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Segmentation and classification of brain tumors using modified median noise filter and deep learning approaches

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Abstract

The most vital challenge for a radiologist is locating the brain tumors in the earlier stage. As the brain tumor grows rapidly, doubling its actual size in about twenty-five days. If not dealt properly, the affected person’s survival rate usually does no longer exceed half a year. This can rapidly cause dying. For this reason, an automatic system is desirable for locating brain tumors at the early stage. In general, when compared to computed tomography (CT), magnetic resonance image (MRI) scans are used for detecting the diagnosis of cancerous and noncancerous tumors. However, while MRI scans acquisition, there is a chance of appearing noise such as speckle noise, salt & pepper noise and Gaussian noise. It may degrade classification performance. Hence, a new noise removal algorithm is proposed, namely the modified iterative grouping median filter. Further, Maximum likelihood estimation-based kernel principal component analysis is proposed for feature extraction. A deep learning-based VGG16 architecture has been utilized for segmentation purposes. Experimental results have shown that the proposed algorithm outperforms the well-known techniques in terms of both qualitative and quantitative evaluation.

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References

  1. Abd-Ellah MK et al (2016) Design and implementation of a computer-aided diagnosis system for brain tumor classification. In 2016 28th International Conference on Microelectronics (ICM). IEEE

  2. Abdullah N, Ngah UK, Aziz SA (2011) Image classification of brain MRI using support vector machine. In 2011 IEEE International Conference on Imaging Systems and Techniques. IEEE

  3. Aborisade D et al (2014) Comparative analysis of textural features derived from GLCM for ultrasound liver image classification. Energy 2:10

    Google Scholar 

  4. Aja-Fernández S, Alberola-López 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

    Article  MathSciNet  Google Scholar 

  5. Anisha K, Wilscy M (2011) Impulse noise removal from medical images using fuzzy genetic algorithm. Int J Multimed Appl 3(4):93–106

    Google Scholar 

  6. Anjanappa C, Sheshadri HS (2015) Development of mathematical morphology filter for medical image impulse noise removal. In 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT). IEEE

  7. Bachoc F, Lagnoux A, López-Lopera AF (2019) Maximum likelihood estimation for Gaussian processes under inequality constraints. Electron J Statistics 13(2):2921–2969

    Article  MathSciNet  MATH  Google Scholar 

  8. Balasubramanian G, Chilambuchelvan A, Vijayan S, Gowrison G (2016) Probabilistic decision based filter to remove impulse noise using patch else trimmed median. AEU-International Journal of Electronics and Communications 70(4):471–481

    Google Scholar 

  9. Balasubramanian G, Chilambuchelvan A, Vijayan S, Gowrison G (2016) An extremely fast adaptive high-performance filter to remove salt and pepper noise using overlapping medians in images. Image Sci J 64(5):241–252

    Article  Google Scholar 

  10. Basu S, Fletcher T, Whitaker R (2006) Rician noise removal in diffusion tensor MRI. In International conference on medical image computing and computer-assisted intervention. Springer

  11. Bernal J, Kushibar K, Asfaw DS, Valverde S, Oliver A, Martí R, Lladó X (2019) Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif Intell Med 95:64–81

    Article  Google Scholar 

  12. Bhat M, Karki MV (2017) Feature selection based on PCA and PSO for multimodal medical image fusion using DTCWT. arXiv preprint arXiv:1701.08918

  13. Bhowmik MK et al (2017) Shape feature based automatic abnormality detection of cervico-vaginal pap smears. in 2nd International Conference on Biomedical Imaging, Signal Processing (ICBSP 2017), published by Journal of Image and Graphics (JOIG), New Jersey, USA

  14. Çalişkan A, Çevik U (2018) An efficient noisy pixels detection model for CT images using extreme learning machines. Tehnički vjesnik 25(3):679–686

    Google Scholar 

  15. Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electric Eng 40(1):16–28

    Article  Google Scholar 

  16. Chang Y et al (2019) Two-stage convolutional neural network for medical noise removal via image decomposition. IEEE Trans Instrum Meas 69(6):2707–2721

