Advertisement

Evolutionary Intelligence

, Volume 12, Issue 4, pp 647–663 | Cite as

MASCA–PSO based LLRBFNN model and improved fast and robust FCM algorithm for detection and classification of brain tumor from MR image

  • Satyasis Mishra
  • Premananda Sahu
  • Manas Ranjan SenapatiEmail author
Research Paper
  • 49 Downloads

Abstract

A novel modified adaptive sine cosine optimization algorithm (MASCA) integrated with particle swarm optimization (PSO) based local linear radial basis function neural network (LLRBFNN) model has been proposed for automatic brain tumor detection and classification. In the process of segmentation, the fuzzy C means algorithm based techniques drastically fails to remove noise from the magnetic resonance images. So, for reduction of noise and smoothening of brain tumor magnetic resonance image an improved fast and robust fuzzy c means algorithm segmentation algorithm has been proposed in this research work. The gray level co-occurrence matrix technique has been employed to extract features from brain tumor magnetic resonance images and the extracted features are fed as input to the proposed modified ASCA–PSO based LLRBFNN model for classification of benign and malignant tumors. In this research work the LLRBFNN model’s weights are optimized by using proposed MASCA–PSO algorithm which provides a unique solution to get rid of the hectic task of radiologist from manual detection. The classification accuracy results obtained from sine cosine optimization algorithm, PSO and adaptive sine cosine optimization algorithm integrated with particle swarm optimization based LLRBFNN models are compared with the proposed MASCA–PSO based LLRBFNN model. It is observed that the result obtained from the proposed model shows better classification accuracy results as compared to the other LLRBFNN based models.

Keywords

Fuzzy C means algorithm (FCM) Fast and robust fuzzy C means algorithm (FRFCM) Local linear radial basis function neural network (LLRBFNN) Adaptive sine cosine optimization algorithm–particle swarm optimization (ASCA–PSO) Sine cosine algorithm (SCA) 

