分水岭优化的Snake模型肝脏图像分割

Snake算法是主动轮廓模型的经典算法,是近年来图像分割和视频领域研究的热点。针对Snake模型中存在的初始轮廓敏感和能量函数中曲率约束不足等问题,提出将分水岭变换和主动轮廓模型相结合的主动轮廓分割算法。首先通过引入标记函数和强制最小值技术解决传统分水岭变换可能导致的过分割问题,然后利用改进的强制标记分水岭算法优化Snake模型的初始轮廓曲线,最后通过在Snake模型中增加一项与曲线形状相关的外部力弥补能量约束函数中曲率约束的不足,从而实现更精确的图像分割。改进后的Snake模型应用于腹部MR图像中,对肝脏图像的识别和分割取得了良好效果。

Liver image segmentation algorithm based on the Snake modeland optimized by watershed transformation

Directional Snake is a classical algorithm in the active contour models, and is widely used in the field of image segmentation and video research in the past few years. Aimed at sensitivity to the initial contour and lack of curvature constraints in the formulation of the function, an automatic contour segmentation algorithm based on an improved watershed transformation and active contour model is presented. First, a modified watershed algorithm based on the marker function and the mandatory minimum technology is proposed in this paper to deal with the over-segmentation. Then,the improved watershed algorithm is adopted for pre-segmentation, and the extracted object contour is taken as the initial contour for the Snake model. Finally, an external force,which is related to the curve shape,is added in the Snake model for making up the lack of curvature constraints in the formulation of the energy function for precise segmentation computation. The improved Snake model can achieve good results in the liver image recognition and segmentation when applied to the MR images of the abdomen.