Advertisement

Automated skin lesion division utilizing Gabor filters based on shark smell optimizing method

  • Hasan Hosseinzadeh
Original Paper
  • 8 Downloads

Abstract

In this work, we have proposed an unmonitored method in order to divide the photograph of lesions on the skin using the fabric characteristics. The fabric characteristic in the photograph is described using frequency of energy which is utilized by statistic based approaches called Gabor filter. Optimization of Gabor filters is done by a meta-heuristic algorithm which is named shark smell optimization. Every Gabor filter in the bank is modified to identify the trend of a given frequency and direction in case it is convoluted with photograph of the lesion. The convolving is conducted in the Fourier space. Also the yielded solution photograph is a characteristic which has joined the characteristic vector. Ultimately, the K-means division is utilized in order to distinguish the lesion from the regular part of skin in the photograph. The empirical outcomes indicate that the suggested analytic technique is completely productive in detecting the lesion on the skin for medical purposes. Obtained results demonstrate the validity of proposed optimization approach.

Keywords

Lesion division Median filter Classification Gabor filters Shark smell optimization K-means Characteristic space 

Notes

References

  1. Abedinia O, Amjady N, Ghasemi A (2016) A new metaheuristic algorithm based on shark smell optimization. Complexity 21(5):97–116MathSciNetCrossRefGoogle Scholar
  2. Abedinia O, Bekravi M, Ghadimi N (2017) Intelligent controller based wide-area control in power system. Int J Uncertain Fuzziness Knowl Based Syst 25(01):1–30CrossRefGoogle Scholar
  3. Agarwal A, Issac A, Dutta MK, Riha K, Uher V (2017a) Automated skin lesion segmentation using K-means clustering from digital dermoscopic images. In: 2017 40th international conference on telecommunications and signal processing (TSP). IEEE, pp 743–748Google Scholar
  4. Agarwal A, Issac A, Dutta MK, Doneva V, Ivanovski Z (2017b) Automated computer vision method for lesion segmentation from digital dermoscopic images. In: Electrical, computer and electronics (UPCON), 2017 4th IEEE Uttar Pradesh section international conference. IEEE, pp 538–542Google Scholar
  5. Ahmadian I, Abedinia O, Ghadimi N (2014) Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization. Front Energy 8(4):412–425CrossRefGoogle Scholar
  6. Feng D (2017) Dermoscopic image segmentation via multi-stage fully convolutional networks. IEEE Trans Biomed Eng 64(9):2065–2074CrossRefGoogle Scholar
  7. Gamino-Sánchez F, Hernández-Gutiérrez IV, Rosales-Silva AJ, Gallegos-Funes FJ, Mújica-Vargas D, Ramos-Díaz E, Kinani JMV (2018) Block-matching fuzzy C-means clustering algorithm for segmentation of color images degraded with Gaussian noise. Eng Appl Artif Intell 73:31–49CrossRefGoogle Scholar
  8. Ganster H, Pinz A, Rohrer R, Wildling E, Binder M, Kittler H (2001) Automated melanoma recognition. IEEE Trans Med Imaging 20(3):233–239CrossRefGoogle Scholar
  9. Gao W et al (2019) Different states of multi-block based forecast engine for price and load prediction. Int J Electr Power Energy Syst 104(2019):423–435CrossRefGoogle Scholar
  10. Garcia R (2012) Computerized analysis of pigmented skin lesions: a review. Artif Intell Med 56(2):69–90CrossRefGoogle Scholar
  11. Gee MS, Saunders HM, Lee JC, Sanzo JF, Jenkins WT, Evans SM, Trinchieri G, Sehgal CM, Felman MD, Lee WM (2001) Doppler ultrasound imaging detects changes in tumor perfusion during antivascular therapy associated with vascular anatomic alterations. Cancer Res 61:2974–2982Google Scholar
