针对目前传统的Snake模型图像分割算法的力场捕捉范围小、对初始轮廓的选取敏感以及对轮廓曲线难以收敛到细小深凹边界的缺陷,提出一种基于Snake模型的脑部CT图像分割新算法.算法首先运用Canny边缘算子对图像进行边缘检测,将边缘检测图像叠加到原始图像上,然后再运用Snake模型和梯度向量流(GVF)Snake模型分别对叠加图像进行分割.实验结果表明,该算法克服了传统Snake模型和GVF Snake模型因边缘轮廓不清晰造成的漏分割情况,防止了GVF Snake模型由于GVF力场的相互作用所造成的过分割现象,同时,还能促使轮廓线收敛到细小深凹边界,提高定位精度,具有更好的分割效果.

Through color information and face contour information,this paper proposes a new feature extraction method based on color information and face contour.First of all,color regions are segmented by using improved color extraction algorithm,and analyzed in order to find candidate faces.Then edges of these candidate faces are detected,in accordance with edge detection points.They are matched with characteristics of face contour to identify the exact location.Finally,false faces are excluded by using mosaic template.Experimental results show that the algorithm has higher accuracy rate,high detection speed,and can detect faces of a certain point of view.