The edge-based level set model gives no satisfactory results for images with weak edge,and the region-based model performs poorly for intensity inhomogeneity images.In this paper,we propose an improved region-based le...The edge-based level set model gives no satisfactory results for images with weak edge,and the region-based model performs poorly for intensity inhomogeneity images.In this paper,we propose an improved region-based level set model that integrates both the gradient information and the region information.The proposed model defines a novel external energy term,which consists of gradient information and signed pressure forces function.In order to eliminate the re-initialization procedure of traditional level set model,an internal energy term is also introduced for the level set function to maintain signed distance function.Compared with traditional models,our model is more robust against images with weak edge and intensity inhomogeneity.Experiments on liver segmentation from abdominal CT images demonstrate the effectiveness and accuracy of the proposed method.展开更多
提出了一种由测地线活动轮廓模型GAC(Geodesic Active Contour)和局部区域信息相结合的图像分割新方法LGAC(Local Geodesic Active Contour)。构造了基于图像局部信息的演化曲线符号压力函数和演化模型,用水平集方法演化实现,零水平集...提出了一种由测地线活动轮廓模型GAC(Geodesic Active Contour)和局部区域信息相结合的图像分割新方法LGAC(Local Geodesic Active Contour)。构造了基于图像局部信息的演化曲线符号压力函数和演化模型,用水平集方法演化实现,零水平集能准确地在目标边缘收敛,对目标背景对比度较低的图像的分割达到理想效果。利用高斯核函数对水平集函数平滑处理以维持演化稳定,节省了计算时间。实验结果证明了该方法的可行性。展开更多
基金Supported by the National Natural Science Foundation of China(60973071)the Natural Science Foundation of Liaoning Province(20092004)
文摘The edge-based level set model gives no satisfactory results for images with weak edge,and the region-based model performs poorly for intensity inhomogeneity images.In this paper,we propose an improved region-based level set model that integrates both the gradient information and the region information.The proposed model defines a novel external energy term,which consists of gradient information and signed pressure forces function.In order to eliminate the re-initialization procedure of traditional level set model,an internal energy term is also introduced for the level set function to maintain signed distance function.Compared with traditional models,our model is more robust against images with weak edge and intensity inhomogeneity.Experiments on liver segmentation from abdominal CT images demonstrate the effectiveness and accuracy of the proposed method.
文摘提出了一种由测地线活动轮廓模型GAC(Geodesic Active Contour)和局部区域信息相结合的图像分割新方法LGAC(Local Geodesic Active Contour)。构造了基于图像局部信息的演化曲线符号压力函数和演化模型,用水平集方法演化实现,零水平集能准确地在目标边缘收敛,对目标背景对比度较低的图像的分割达到理想效果。利用高斯核函数对水平集函数平滑处理以维持演化稳定,节省了计算时间。实验结果证明了该方法的可行性。
