A review on brain tumor segmentation of MRI images

Magn Reson Imaging. 2019 Sep:61:247-259. doi: 10.1016/j.mri.2019.05.043. Epub 2019 Jun 11.

Abstract

The process of segmenting tumor from MRI image of a brain is one of the highly focused areas in the community of medical science as MRI is noninvasive imaging. This paper discusses a thorough literature review of recent methods of brain tumor segmentation from brain MRI images. It includes the performance and quantitative analysis of state-of-the-art methods. Different methods of image segmentation are briefly explained with the recent contribution of various researchers. Here, an effort is made to open new dimensions for readers to explore the concerned area of research. Through the entire review process, it has been observed that the combination of Conditional Random Field (CRF) with Fully Convolutional Neural Network (FCNN) and CRF with DeepMedic or Ensemble are more effective for the segmentation of tumor from the brain MRI images.

Keywords: Brain tumor; Classification; Ensemble learning; MRI; Segmentation.

Publication types

  • Review

MeSH terms

  • Algorithms
  • Animals
  • Brain / diagnostic imaging*
  • Brain / pathology
  • Brain Neoplasms / diagnostic imaging*
  • Brain Neoplasms / pathology
  • Cluster Analysis
  • Fuzzy Logic
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Magnetic Resonance Imaging*
  • Neural Networks, Computer*
  • Support Vector Machine