Notes

References

  1. 1.
  2. 2.
    Ding Y, Fu X (2016) Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm. Neurocomputing 188:233–238.  https://doi.org/10.1016/j.neucom.2015.01.106 CrossRefGoogle Scholar
  3. 3.
    Pereira DC, Ramos RP, do Nascimento MZ (2014) Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. Comput Methods Programs Biomed 114(1):88–101.  https://doi.org/10.1016/j.cmpb.2014.01.014 CrossRefGoogle Scholar
  4. 4.
    Mahapatra D (2017) Semi-supervised learning and graph cuts for consensus based medical image segmentation. Pattern Recognit 63:700709.  https://doi.org/10.1016/j.patcog.2016.09.030 CrossRefGoogle Scholar
  5. 5.
    Bahadure NB, Ray AK, Thethi HP (2017) Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Hindawi Int J Biomed Imaging 2017, Article ID 9749108.  https://doi.org/10.1155/2017/9749108 CrossRefGoogle Scholar
  6. 6.
    Satheeskumaran S, Sabrigiriraj M (2014) A new LMS based noise removal and DWT based R-peak detection in ECG signal for biotelemetry applications. Natl Acad Sci Lett 37(4):341–349.  https://doi.org/10.1007/s40009-014-0238-3 CrossRefGoogle Scholar
  7. 7.
    Shanmuga Priya S, Valarmathi A (2018) Efficient fuzzy c-means based multilevel image segmentation for brain tumor detection in MR images. In: Design automation for embeded system. Springer, Berlin.  https://doi.org/10.1007/s10617-017-9200-1. ISSN: 1572-8080CrossRefGoogle Scholar
  8. 8.
    Javed A, Kim YC, Khoo MCK, Ward SLD, Nayak KS (2016) Dynamic 3-D MR visualization and detection of upper airway obstruction during sleep using region-growing segmentation. IEEE Trans Biomed Eng 63(2):431–437.  https://doi.org/10.1109/TBME.2015.2462750 CrossRefGoogle Scholar
  9. 9.
    Abd-Ellah MK, Awad AI, Khalaf AM, Hamed FA (2016) Design and implementation of a computer-aided diagnosis system for brain tumor classification. In: 28th international conference on microelectronics (ICM), Cairo, pp 73–76Google Scholar
  10. 10.
    Li Z, Chen J (2015) Super pixel segmentation using linear spectral clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Boston, pp 1356–1363Google Scholar
  11. 11.
    Nandi AK, Basel AJ, Rui F (2015) Integrative cluster analysis in bioinformatics. Wiley, BerlinGoogle Scholar
  12. 12.
    Demirhan A, Güler I (2011) Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation. Eng Appl Artif Intell 24:358–367.  https://doi.org/10.1016/j.engappai.2010.09.008 CrossRefGoogle Scholar
  13. 13.
    Shree NV, Kumar TNR (2018) Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inform 5:23–30.  https://doi.org/10.1007/s40708-017-0075-5 CrossRefGoogle Scholar
  14. 14.
    Chatzis SP, Varvarigou TA (2008) A fuzzy clustering approach toward hidden markov random field models for enhanced spatially constrained image segmentation. IEEE Trans Fuzzy Syst 16(5):1351–1361.  https://doi.org/10.1109/TFUZZ.2008.2005008 CrossRefGoogle Scholar
  15. 15.
    Lei T, Jia X, Zhang Y, He L, Meng H, Nandi AK (2018) Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering. IEEE Trans Fuzzy Syst 26(5):3027–3041.  https://doi.org/10.1109/tfuzz.2018.2796074 CrossRefGoogle Scholar
  16. 16.
    Issa M, Hassanien AE, Oliva D, Helmi A, Ziedan I, Alzohairy A (2018) ASCA–PSO: adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment. Expert Syst Appl 99(1):56–70.  https://doi.org/10.1016/j.eswa.2018.01.019 CrossRefGoogle Scholar
  17. 17.
    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):193–199.  https://doi.org/10.1109/42.996338 CrossRefGoogle Scholar
  18. 18.
    Chen S, Zhang D (2004) Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans Syst Man Cybern B Cybern 34(4):1907–1916.  https://doi.org/10.1109/tsmcb.2004.831165 CrossRefGoogle Scholar
  19. 19.
    Szilagyi L, Benyo Z, Szilagyii SM, Adam HS (2003) MR brain image segmentation using an enhanced fuzzy c-means algorithm. In: Proceeding of the 25th annual international conference of the IEEE EMBS, pp 17–21Google Scholar
  20. 20.
    Cai W, Chen S, Zhang D (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognit 40(3):825–838.  https://doi.org/10.1016/j.patcog.2006.07.011 CrossRefzbMATHGoogle Scholar
  21. 21.
    Krinidis S, Chatzis V (2010) A robust fuzzy local information c-means clustering algorithm. IEEE Trans Image Process 19(5):1328–1337.  https://doi.org/10.1109/tip.2010.2040763 MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Gong M, Zhou Z, Ma J (2012) Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans Image Process 21(4):2141–2151.  https://doi.org/10.1109/TIP.2011.2170702 MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Gong M, Liang Y, Shi S, Ma J (2013) Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Trans Image Process 22(2):573–584.  https://doi.org/10.1109/TIP.2012.2219547 MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Guo F, Wang X, Shen J (2016) Adaptive fuzzy c-means algorithm based on local noise detecting for image segmentation. IET Image Process 10(4):272–279.  https://doi.org/10.1049/iet-ipr.2015.0236 CrossRefGoogle Scholar
