Automatic Brain Tumor Segmentation in Multimodal Magnetic Resonance Images (MRI) Using Brain Symmetry Analysis and Active Contour

The abnormal growth of the brain cells leads to a brain tumor, which has the highest mortality rate. Brain tumor segmentation from magnetic resonance images (MRI) separate the abnormal mass of tissue from normal brain tissues. However, manual brain tumor segmentation from MRI is time-consuming and prone to human errors. Therefore, developing an automatic brain tumor segmentation is an important and challenging task from a medical point of view.

This paper presents an active contour-based method for automatic brain tumor segmentation from multimodal MRI. In the first step, the tumor boundary is detected using the symmetry of the brain structures in the two hemispheres of the brain, followed by the kernel-based fuzzy algorithm. Then, in the second step, the active contour is used to segment the brain tumor from multimodal MRI using an initial contour defined based on the detected boundary.

The proposed method was evaluated on the BraTS2017 dataset, including high- and low-grade tumors. In comparison with other active contour-based methods, the experimental evaluation using Dice (95.22±0.033), Jaccard (91.10±0.062) sensitivity (94.79±0.059), and specificity (99.70±0.003) showed that the proposed method yielded better performance on tumor segmentation.

The proposed automatic tumor segmentation method achieved better segmentation results than other active contour-based methods.