Automatic Segmentation of Brain Tumor Image Based on Region Growing with Co-constraint

Abstract

Image segmentation remains an ongoing challenge in medical image processing research. Owing to brain tumor’s inhomogeneous structure and blurred boundary, the segmentation of brain tumor image is not always ideal. Therefore, we propose a novel region growing model that enables to segment the brain tumor image accurately and automatically. The model mainly improves the selection of seed points and the growth rules. Using the method of fusion information with multimodal MRI images is described to select the seed point automatically, which makes the segmentation algorithm more robust. Furthermore, in order to mostly remain the local feature and the boundary information of brain tumor, a spatial texture feature is constructed in this study. Based on the above model, an automatic brain tumor image segmentation algorithm is established, which uses the region growing with the Co-constraint of intensity and spatial texture. In terms of performance evaluation, the proposed method not only outperforms other segmentation algorithms in the accuracy of results, but also has lower computational cost. This is undoubtedly a worthy method of brain tumor image segmentation.