Segmentation of brain tumors in MRI images using multi-scale gradient vector flow

Abstract

The gradient vector flow (GVF) algorithm has been used extensively as an efficient method for medical image segmentation. This algorithm suffers from poor robustness against noise as well as lack of convergence in small scale details and concavities. As a cure to this problem, in this paper the idea of multi scale is applied to the traditional GVF algorithm for segmentation of brain tumors in MRI images. Using this idea, the active contour is evolved with respect to scaled edge maps in a multi scale manner. The edge detection performance of the modified GVF algorithm is further enhanced by applying a threshold-based edge detector to improve the edge map. The Bspline snake is selected for representation of the active contour, due to its ability to capture corners and its local control. The results showed an improvement of 30% in the accuracy of tumor segmentation against traditional GVF and 10 % as compared to Bspline GVF in the presence of noise, besides the repeatability of the algorithm in contrast to traditional GVF. The clinical evaluation also proved the accuracy and sensitivity of the proposed method as 92.8% and 95.4%, respectively.