PlumX Metrics
Embed PlumX Metrics

3D-MRI brain tumor detection model using modified version of level set segmentation based on dragonfly algorithm

Symmetry, ISSN: 2073-8994, Vol: 12, Issue: 8
2020
  • 58
    Citations
  • 0
    Usage
  • 45
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    58
    • Citation Indexes
      58
  • Captures
    45
  • Mentions
    1
    • Blog Mentions
      1
      • Blog
        1

Article Description

Accurate brain tumor segmentation from 3D Magnetic Resonance Imaging (3D-MRI) is an important method for obtaining information required for diagnosis and disease therapy planning. Variation in the brain tumor's size, structure, and form is one of the main challenges in tumor segmentation, and selecting the initial contour plays a significant role in reducing the segmentation error and the number of iterations in the level set method. To overcome this issue, this paper suggests a two-step dragonfly algorithm (DA) clustering technique to extract initial contour points accurately. The brain is extracted from the head in the preprocessing step, then tumor edges are extracted using the two-step DA, and these extracted edges are used as an initial contour for the MRI sequence. Lastly, the tumor region is extracted from all volume slices using a level set segmentation method. The results of applying the proposed technique on 3D-MRI images from the multimodal brain tumor segmentation challenge (BRATS) 2017 dataset show that the proposed method for brain tumor segmentation is comparable to the state-of-the-art methods.

Bibliographic Details

Hassan A. Khalil; Saad Darwish; Yasmine M. Ibrahim; Osama F. Hassan

MDPI AG

Computer Science; Chemistry; Mathematics; Physics and Astronomy

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know