  12. Ghadimi N (2018) A hybrid neural network—world cup optimization algorithm for melanoma detection. Open Med 13(1):9–16 andMathSciNetCrossRefGoogle Scholar
  13. Gollou AR, Ghadimi N (2017) A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. J Intell Fuzzy Syst 32(6):4031–4045CrossRefGoogle Scholar
  14. Guarracino MR, Maddalena L (2018) SDI+: a novel algorithm for segmenting dermoscopic images. IEEE J Biomed Health Inform.  https://doi.org/10.1109/JBHI.2018.2808970 CrossRefGoogle Scholar
  15. Harangi B (2017) Skin lesion detection based on an ensemble of deep convolutional neural network. arXiv:1705.03360
  16. Hofbauer H, Uhl A (2016) Calculating a boundary for the significance from the equal-error rate. In: Biometrics (ICB) international conference on IEEEGoogle Scholar
  17. Hosseini Firouz M, Ghadimi N (2016) Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods. Complexity 21(6):70–88MathSciNetCrossRefGoogle Scholar
  18. Jalili A, Ghadimi N (2016) Hybrid harmony search algorithm and fuzzy mechanism for solving congestion management problem in an electricity market. Complexity 21(S1):90–98MathSciNetCrossRefGoogle Scholar
  19. Kapur JN, Sahoo PK, Wong AKC (1985) A new method for graylevel picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29:273–285CrossRefGoogle Scholar
  20. Kaur G, Aggarwal EN (2017) Organized classification of melanoma images using gaussian mixture model and artificial neural network. Imp J Interdiscip Res 3(8)Google Scholar
  21. Lughofer E, Pratama M, Skrjanc I (2017) Incremental rule splitting in generalized evolving fuzzy systems for autonomous drift compensation. IEEE Trans Fuzzy Syst.  https://doi.org/10.1109/TFUZZ.2017.2753727 CrossRefGoogle Scholar
  22. Noruzi A et al (2015) A new method for probabilistic assessments in power systems, combining monte carlo and stochastic-algebraic methods. Complexity 21(2):100–110MathSciNetCrossRefGoogle Scholar
  23. Oliveira RB, Mercedes Filho E, Ma Z, Papa JP, Pereira AS, Tavares JMR (2016) Computational methods for the image segmentation of pigmented skin lesions: a review. Comput Methods Programs Biomed 131:127–141CrossRefGoogle Scholar
  24. Parsian A, Ramezani M, Ghadimi N (2017) A hybrid neural network-gray wolf optimization algorithm for melanoma detection. Biomed Res 28(8):3408–3411Google Scholar
  25. Pathan S, Prabhu KG, Siddalingaswamy PC (2018) Hair detection and lesion segmentation in dermoscopic images using domain knowledge. Med Biol Eng Comput.  https://doi.org/10.1007/s11517-018-1837-9 CrossRefGoogle Scholar
  26. Polat K (2018) A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation. Appl Soft Comput 69:426–434CrossRefGoogle Scholar
  27. Pratama M, Lughofer E, Er MJ, Anavatti S, Lim CP (2017) Data driven modelling based on recurrent interval-valued metacognitive scaffolding fuzzy neural network. Neurocomputing 262:4–27CrossRefGoogle Scholar
  28. Qi J, Le M, Li C, Zhou P (2017) Global and local information based deep network for skin lesion segmentation. arXiv:1703.05467
  29. Razmjooy N, Ramezani M, Ghadimi N (2017) Imperialist competitive algorithm-based optimization of neuro-fuzzy system parameters for automatic red-eye removal. Int J Fuzzy Syst 19(4):1144–1156CrossRefGoogle Scholar
  30. Rubio JJ (2017) USNFIS: uniform stable neuro fuzzy inference system. Neurocomputing 262:57–66CrossRefGoogle Scholar
  31. Rubio JJ (2018) Error convergence analysis of the SUFIN and CSUFIN. Appl Soft Comput.  https://doi.org/10.1016/j.asoc.2018.04.003 CrossRefGoogle Scholar
  32. Wen H (2017) II-FCN for skin lesion analysis towards melanoma detection. arXiv:1702.08699

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of MathematicsArdabil Branch, Islamic Azad UniversityArdabilIran

Personalised recommendations