Multi-modal Brain Tumor Segmentation Based on Self-organizing Active Contour Model

In this paper, an automatic and practical method based on active contour model (ACM) is proposed for multi-modal brain tumor segmentation. Firstly, we construct a concurrent self-organizing map (CSOM) networks. Then, applying the networks into a local region based ACM framework constructs a SOM based ACM, i.e. self-organizing active contour model (SOAC). Finally, by using SOAC, making tumor segmentation problems to be stated as a process of contour evolution. However, the segmentation task cannot be well performed for singlemodal MRI images due to intensity similarities between brain normal tissues and lesions. For highlighting different tissues, between normal and abnormal, using multi-modal MRI information is an effective way to improve segmentation accuracy, obviously. Therefore, we introduce a global difference strategy, which creates a series of difference images from multi-modal MRI images, namely global difference images (GDI). By reorganizing MRI images and GDI, we propose an automatic segmentation method for brain tumor region extraction with multi-modal MRI images based on SOAC. The effectiveness of the method is tested on the real data from BRATS2013 and part of BRATS2015.