    Article  Google Scholar 

  17. Chanu PR, Singh KM (2018) Impulse noise removal from medical images by two stage quaternion vector median filter. J Med Syst 42(10):197

    Article  Google Scholar 

  18. Chen Q-Q, Hung M-H, Zou F (2017) Effective and adaptive algorithm for pepper-and-salt noise removal. IET Image Process 11(9):709–716

    Article  Google Scholar 

  19. Chen J, Zhan Y, Cao H, Xiong G (2019) Iterative grouping median filter for removal of fixed value impulse noise. IET Image Process 13(6):946–953

    Article  Google Scholar 

  20. Chinnu A (2015) MRI brain tumor classification using SVM and histogram based image segmentation. Int J Comput Sci Inform Technol 6(2):1505–1508

    Google Scholar 

  21. Cui S, Mao L, Jiang J, Liu C, Xiong S (2018) Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network. J healthcare eng 2018:1–14

    Google Scholar 

  22. Deepa B, Sumithra M (2015) Comparative analysis of noise removal techniques in MRI brain images. In 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE

  23. Geng L et al (2019) Lung segmentation method with dilated convolution based on VGG-16 network. Comput Assist Surg 24(sup2):27–33

    Article  Google Scholar 

  24. Gonzalez-Hidalgo M et al (2018) Improving salt and pepper noise removal using a fuzzy mathematical morphology-based filter. Appl Soft Comput 63:167–180

    Article  Google Scholar 

  25. Güler İ, Toprak A, Demirhan A, Karakış R (2008) MR images restoration with the use of fuzzy filter having adaptive membership parameters. J Med Syst 32(3):229–234

    Article  Google Scholar 

  26. Gurusamy R, Subramaniam V (2017) A machine learning approach for MRI brain tumor classification. Comput Mater Continua 53(2):91–109

    Google Scholar 

  27. Hamuda E, Glavin M, Jones E (2016) A survey of image processing techniques for plant extraction and segmentation in the field. Comput Electron Agric 125:184–199

    Article  Google Scholar 

  28. Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans syst, man, cybern, (6): p. 610–621

  29. Huang K, Aviyente S (2008) Wavelet feature selection for image classification. IEEE Trans Image Process 17(9):1709–1720

    Article  MathSciNet  Google Scholar 

  30. Iqbal S, Ghani MU, Saba T, Rehman A (2018) Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN). Microsc Res Tech 81(4):419–427

    Article  Google Scholar 

  31. Jafari M et al. (2020) DRU-Net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE

  32. Jin L, Xiong C, Li D (2008) Selective adaptive weighted median filter. Opt Eng 47(3):037001

    Article  Google Scholar 

  33. Kaur T, Gandhi TK (2019) Automated brain image classification based on VGG-16 and transfer learning. In 2019 International Conference on Information Technology (ICIT). IEEE

  34. Khalifa I, Youssif A, Youssry H (2012) MRI brain image segmentation based on wavelet and FCM algorithm. Int J Comput Appl. 47(16)

  35. Khan S, Lee D-H (2017) An adaptive dynamically weighted median filter for impulse noise removal. Eurasip J Adv Signal Process 2017(1):67

    Article  Google Scholar 

  36. Lahmiri S, Dawson DA, Shmuel A (2018) Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures. Biomed Eng Lett 8(1):29–39

    Article  Google Scholar 

  37. Lin P-H et al (2016) A morphological mean filter for impulse noise removal. J Disp Technol 12(4):344–350

    Google Scholar 

  38. Lu C-T, Chou T-C (2012) Denoising of salt-and-pepper noise corrupted image using modified directional-weighted-median filter. Pattern Recogn Lett 33(10):1287–1295

    Article  Google Scholar 

  39. Lu J et al (2008) Noise removal for medical x-ray images in multiwavelet domain. Int J Image Graph 8(01):25–46

    Article  Google Scholar 

  40. Lu J, Zhao T, Zhang Y (2008) Feature selection based-on genetic algorithm for image annotation. Knowl-Based Syst 21(8):887–891

