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15 March 2019 Brain tumor segmentation based on 3D neighborhood features using rule-based learning
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Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 1104103 (2019) https://doi.org/10.1117/12.2523220
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany
Abstract
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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zeynab Barzegar and Mansour Jamzad "Brain tumor segmentation based on 3D neighborhood features using rule-based learning", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 1104103 (15 March 2019); https://doi.org/10.1117/12.2523220
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