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An Efficient Brain Tumor Detection and Segmentation in MRI Using Parameter-Free Clustering

  • Shiv Naresh Shivhare
  • Shikhar Sharma
  • Navjot Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

Automation in detecting and segmenting brain tumor is the need of the era in order to diagnose human brain magnetic resonance images (MRIs) and required for better treatment planning as compared to the manual process. Manual diagnosis of brain tumor MRI is a time-consuming process and often depends on the expertise of the clinician or radiologist which may lead to a chance of human error. However, automatic brain tumor detection has been a complex task in medical image analysis due to unknown, unstructured nature of abnormality and a huge variability in shape, location, and characteristics of different sub-compartments of the tumor. In this paper, we propose a fully automatic model for brain tumor detection based on parameter-free clustering algorithm and morphological dilation and hole-filling operations. The method is applied to an axial slice of the T1c modality of BRATS 2015 training dataset. In our experiments, we segmented the tumor from the contrast-enhanced T1-weighted image and compared the results with the available ground truth. Results of tumor segmentation achieved of 75% of the Dice similarity coefficient (DSC) for a tumor core region when compared to the ground truth.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Shiv Naresh Shivhare
    • 1
  • Shikhar Sharma
    • 1
  • Navjot Singh
    • 1
  1. 1.National Institute of Technology, UttarakhandSrinagar (Garhwal)India

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