    Article  Google Scholar 

  41. Madhukumar S, Santhiyakumari N (2015) Evaluation of k-means and fuzzy C-means segmentation on MR images of brain. Egypt J Radiol Nucl Med 46(2):475–479

    Article  Google Scholar 

  42. Mafi M, Martin H, Adjouadi M (2017) High impulse noise intensity removal in MRI images. In 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE

  43. Maheswari D, Radha V (2010) Noise removal in compound image using median filter. International journal on computer science and engineering 2(04):1359–1362

    Google Scholar 

  44. Mathew AR, Anto PB (2017) Tumor detection and classification of MRI brain image using wavelet transform and SVM. In 2017 International Conference on Signal Processing and Communication (ICSPC). IEEE

  45. Matthew AR, Prasad A, Anto PB (2017) A review on feature extraction techniques for tumor detection and classification from brain MRI. In 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE

  46. Menon N, Ramakrishnan R (2015) Brain tumor segmentation in MRI images using unsupervised artificial bee colony algorithm and FCM clustering. In 2015 International Conference on Communications and Signal Processing (ICCSP). IEEE

  47. Morchid M, Dufour R, Bousquet PM, Linarès G, Torres-Moreno JM (2014) Feature selection using principal component analysis for massive retweet detection. Pattern Recogn Lett 49:33–39

    Article  Google Scholar 

  48. Mújica-Vargas D, Gallegos-Funes FJ, de Jesús Rubio J, Pacheco J (2017) Impulsive noise filtering using a median Redescending M-estimator. Intelligent Data Analysis 21(3):739–754

    Article  Google Scholar 

  49. Murugan K, Arunachalam V, Karthik S (2019) Hybrid filtering approach for retrieval of MRI image. J Med Syst 43(1):9

    Article  Google Scholar 

  50. Myronenko A (2018) 3D MRI brain tumor segmentation using autoencoder regularization. In International MICCAI Brainlesion Workshop. Springer

  51. Naik J, Patel S (2014) Tumor detection and classification using decision tree in brain MRI. Int J Comput Sci Netw Secur 14(6):87

    Google Scholar 

  52. Nguyen C, Wang Y, Nguyen HN (2013) Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic

  53. Oh KT et al (2020) Semantic Segmentation of White Matter in FDG-PET Using Generative Adversarial Network. J Digit Imaging:1–10

  54. Oo SZ, Khaing AS (2014) Brain tumor detection and segmentation using watershed segmentation and morphological operation. Int J Res Eng Technol 3(03):367–374

    Article  Google Scholar 

  55. Qin K, Xu K, Liu F, Li D (2011) Image segmentation based on histogram analysis utilizing the cloud model. Comput Math Appl 62(7):2824–2833

    Article  MATH  Google Scholar 

  56. Ramesh S, Paramanandham N (2020) Segmentation and classification of brain tumors using Modified Median Noise Filter and Deep learning approaches

  57. Rathi VGP, Palani DS (2012) A novel approach for feature extraction and selection on MRI images for brain tumor classification. in Int Conf Comp Sci Eng Appl

  58. Roy A, Singha J, Manam L, Laskar RH (2017) Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images. IET Image Process 11(6):352–361

    Article  Google Scholar 

  59. Ruba T et al (2020) Accurate Classification and Detection of Brain Cancer Cells in MRI and CT Images using Nano Contrast Agents. Biomedical and Pharmacology Journal. 13(3)

  60. Sara U, Akter M, Uddin MS (2019) Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study. J Comput Commun 7(3):8–18

    Article  Google Scholar 

  61. Saritas MM, Yasar A (2019) Performance analysis of ANN and Naive Bayes classification algorithm for data classification. Int J Intell Syst Appl Eng 7(2):88–91

    Article  Google Scholar 

  62. Sarwinda D, Arymurthy AM (2013) Feature selection using kernel PCA for Alzheimer's disease detection with 3D MR Images of brain. In 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE

