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Brain Tumor Image Segmentation using K-means Clustering Algorithm

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  • Kanchana Devi E
  • D. Devi Aruna

Abstract

Brain tumor segmentation aims to separate the different tumor tissues such as active cells, necrotic core, and edema from normal brain tissues of White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). MRI based brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging (MRI) images. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting brain tumor are becoming more and more mature and coming closer to routine clinical applications. The purpose of this paper is to provide a K-means clustering algorithm for MRI-based brain tumor segmentation. K-means clustering algorithm is an unsupervised algorithm and it is used to segment the interest area from the background. However, before applying K-means algorithm, first partial stretching enhancement is applied to the image to improve the quality of the image.

Suggested Citation

  • Kanchana Devi E & D. Devi Aruna, 2017. "Brain Tumor Image Segmentation using K-means Clustering Algorithm," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2(1), pages 209-214, February.
    • Handle: RePEc:jbh:ijsrcs:v2:y2017:i1:id:hcseit172133
    Note: Article URL: https://ijsrcseit.com/CSEIT172133
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