摘要
由于采用高斯和瑞利分布描述超声图像均存在较大偏差,且分割过程缺乏超声图像边缘信息引导,致使其相应的局部高斯分布拟合(LGDF)模型和局部瑞利分布拟合(LRDF)模型对超声图像分割性能不理想。针对上述问题,提出了一种边缘熵加权的局部Fisher-Tippett(FT)分布拟合模型。该模型根据超声图像中目标和背景在局部区域满足不同的FT分布,利用最大后验概率(MAP)准则导出超声图像分割的最小化能量函数。该能量函数的求解采用水平集方法,且通过在长度正则化项中引入边缘熵构造加权函数,引导活动轮廓更好地捕获分割目标的弱边缘。通过大量真实超声图像实验验证了提出模型在局部FT分布拟合和边缘熵引入2方面的改进均能有效提升分割性能,且在定性和定量对比评价上均优于现有的多种超声图像分割方法。
Local Gaussian distribution fitting(LGDF) or local Rayleigh distribution fitting(LRDF) models often give relatively poor performance on segmenting ultrasound images, due to the large bias in describing ultrasound images by either Gaussian or Rayleigh distribution, and the lack of guidance for ultrasound images edge information during image segmentation. To deal with these problems, an edge entropy weighted local Fisher-Tippett(FT) distribution fitting model was presented in this paper. According to the fact that the object and background in local regions of ultrasound images meet with different FT distributions, the proposed model adopted maximum a posteriori(MAP)probability to derive an energy function to be minimized. The energy function was solved by the level set method.Meanwhile, the edge entropy was included into the length regularization term as a weight function to guide the active contour to better capture the obscure and weak edges of the object. Extensive experiments on synthetic and real ultrasound images have demonstrated that the proposed model can not only achieve an enhancement for the local FT distribution fitting and the inclusion of the edge entropy, but also qualitatively and quantitatively outperform many of the existing methods.
作者
崔文超
徐德伟
孙水发
潘志红
王习东
CUI Wen-chao;XU De-wei;SUN Shui-fa;PAN Zhi-hong;WANG Xi-dong(College of Computer and Information Technology,China Three Gorges University,Yichang Hubei 443002,China;Yichang Key Laboratory of Intelligent Medicine,China Three Gorges University,Yichang Hubei 443002,China;People’s Hospital of China Three Gorges University,Yichang Hubei 443000,China)
出处
《图学学报》
CSCD
北大核心
2022年第2期263-272,共10页
Journal of Graphics
基金
国家自然科学基金项目(61871258,U1703261)。