  63. Shan ZY, Yue GH, Liu JZ (2002) Automated histogram-based brain segmentation in T1-weighted three-dimensional magnetic resonance head images. NeuroImage 17(3):1587–1598

    Article  Google Scholar 

  64. Sharma M, Dubey R, Gupta S (2012) Feature extraction of mammograms. Int J Adv Comput Res, 2(5)

  65. Shasidhar M, Raja VS, Kumar BV (2011) MRI brain image segmentation using modified fuzzy c-means clustering algorithm. In 2011 International Conference on Communication Systems and Network Technologies. IEEE

  66. Shehab LH et al. (2020) An efficient brain tumor image segmentation based on deep residual networks (ResNets). J King Saud Univ-Eng Sci

  67. Shree NV, Kumar T (2018) Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain inform 5(1):23–30

    Article  Google Scholar 

  68. Singh JF, Magudeeswaran V (2017) Thresholding based method for segmentation of MRI brain images. In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC). IEEE

  69. Subramani T (2019) Brain tumor segmentation based on a hybrid clustering technique. California State University, Northridge.

  70. Sun L, Lee C, Hoeting JA (2015) A penalized simulated maximum likelihood approach in parameter estimation for stochastic differential equations. Computational Statistics & Data Analysis 84:54–67

    Article  MathSciNet  MATH  Google Scholar 

  71. Thanh DN et al. (2020) A two-stage filter for high density salt and pepper denoising. Multimedia tools and applications

  72. Toprak A, Güler İ (2006) Suppression of impulse noise in medical images with the use of fuzzy adaptive median filter. J Med Syst 30(6):465–471

    Article  Google Scholar 

  73. Vasanth K, Varatharajan R (2020) An adaptive content based closer proximity pixel replacement algorithm for high density salt and pepper noise removal in images. J Ambient Intell Humaniz Comput, p. 1–15

  74. Verma S et al. (2013) Analysis of image segmentation algorithms using MATLAB. In Proceedings of the Third International Conference on Trends in Information, Telecommunication and Computing. Springer

  75. Villar SA, Torcida S, Acosta GG (2017) Median filtering: a new insight. Journal of Mathematical Imaging and Vision 58(1):130–146

    Article  MATH  Google Scholar 

  76. 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

    Article  Google Scholar 

  77. Wong K-P (2005) Medical image segmentation: methods and applications in functional imaging, in Handbook of biomedical image analysis. Springer. p. 111–182

  78. Xie, S.-y., et al. (2009) Brain fMRI processing and classification based on combination of PCA and SVM. in 2009 International Joint Conference on Neural Networks. IEEE

  79. Xu Y, Zhang D, Song F, Yang JY, Jing Z, Li M (2007) A method for speeding up feature extraction based on KPCA. Neurocomputing 70(4–6):1056–1061

    Article  Google Scholar 

  80. Zhang P, Li F (2014) A new adaptive weighted mean filter for removing salt-and-pepper noise. IEEE signal process lett 21(10):1280–1283

    Article  Google Scholar 

  81. Zhang Y-D, Govindaraj VV, Tang C, Zhu W, Sun J (2019) High performance multiple sclerosis classification by data augmentation and AlexNet transfer learning model. J Medical Imaging Health Inform 9(9):2012–2021

    Article  Google Scholar 

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  1. S. Ramesh, S. Sasikala and Nirmala Paramanandham contributed equally to this work.

Authors and Affiliations

  1. Vignan Foundation for Science, Technology & Research, Guntur, Andhra Pradesh, India

    S. Ramesh

  2. Department of ECE, Kongu Engineering College, Erode, Tamilnadu, India

    S. Sasikala

  3. School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, India

    Nirmala Paramanandham

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  1. S. Ramesh
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Correspondence to Nirmala Paramanandham.

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Ramesh, S., Sasikala, S. & Paramanandham, N. Segmentation and classification of brain tumors using modified median noise filter and deep learning approaches. Multimed Tools Appl 80, 11789–11813 (2021). https://doi.org/10.1007/s11042-020-10351-4

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