基于改进C-V模型的图像分割方法

传统C-V模型分割图像利用图像区域特征，忽略 了边缘等能够反应图像细节的特征。为了达到更好的图像分割效果，对于这些细节信息的处理则显得尤为重要。图像的梯度信息在边缘区域具有较大幅值，在同质区域具有较小幅值，因而可以用图像梯度来反映图像的边缘信息。把边缘信息融入C-V模型，利用同质区域信 息和边缘信息控制曲线演化,则可以达到更好的分割效果。本文提出的新模型克服了C-V模型的一些 缺陷，对背景灰度不均匀或含弱边缘的图像能够获得更好的分割效果。

Method of Image Segmentation Based on Improved C-V Model

The region information of images is used by image segmentation based on C-V model, but features reflecting the detail of images such as edge information is ignored. For getting better results of image segmentation, it is particularly important to deal with these details. The amplitude of an image is larger in the edge region and smaller in the homogeneous region. It can be used to reflect the edge information of an image. By incorporating edge information into C-V model, using both the information of homogeneous regions and the edge information to control the active contours, it will obtaia better results of segmentation. The proposed model can overcome some disadvantages of C-V model, and achieve better image segmentation for those images that have the intensity inhomogeneity in backgrounds or weak edges.