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Lesion Localization and Extreme Gradient Boosting Characterization with Brain Tumor MRI Images

  • P. M. Siva RajaEmail author
  • K. Ramanan
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 37)

Abstract

Detection of tumor lesion with their precise location and characterization is an important task in the brain tumor diagnosis from the magnetic resonance (MR) images. But it is a time-taking task and error-prone process by radiologists or clinical experts. Several works have been introduced in brain tumor detection but it failed to discover the exact location and characterization of the lesion. In order to improve the automatic tumor lesion localization and characterization, an efficient machine learning technique called Lee Filtered Bivariate Correlative Regression based Extreme Gradient Boosting (LFBCR-EGB) is introduced. Initially “n” numbers of MRI brain images are taken from the database. The LFBCR-EGB technique comprises three major processes, namely preprocessing, lesion localization, and characterization. The regression function is used to find the positive and negative similarity between the pixels in an image. The negative similarity result provides the exact localization results with minimum time. Finally, the lesion characterization is done by applying an extreme gradient boosting technique to improve the accuracy with certain features. The features are extracted from the ROI and they construct several weak learners. The ID3 classifier is used as weak learners of the extreme gradient boosting technique to classify normal or abnormal tissue based on the information gain. In this way, the LFBCR-EGB technique performs accurate and fast tumor detection along with the exact location of the tumor. Experimental evaluation of the proposed LFBCR-EGB technique is carried out using MRI brain image database with different factors such as peak signal-to-noise ratio, lesion localization time, and classification accuracy. The simulation results show that the proposed LFBCR-EGB technique obtains better results in terms of peak signal ratio and classification accuracy with lesion localization time.

Keywords

Magnetic resonance (MR) images Lesion localization Characterization Bivariate correlated regression Feature extraction Extreme gradient boosting technique 

References

  1. 1.
    A. Arnaud, F. Forbes, N. Coquery, N. Collomb, B. Lemasson, E.L. Barbier, Fully automatic lesion localization and characterization: application to brain tumors using multiparametric quantitative MRI data. IEEE Trans. Med. Imaging 37(7), 1678–1689 (2018)CrossRefGoogle Scholar
  2. 2.
    A. Chaddad, Automated feature extraction in brain tumor by magnetic resonance imaging using Gaussian mixture models. Int. J. Biomed. Imaging. 2015, 1–11 (2015). Hindawi Publishing CorporationCrossRefGoogle Scholar
  3. 3.
    Z. Akkus, A. Galimzianova, A. Hoogi, D.L. Rubin, B.J. Erickson, Deep learning for brain MRI segmentation: state of the art and future directions. J. Digit. Imaging 30(4), 449–459 (2017). SpringerCrossRefGoogle Scholar
  4. 4.
    J. Cheng, W. Huang, S. Cao, R. Yang, W. Yang, Z. Yun, Z. Wang, Q. Feng, Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE 10(12), 1–13 (2015)Google Scholar
  5. 5.
    N. Gupta, P. Khanna, A non-invasive and adaptive CAD system to detect brain tumor from T2-weighted MRIs using customized Otsu’s thresholding with prominent features and supervised learning. Signal Process. Image Commun. 59, 18–26 (2017). ElsevierCrossRefGoogle Scholar
  6. 6.
    J. Amin, M. Sharif, M. Yasmin, S.L. Fernandes, A distinctive approach in brain tumor detection and classification using MRI. Pattern Recogn. Lett. 1–10 (2017). ElsevierGoogle Scholar
  7. 7.
    A. Jayachandran, R. Dhanasekaran, Severity analysis of brain tumor in MRI images using modified multi-texton structure descriptor and Kernel-SVM. Arab. J. Sci. Eng. 39(10), 7073–7086 (2014)CrossRefGoogle Scholar
  8. 8.
    K. Kamnitsas, C. Ledig, V.F.J. Newcombe, J.P. Simpson, A.D. Kane, D.K. Menon, D. Rueckert, B. Glocker, Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017). ElsevierCrossRefGoogle Scholar
  9. 9.
    M. Soltanineja, G. Yang, T. Lambrou, N. Allinson, T.L. Jones, T.R. Barrick, F.A. Howe, X. Ye, Automated brain tumour detection and segmentation using super pixel-based extremely randomized trees in FLAIR MRI. Int. J. Comput. Assist. Radiol. Surg. 12(2), 183–203 (2017). SpringerGoogle Scholar
  10. 10.
    N. Varuna Shree, T.N.R. Kumar, Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inf. 5(1), 23–30 (2018). SpringerGoogle Scholar
  11. 11.
    N.B. Bahadure, A.K. Ray, H.P. Thethi, Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int. J. Biomed. Imaging 2017, 1–12 (2017). HindawiGoogle Scholar
  12. 12.
    O. Charron, A. Lallement, D. Jarnet, V. Noblet, J.-B. Clavier, P. Meyer, Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput. Biol. Med. 95, 43–54 (2018). ElsevierGoogle Scholar
  13. 13.
    S. Pereira, R. Meier, R. McKinley, R. Wiest, V. Alvesb, C.A. Silva, M. Reyes, Enhancing interpretability of automatically extracted machine learning features: application to an RBM-random forest system on brain lesion segmentation, Med. Image Anal. 44, 228–244 (2018). ElsevierCrossRefGoogle Scholar
  14. 14.
    S. Roy, D. Bhattacharyya, S.K. Bandyopadhyay, T.-H. Kim, Heterogeneity of human brain tumor with lesion identification, localization, and analysis from MRI. Inf. Med. Unlocked, 1–12 (2017). ElsevierGoogle Scholar
  15. 15.
    M. Saii, Z. Kraitem, Automatic brain tumor detection in MRI using image processing techniques. Biomed. Stat. Inf. 2(2), 73–76 (2017)Google Scholar
  16. 16.
    Y.-D. Zhang, S. Chen, S.H. Wang, J.-F. Yang, P. Phillips, Magnetic resonance brain image classification based on weighted-type fractional fourier transform and nonparallel support vector machine. Int. J. Imaging Syst. Technol. 25(4), 317–327 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Amrita College of Engineering and Technology NagercoilKanyakumariIndia

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