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An improved Gabor wavelet transform and rough K-means clustering algorithm for MRI brain tumor image segmentation

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Abstract

Image processing is significant in the medical field which provides detailed information about medical images and image segmentation is an essential part of medical image processing. In the medical field, various modalities have been utilized such as X-ray, CT scan and MRI, etc. MRI provides accurate results than other techniques. Our proposed technique is highly focused on tumor identification using MRI image segmentation. The proposed methodology consists of five stages namely, pre-processing, feature extraction, feature selection, classification, and segmentation. Initially, input MRI images are given to the preprocessing stage to fit the images for further processing. In this preprocessing phase, the input images are converted into a transform domain with the aid of Improved Gabor Wavelet Transform (IGWT). Then, GLCM related features are extracted and important features are selected with the help of the Oppositional fruit fly algorithm (OFFA). Then, the selected features are given to the support vector machine (SVM) classifier to classify an image as normal or abnormal. After the classification process, the abnormal images are selected and given to the segmentation process. For segmentation, in this paper, we utilized an effective rough k-means algorithm. The performance of the proposed methodology is evaluated in terms of Sensitivity, Specificity, and Accuracy. The experimental results show that our proposed method attained better results compared to existing work.

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References

  1. Al-Dmour H, Al-Ani A (2018) A clustering fusion technique for MR brain tissue segmentation. Neurocomputing 275:546–559

    Article  Google Scholar 

  2. Angulakshmi M, Lakshmi Priya GG (2018) Brain tumour segmentation from MRI using superpixels based spectral clustering. J King Saud Univ-Comp Inf Sci

  3. Banchpalliwar RA, Suresh SS (2016) A review on brain MRI image segmentation clustering algorithm. IOSR J Electron Comm Eng (IOSR-JECE) 11(1):80–84

    Google Scholar 

  4. Bharathi R, Satish D (2015) Improving MRI BRAIN tumor classification and segmentation based on comparison technique. Int J Emerg Technol Comp Sci Electronics (IJETCSE) 13(1):560–569

    Google Scholar 

  5. Borole VY, Nimbhore SS, Kawthekar SS (2015) Image processing techniques for brain tumor detection: A review. Int J Emerg Trends Technol Comp Sci (IJETTCS) 4(5):28–32

    Google Scholar 

  6. Chanchlani A, Chaudhari MR, Shewale BY, Jha A (2017) Tumor detection in brain MRI using clustering and segmentation algorithm. Imp J Interdiscip Res 3(5)

  7. Ergen B (2014) A fusion method of gabor wavelet transform and unsupervised clustering algorithms for tissue edge detection. In proceedings of Science of World Journal in Hindawi Publishing Corporation 1–13

  8. Kaur G, Rani J (2016) MRI brain tumor segmentation methods-A review. Infinite Stud 6(3)

  9. Kumaran N, Bhavani R (2013) MRI Image Retrieval Using Gabor Wavelet Based Texture Features. Int J Adv Res Sci Technol 2(1):2320–1126

    Google Scholar 

  10. Leng L, Teoh ABJ (2015) Alignment-free row-co-occurrence cancelable palmprint fuzzy vault. Pattern Recogn 48(7):2290–2303

    Article  Google Scholar 

  11. Leng L, Zhang J (2011) Dual-key-binding cancelable palmprint cryptosystem for palmprint protection and information security. J Netw Comput Appl 34(6):1979–1989

    Article  Google Scholar 

  12. Leng L, Zhang J (2013) Palmhash code vs. palmphasor code. Neurocomputing 108:1–12

    Article  Google Scholar 

  13. Leng L, Zhang J, Xu J, Khan MK, Alghathbar K (2010) Dynamic weighted discrimination power analysis in DCT domain for face and palmprint recognition. In 2010 international conference on information and communication technology convergence (ICTC), pp. 467–471. IEEE

  14. Leng L, Zhang J, Chen G, Khan MK, Alghathbar K (2011) "Two-directional two-dimensional random projection and its variations for face and palmprint recognition." In International conference on computational science and its applications, pp. 458–470. Springer, Berlin, Heidelberg

  15. Leng, L, Zhang S, Xue B, Khan MK (2012) "Two-dimensional cancelable biometric scheme." In 2012 International Conference on Wavelet Analysis and Pattern Recognition, pp. 164–169. IEEE

  16. Leng L, Li M, Teoh ABJ (2013) Conjugate 2D Palm Hash code for secure palm-print-vein verification. In 2013 6th International Congress on Image and Signal Processing (CISP) 3: 1705-1710. IEEE

