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Detection of Tumor in Brain MR İmages Using Hybrid IKProCM and SVM

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • Radha R

    (SRM Institute of Science and Technology, Computer Science and Engineering)

  • Sasikala E

    (SRM Institute of Science and Technology, Computer Science and Engineering)

  • Prakash M

    (SRM Institute of Science and Technology, Computer Science and Engineering)

Abstract

The most widely used procedure for perceiving lump in brain is Magnetic Resonance Imaging (MRI). Due to the increase in the need for an efficient classification procedure, a hybrid mechanism is proposed in this paper. The work discussed in this paper is a blend of Support Vector Machine (SVM) and Improved Kernel Prospective C-Means algorithm (IKProCM). The proposed approach is a collaborative work of SVM and IKProCM, a cross-breed procedure for perceiving brain lesion. The algorithm enhanced the image using enhancement techniques to improve contrast, resolution and removal of noise. Skull extraction is achieved by applying morphological operations and thresholding mechanism. IKProCM clustering mechanism is used to segment the image to identify the expanse of interest in the MR images. The Size Zone Matrix (SZM) is applied for pulling out required features found in the MRI image. Later SVM procedure segregates the MRI images as defective and non defective.

Suggested Citation

  • Radha R & Sasikala E & Prakash M, 2020. "Detection of Tumor in Brain MR İmages Using Hybrid IKProCM and SVM," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1383-1389, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_142
    DOI: 10.1007/978-3-030-41862-5_142
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