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An efficient method for brain tumor detection and categorization using MRI images by K-means clustering & DWT

Abstract

Brain tumor is an uncontrolled mass of tissues in the brain which originate due to mutated growth of tissues. Brain tumor has become a leading cost of death in modern day environment and researchers are inclined to find ways to mitigate the proliferation of this disease. A lot of methods have been applied in brain tumor detection ranging from image processing to signal based analysis. In this study a robust image processing based method is applied using MRI images. MRI images are preferred due to their simplicity and low noise presence. In this study first a clustering based method is used to segment the image and then SVM is applied for tumor detection. A total seven features were considered and were analyzed by the classifiers. SVM with 94.6% accuracy gave a robust result.

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References

  1. 1.

    Shil SK, Polly FP (2017) An improved brain tumor detection and classification mechanism. In: ICTC

  2. 2.

    Chetty H, Shah M (2017) A survey on brain tumor extraction approach from MRI images using image processing. In: I2CT

  3. 3.

    Hunnur MS, Raut A, Kulkarni S (2017) Implementation of image processing for detection of brain tumors. In: ICICCS

  4. 4.

    Saha C, Hossain MF (2017) MRI brain tumor images classification using K-means clustering, NSCT and SVM. In: IEEE

  5. 5.

    Dvorak P, Kropatsch W, Bartusek K (2013) Automatic detection of brain tumors in MR images. In: TSP (ISSN-978-1-4799-0404-4)

  6. 6.

    Naz S, Hameed IA (2015) Automated techniques for brain tumor segmentation and detection: a review study. In: IEEE

  7. 7.

    Bahadure NB, Ray AK, Thethi HP (2017) Feature extraction and selection with optimization technique for brain tumor detection from MR images. In: ICCIDS

  8. 8.

    Mukhopadhyay J (2017) Image resizing in the compressed domain. In: ISSCS

  9. 9.

    Papamarkou I, Papamarkos N (2013) Conversion of color documents to grayscale. In: MED

  10. 10.

    Acuña RG, Tao J, Klette R (2015) Generalization of otsu’s binarization into recursive color image segmentation. In: IVCNZ

  11. 11.

    Kumar A, Sinha R, Bhattacherjee V, Verma DS, Singh S (2012) Modelling using K-means clustering algorithm. In: Recent advances in information technology (RAIT). IEEE

  12. 12.

    Bishnu PS, Bhattacherjee V (2012) A dimension reduction technique for K-Means clustering algorithm. In: Recent advances in information technology (RAIT). IEEE

  13. 13.

    Akram MU, Usman A (2011) Computer aided system for brain tumor detection and segmentation. In: IEEE (ISSN-978-1-61284-941-6)

  14. 14.

    Logeswari T, Karnan M (2010) An enhanced implementation of brain tumor detection using segmentation based on soft computing. In: International conference on signal acquisition and processing, IEEE 2010, (ISSN-978-0-7695-3960-7)

  15. 15.

    Mathew AR, Anto PB (2017) Tumor detection and classification of MRI brain image using wavelet transform and SVM. In: ICSPC

  16. 16.

    Zhang Z, Komazaki N, Toda H, Miyake T, Imamura T (2008) Directional selection of 2D Complex discrete wavelet transform and its application to image processing. In: ICWPR

  17. 17.

    Vijay J, Subhashini J (2013) An efficient brain tumor detection methodology. In: International conference on communication and signal processing, April 2013, India, (ISSN-978-1-4673-4866-9)

  18. 18.

    Subashini MM, Sahoo SK (2012) Brain tumour detection using pulse coupled neural network (PCNN) and back propagation network. In: SEISCON—2012 IEEE

  19. 19.

    Kumar TS, Rashmi K, Ramadoss S, Sandhya LK, Sangeetha TJ (2017) Brain tumor detection using SVM classifier. In: ICSSS

  20. 20.

    Telrandhe SR, Pimpalkar A, Kendhe A (2016) Detection of brain tumor from MRI images by using segmentation and SVM. In: IEEE

  21. 21.

    Lau PY, Voon FCT, Ozawa S (2005) The detection and visualization of brain tumors on T2-weighted MRI images using multiparameter feature blocks. In: Proceedings of the 2005 IEEE engineering in medicine and biology 27th annual conference, Shanghai, China, 1–4 September 2005

  22. 22.

    Islam A, Reza SM, Iftekharuddin KM (2013) Multifractal texture estimation for detection and segmentation of brain tumors. IEEE Trans Biomed Eng. 60(11):3204–3215 (ISSN-0018-9294)

  23. 23.

    Ulku EE, Camurcu AY (2013) Computer aided brain tumor detection with histogram equalization and morphological image processing techniques. In: ICECCO (ISSN-978-1-4799-3343-3)

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Acknowledgements

The authors of this paper express their warm regards to doctors at Rajendra institute of medical sciences for providing valuable information about this topic.

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Correspondence to Atish Chaudhary.

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Chaudhary, A., Bhattacharjee, V. An efficient method for brain tumor detection and categorization using MRI images by K-means clustering & DWT. Int. j. inf. tecnol. 12, 141–148 (2020). https://doi.org/10.1007/s41870-018-0255-4

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Keywords

  • Tumor
  • DNA
  • Classifier
  • SVM
  • K-means
  • Cancer
  • Benign
  • Malignant