1740Google Scholar
  20. 20.
    Sanyal N, Chatterjee A, Munshi S (2011) An adaptive bacterial foraging algorithm for fuzzy entropy based image segmentation. Expert Syst Appl 38(12):15489–15498CrossRefGoogle Scholar
  21. 21.
    Xiao Y, Cao Y, Yu W (2012) Multi-level threshold selection based on artificial bee colony algorithm and maximum entropy for image segmentation. Int J Comput Appl Technol 43(4):343–350CrossRefGoogle Scholar
  22. 22.
    Ye ZW, Wang MW, Liu W (2015) Fuzzy entropy based optimal thresholding using bat algorithm. Appl Soft Comput 31:381–395CrossRefGoogle Scholar
  23. 23.
    Naidu MSR, Rajesh KP (2017) Multilevel image thresholding for image segmentation by optimizing fuzzy entropy using Firefly algorithm. Int J Eng Technol 9(2):472–488CrossRefGoogle Scholar
  24. 24.
    Wang Z, Bovik AC, Sheikh HR (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefGoogle Scholar
  25. 25.
    Zhao M, Fu AM, Yan H (2001) A technique of three-level thresholding based on probability partition and fuzzy 3-partition. IEEE Trans Fuzzy Syst 9:469–479CrossRefGoogle Scholar
  26. 26.
    Tao W, Jin H, Liu L (2007) Object segmentation using ant colony optimization algorithm and fuzzy entropy. Pattern Recognit Lett 28:788–796CrossRefGoogle Scholar
  27. 27.
    Zhang D (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386CrossRefGoogle Scholar
  28. 28.
    Sallam K-M, Elsayed S-M, Sarker R-A (2017) Landscape-based adaptive operator selection mechanism for differential evolution. Inf Sci 418:383–404CrossRefGoogle Scholar
  29. 29.
    Zhang X, Zhang X (2017) Improving differential evolution by differential vector archive and hybrid repair method for global optimization. Soft Comput 21:7107–7116CrossRefGoogle Scholar
  30. 30.
    Park S-Y, Lee J-J (2016) Stochastic opposition-based learning using a beta distribution in differential evolution. IEEE Trans Cybern 46:2184–2194CrossRefGoogle Scholar

Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2018

Authors and Affiliations

  • Yuling Fan
    • 1
  • Peizhong Liu
    • 1
    Email author
  • Jianeng Tang
    • 1
    • 2
  • Yanmin Luo
    • 3
  • Yongzhao Du
    • 1
  1. 1.College of EngineeringHuaqiao UniversityQuanzhouChina
  2. 2.College of Mechanical Engineering and AutomationHuaqiao UniversityXiamenChina
  3. 3.College of Computer Science and TechnologyHuaqiao UniversityXiamenChina

Personalised recommendations