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

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.

[1]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  P. Lions,et al.  Image selective smoothing and edge detection by nonlinear diffusion. II , 1992 .

[3]  Xiaobo Li,et al.  Adaptive image region-growing , 1994, IEEE Trans. Image Process..

[4]  Andrew Mehnert,et al.  An improved seeded region growing algorithm , 1997, Pattern Recognit. Lett..

[5]  Yingli Lu,et al.  Region growing method for the analysis of functional MRI data , 2003, NeuroImage.

[6]  Joan Serra,et al.  Image segmentation , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[7]  Tan Ying Adaptive and high accurate region growing image segmentation method based on region evolution , 2007 .

[8]  Chao Pan,et al.  MRI brain tumor segmentation based on improved fuzzy c-means method , 2009, International Symposium on Multispectral Image Processing and Pattern Recognition.

[9]  Frank Y. Shih,et al.  Image Segmentation , 2007, Encyclopedia of Biometrics.