  25. 25.
    Rezaei K, Agahi H (2017) Malignant and benign brain tumor segmentation and classification using SVM with weighted kernel width. Sig Image Proc Int J (SIPIJ).  https://doi.org/10.5121/sipij.2017.8203 CrossRefGoogle Scholar
  26. 26.
    Torheim T, Malinen E, Kvaal K et al (2014) Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines. IEEE Trans Med Imaging 33(8):1648–1656.  https://doi.org/10.1109/TMI.2014.2321024 CrossRefGoogle Scholar
  27. 27.
    Lang R, Zhao L, Jia K (2016) Brain tumor image segmentation based on convolution neural network. In: 2016 9th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), Datong, pp 1402–1406Google Scholar
  28. 28.
    Deepa SN, Arunadevi B (2013) Extreme learning machine for classification of brain tumor in 3D MR images. Informatologia 46(2):111–121. ISSN 1330-0067Google Scholar
  29. 29.
    Krishna TG, Sunitha KVN, Mishra S (2018) Detection and classification of brain tumor from MRI medical image using wavelet transform and PSO based LLRBFNN algorithm. Int J Comput Sci Eng 6(1).  https://doi.org/10.26438/ijcse/v6i1.1823. E-ISSN: 2347-2693CrossRefGoogle Scholar
  30. 30.
    Nayak PK, Mishra S, Dash PK, Bisoi Ranjeeta (2016) Comparison of modified teaching–learning-based optimization and extreme learning machine for classification of multiple power signal disturbances. Neural Comput Appl 27(7):2107–2122.  https://doi.org/10.1007/s00521-015-2010-0 CrossRefGoogle Scholar
  31. 31.
    Patra A, Das S, Mishra SN, Senapati MR (2017) An adaptive local linear optimized radial basis functional neural network model for financial time series prediction. Neural Comput Appl 28(1):101–110.  https://doi.org/10.1007/s00521-015-2039-0 CrossRefGoogle Scholar
  32. 32.
    Liu B, Wang L, Jin YH (2007) An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans Syst Man Cybern B Cybern 37(1):18–27.  https://doi.org/10.1109/tsmcb.2006.883272 CrossRefGoogle Scholar
  33. 33.
    Senapati MR, Vijaya I, Dash PK (2007) Rule extraction by training radial basis functional neural network with particle swarm optimization. Am J Sci 3(8):592–599. ISSN: 1549-3636Google Scholar
  34. 34.
    Yang X-S, Deb S, Fong S (2011) Accelerated particle swarm optimization and support vector machine for business optimization and applications. In: International conference on networked digital technologies, NDT 2011. Communications in computer and information science, vol 136, pp 53–66. Springer, BerlinCrossRefGoogle Scholar
  35. 35.
    Kaur T, Saini BS, Gupta S (2016) Optimized multi threshold brain tumor image segmentation using two dimensional minimum cross entropy based on co-occurrence matrix. In: Medical imaging in clinical applications. Part of the studies in computational intelligence, vol 651. Springer, Berlin, pp 461–486.  https://doi.org/10.1007/978-3-319-33793-7_20 CrossRefGoogle Scholar
  36. 36.
    Garg H (2016) A hybrid PSO–GA algorithm for constrained optimization problems. Appl Math Comput 274(1):292–305.  https://doi.org/10.1016/j.amc.2015.11.001 MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    de Fátima Araújoa T, Uturbey W (2013) Performance assessment of PSO, DE and hybrid PSO–DE algorithms when applied to the dispatch of generation and demand. Int J Electr Power Energy Syst 47:205–217.  https://doi.org/10.1016/j.ijepes.2012.11.002 CrossRefGoogle Scholar
  38. 38.
    Santra D, Mukherjee A, Sarker K, Chatterjee D (Oct 2016) Hybrid PSO–ACO algorithm to solve economic load dispatch problem with transmission loss for small scale power system. In: 2016 international conference on intelligent control power and instrumentation (ICICPI), pp 21–23Google Scholar
  39. 39.
    Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133.  https://doi.org/10.1016/j.knosys.2015.12.022 CrossRefGoogle Scholar
  40. 40.
    Tasnin W, Saikia LC (2018) Maiden application of an sine–cosine algorithm optimised FO cascade controller in automatic generation control of multi-area thermal system incorporating dish-Stirling solar and geothermal power plants. IET Renew Power Gener 12(5):585–597.  https://doi.org/10.1049/iet-rpg.2017.0063 CrossRefGoogle Scholar
  41. 41.
    Nenavath H, Jatoth RK, Das S (2018) A synergy of the sine–cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm Evol Comput.  https://doi.org/10.1016/j.swevo.2018.02.011 CrossRefGoogle Scholar
  42. 42.
