Loading...

Brain tumor segmentation based on 3D neighborhood features using rule-based learning

Barzegar, Z ; Sharif University of Technology | 2019

385 Viewed
  1. Type of Document: Article
  2. DOI: 10.1117/12.2523220
  3. Publisher: SPIE , 2019
  4. Abstract:
  5. In order to plan precise treatment or accurate tumor removal surgery, brain tumor segmentation is critical for detecting all parts of tumor and its surrounding tissues. To visualize brain anatomy and detect its abnormalities, we use multi-modal Magnetic Resonance Imaging (MRI) as input. This paper introduces an efficient and automated algorithm based on the 3D bit-plane neighborhood concept for Brain Tumor segmentation using a rule-based learning algorithm. In the proposed approach, in addition to using intensity values in each slice, we consider sets of three consecutive slices to extract information from 3D neighborhood. We construct a Rule base using sequential covering algorithm. Through a rule-based ordering method and a reward/penalty policy, we assign weights to each rule such that the largest weight is assigned to the strongest (mostly referred) rule. Finally, the rules are ranked from the strongest to the weakest. Regarding to the strength of rules in the framework, those with highest weight are selected for voxel labeling. This algorithm is tested on BRATS 2015 training database of High and Low Grade tumors. Dice and Jaccard indices are calculated and comparative analysis is implemented as well. Experimental results indicate competitive performance compared to the state of the art methods
  6. Keywords:
  7. 3D Neighborhood Features ; Bit plane Slicing ; Brain Tumor ; Multi modal Brain MRI ; Rule-based Learning ; Segmentation ; Brain ; Computer vision ; Image segmentation ; Learning algorithms ; Magnetic resonance imaging ; Bit-plane slicing ; Brain MRI ; Brain tumors ; Tumors
  8. Source: 11th International Conference on Machine Vision, ICMV 2018, 1 November 2018 through 3 November 2018 ; Volume 11041 , 2019 ; 0277786X (ISSN) ; 9781510627482 (ISBN)
  9. URL: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11041/1104103/Brain-tumor-segmentation-based-on-3D-neighborhood-features-using-rule/10.1117/12.2523220.short?SSO=1