摘要
提出了一种新颖的基于先验形状学习的混杂活动轮廓(SHAC)模型,该模型采用变分水平集方法,融合自适应区域信息与边界信息,运用主成分分析的方法从给定的含有目标物体轮廓的训练集学习得到最佳形状信息,并将其作为先验形状。将自适应区域特征和轮廓特征作为局部信息,先验形状作为全局信息,在迭代过程中结合全局和局部信息实现对演化曲线的形变进行指导和约束,达到分割目标物体的目的。通过定量和定性地分析低对比度的乳腺核磁共振图像中的乳腺轮廓的分割,以及具有复杂背景的自然图像中感兴趣区域的分割结果,验证了SHAC模型比传统活动轮廓模型具有更高的准确率,表明了该模型不仅提高了图像分割中对弱边界的识别度,减弱了非目标轮廓的干扰,而且具有良好的抗噪能力。
In this paper,a new Shape-prior based Hybrid Active Contour（SHAC）model was presented for segmentation.By using level set method,this model combines boundary and adaptive region information together and learns an optimal prior shape from the training set.It takes the boundary and adaptive region feature as local information while prior shape as global information.The model combines global and local information in the process of iteration to guide the evolution of deformative curve and achieve the goal of segmenting target objects.Experiments show that compared with GAC,C-V,and RSF models,SHAC model displays its advantages not only in the segmentation of image strong noise and weak boundary,but also in the image with low contrast resolution,complicated background and contributes improved accuracy.
出处
《计算机科学》
CSCD
北大核心
2014年第11期301-305,316,共6页
Computer Science
基金
国家自然科学基金(61273259
61005027
61272223)资助
