MR Image Segmentation Based on GAC Model with Multiscale Gradient Vector Field
基于多尺度梯度矢量场GAC模型的MR医学图像分割

PDE(partial differential equation) based GAC(geodesic active contour) model is useful for medical image segmentation.To improve the local minimum problem of GAC model,usually we need smooth the image with a proper smoothness scale before we acquire the gradients of the image.But it is difficult to choose the smoothness scale as we usually do in acquiring the gradients of the approximating image by smoothening it with a single scale.In order to overcome this drawback,multi-scale gradient vector field is used instead of single-scaled gradient vector of images in GAC model.The multi-scale gradient vector field,which can be obtained by updating the gradient vector for each position of the image from lower to higher levels resolution,is still smooth enough in the whole image and accurate for the main edges of the image.The experimental results show that this improved GAC model is effective for MRI(magnetic resonance imaging) segmentation.