Low MSE Based Brain Region Segmentation With CNN Using WLS Filter

@article{1702807,
      author = {Neetu Khichar, Preeti Vyas},
      title = {Low MSE Based Brain Region Segmentation With CNN Using WLS Filter},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {1},
      pages = {28-33},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702807.pdf},
      abstract = {Brain region segmentation in MRI based images is a crucial step for empirical analysis of the main anatomical structures of the brain in large-scale studies. Brain region segmentation is of paramount importance because it helps experts to focus on specific regions of the brain to study them. However, segmentation of the brain region can be a difficult task due to high similarities and correlations of intensity among different regions of the brain image. Therefore in order to mitigate these challenging tasks, there is a need for objective diagnosis and efficient processing of the MRI based brain images. This paper proposes a method in which the brain region is segmented using deep learning based semantic segmentation (CNN) which is combined with WLS filter for better accuracy and low MSE (Mean Square Error).},
      keywords = {Brain region segmentation, Deep learning, CNN, Image processing, WLS filter, MSE},
      month = {July},
  }