MRI brain tumor segmentation with region growing method based on the gradients and variances along and inside of the boundary curve

Abstract

Region growing method is a classical method in medical image segmentation. To overcome the difficulty of manual threshold selection and sensitivity to noise, an adaptive region growing method based on the gradients and variances along and inside of the boundary curve is proposed. Firstly, we use the anisotropic diffusion filter to preserve the edge information. Then the new model is given, which chooses the mean variance inside of the boundary curve and the reciprocal of the mean gradient along the curve as the research subjects. The objective function of the model is to add two elements about gradient and variance mentioned above. The minimum of the sum is the optimum result which corresponding to the desirable threshold. In region growing processing step, the threshold is increased gradually and the set of the coarse contour is obtained. Finally, through optimizing the model, the optimal segmentation result can be acquired from the set of contours. In clinical MRI image segmentation, our method can produce very satisfactory results.