  17. Leng L, Teoh ABJ, Li M, Khan MK (2014) Analysis of correlation of 2DPalmHash code and orientation range suitable for transposition. Neurocomputing 131:377–387

    Article  Google Scholar 

  18. Leng L, Teoh ABJ, Li M, Khan MK (2014) A remote cancelable palmprint authentication protocol based on multi-directional two-dimensional PalmPhasor-fusion. Secur Commun Netw 7(11):1860–1871

    Article  Google Scholar 

  19. Leng L, Teoh ABJ, Li M, Khan MK (2015) Orientation range of transposition for vertical correlation suppression of 2DPalmPhasor code. Multimed Tools Appl 74(24):11683–11701

    Article  Google Scholar 

  20. Leng L, Li M, Kim C, Xue B (2017) Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimed Tools Appl 76(1):333–354

    Article  Google Scholar 

  21. Leng L, Teoh ABJ, Li M (2017) Simplified 2D Palm Hash code for secure palmprint verification. Multimed Tools Appl 76(6):8373–8398

    Article  Google Scholar 

  22. Liu Y-h, Muftah M, Das T, Ba L, Robson K, Auer D (2012) Classification of MR tumor images based on Gabor wavelet analysis. J Med Biol Eng 32(1):22–28

    Article  Google Scholar 

  23. Mahajan GS, Kanchan Bhagat S Survey on Medical Image Segmentation using Enhanced K-Means and Kernelized Fuzzy C-Means. Int J Adv Eng Technol 6(6):2531–2536

  24. Malviya MR, Akshay M, Joshi AS (2014) Review on automatic brain tumor detection based on Gabor wavelet. Int J Eng 3(1):53–56

    Google Scholar 

  25. Manikandan R, Monolisa GS, Saranya K (2013) A cluster based segmentation of magnetic resonance images for brain tumor detection. Middle-East J Sci Res 14(5):669–672

    Google Scholar 

  26. Mohan G, Monica Subashini M (2018) MRI based medical image analysis: Survey on brain tumor grade classification. Biomed Signal Process Control 39:139–161

    Article  Google Scholar 

  27. Nabizadeh N, Kubat M (2015) Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features. Comp Electrical Eng, 301 45:286

  28. Noureen E, Hassan K (2014) Brain tumor detection using histogram thresholding to get the threshold point. IOSR J Electr Electron Eng 9(5):14–19

    Article  Google Scholar 

  29. Patel J, Doshi K (2014) A study of segmentation methods for detection of tumor in brain MRI. Adv Electron Electric Eng 4(3):279–284

    Google Scholar 

  30. Potdukhe B, Nagtode SA (2016) Application of wavelet transform for brain tumour detection. Int J Innov Res Comp Comm Eng 4(3):3025–3030

    Google Scholar 

  31. Sauwen N, Acou M, Van Cauter S, Sima DM, Veraart J, Maes F, Himmelreich U, Achten E, Van Huffel S (2016) Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric. MRI NeuroImage Clin 12:753–764

    Article  Google Scholar 

  32. Shingade S, Jain P (2017). Identification of brain tumor using gabor wavelets techniques 4(2):16–21

  33. Singh P, Arora K, Sinha S (2017) Survey on MR image segmentation using fuzzy C-means algorithm. Int J Eng Res 6(5):261–264

    Article  Google Scholar 

  34. Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X (2018) Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Comput Methods Prog Biomed 157:69–84

    Article  Google Scholar 

  35. Verma R, Tiwari S, Rahim N (2015) Unsupervised MRI Brain tumor detection techniques with morphological operations. Int J Sci, Eng Technol Res (IJSETR) 4(11):3710–3715

    Google Scholar 

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Authors and Affiliations

  1. Department of ECE, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, Andhra Pradesh, India

    D. Maruthi Kumar & M. N. Giri Prasad

  2. Department of ECE, Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal, Andhra Pradesh, India

    D. Satyanarayana

Authors
  1. D. Maruthi Kumar
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  2. D. Satyanarayana
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  3. M. N. Giri Prasad
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Correspondence to D. Maruthi Kumar.

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Kumar, D.M., Satyanarayana, D. & Prasad, M.N.G. An improved Gabor wavelet transform and rough K-means clustering algorithm for MRI brain tumor image segmentation. Multimed Tools Appl 80, 6939–6957 (2021). https://doi.org/10.1007/s11042-020-09635-6

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  • DOI: https://doi.org/10.1007/s11042-020-09635-6

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