    Gonçalves H, Gonçalves JA, Corte-Real L (2011) HAIRIS: a method for automatic image registration through histogram-based image segmentation. IEEE Trans Image Process 20(3):776–789.  https://doi.org/10.1109/TIP.2010.2076298 MathSciNetCrossRefzbMATHGoogle Scholar
  43. 43.
    Jamil M, Yang X-S (2013) A literature survey of benchmark functions for global optimization problems. Int J Math Model Numer Optim 4(2):150–194.  https://doi.org/10.1504/IJMMNO.2013.055204 CrossRefzbMATHGoogle Scholar
  44. 44.
    Smith TM, Bonacuse P, Sosa J, Kulis M, Evans L (2018) A quantifiable and automated volume fraction characterization technique for secondary and tertiary γ′ precipitates in Ni-based super alloys. Mater Charact 140:86–94.  https://doi.org/10.1016/j.matchar.2018.03.051 CrossRefGoogle Scholar
  45. 45.
    Pal C, Das P, Chakrabarti A, Ghosh R (2017) Rician noise removal in magnitude MRI images using efficient anisotropic diffusion filtering. Int J Imaging Syst Technol 27(3):248–264.  https://doi.org/10.1002/ima.22230 CrossRefGoogle Scholar
  46. 46.
    Aja-Fernandez S, Alberola-Lopez C, Westin C-F (2008) Noise and signal estimation in magnitude MRI and Rician distributed images: A LMMSE approach. IEEE Trans Image Process 17(8):1383–1398.  https://doi.org/10.1109/tip.2008.925382 MathSciNetCrossRefGoogle Scholar
  47. 47.
    Dataset: Webpage of Medical School of Harvard University. www.med.harvard.edu/AANLIB/home.html
  48. 48.
    Cocosco CA, Kollokian V, Kwan RK-S, Evans AC (2011). BrainWeb: online interface to a 3D MRI simulated brain database (Online). http://www.bic.mni.mcgill.ca/brainweb
  49. 49.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612.  https://doi.org/10.1109/TIP.2003.819861 CrossRefGoogle Scholar
  50. 50.
    Aja-Fernandez S, San-José-Estépar R, Alberola-Lopez C, Westin C (Sept 2006) Image quality assessment based on local variance. In: Proceeding of the 28th IEEE EMBS, New York, pp 4815–4818Google Scholar
  51. 51.
    Nenavatha H, Jatotha RK, Das S (2018) A synergy of the sine–cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm Evol Comput 43:1–30.  https://doi.org/10.1016/j.swevo.2018.02.011 CrossRefGoogle Scholar
  52. 52.
    Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Slap swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191.  https://doi.org/10.1016/j.advengsoft.2017.07.002 CrossRefGoogle Scholar
  53. 53.
    Mirjalili S, Mirjalili SM, Lewis A (2014) Gary wolf optimizer. Adv Eng Softw 69:46–61.  https://doi.org/10.1016/j.advengsoft.2013.12.007 CrossRefGoogle Scholar
  54. 54.
    Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67.  https://doi.org/10.1016/j.advengsoft.2016.01.008 CrossRefGoogle Scholar
  55. 55.
    Mirjalili S (2015) Mouth –flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249.  https://doi.org/10.1016/j.knosys.2015.07.006 CrossRefGoogle Scholar
  56. 56.
    Xu Y, Fan P, Yuan L (2013) A simple and efficient artificial bee colony algorithm. Math Probl Eng 2013:1–9, Article ID 526315, Hindawi. http://dx.doi.org/10.1155/2013/526315 Google Scholar
  57. 57.
    Mahesh KM, Renjit JA (2018) Evolutionary intelligence for brain tumor recognition from MRI images: a critical study and review. Evol Intell.  https://doi.org/10.1007/s12065-018-0156-2 CrossRefGoogle Scholar
  58. 58.
    Nayak DR, Dash R, Majhi B (2016) Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 117:188–197.  https://doi.org/10.1016/j.neucom.2015.11.034i CrossRefGoogle Scholar
  59. 59.
    Mohana G, Subashini MM (2018) MRI based medical image analysis: survey on brain tumor grade classification. Biomed Signal Process Control 39:139–161CrossRefGoogle Scholar
  60. 60.
    Mohanaiah P, Sathyanarayana P, GuruKumar L (2013) Image texture feature extraction using GLCM approach. Int J Sci Res Publ 3(5):1Google Scholar
  61. 61.
    Das S, Chowdhury M, Kundu MK (2013) Brain MR image classification using multiscale geometric analysis of ripplet. Prog Electromagn Res 137:1–17.  https://doi.org/10.2528/PIER13010105 CrossRefGoogle Scholar
  62. 62.
    Nayak DR, Dash R, Majhi B (2017) Discrete ripplet-II transform and modified PSO based improved evolutionary extreme learning machine for pathological brain detection. Neurocomputing.  https://doi.org/10.1016/j.neucom.2017.12.030 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Satyasis Mishra
    • 1
  • Premananda Sahu
    • 2
  • Manas Ranjan Senapati
    • 3
    Email author
  1. 1.Department of ECEAdama Science and Technology UniversityAdamaEthiopia
  2. 2.Department of CSECenturion University of Technology and ManagementBhubaneswarIndia
  3. 3.Department of Information TechnologyVeer Surendra Sai University of TechnologyBurlaIndia

Personalised recommendations