A novel generalized gradient vector flow snake model using minimal surface and component-normalized method for medical image segmentation. (April 2016) - Zhu, Shiping; Gao, Ruidong
Highlights: We adopt minimal surface function to take the place of Laplace operator in GGVF snake model. We use component-based normalization method instead of the vector-based normalization in the proposed snake model. Precision, Recall and F1 measure are adopted to evaluate the segmentation results objectively. The proposed snake model has better convergence property to enter into long and thin indentations of medical images. Abstract: Active contours, or snakes, have a wide range of applications in medical image segmentation. Gradient vector flow (GVF) field, generalized GVF field and other external force fields have been proposed to address the problems of traditional snake models, such as low accuracy of segmentation and poor convergence ability in indentations. In order to further solve the two problems, we put forward a novel generalized gradient vector flow snake model using minimal surface and component-normalized method. We adopt minimal surface function instead of Laplace operator to settle the problem of low segmentation accuracy. We also use component-based normalization method instead of conventional vector-based normalization method to improve the ability of snake curve to converge into long and thin indentations. Experimental results and comparisons against other methods indicate that the proposed snake model own the ability to protect weak borders and solve the incorrect segmentation problem effectively. Meantime, our method performs much better than generalized GVF snake model in terms of long and thin indentation.