乳腺癌灶的精确分割是乳腺癌计算机辅助诊断的重要前提.在动态对比增强核磁共振成像(Dynamic contrastenhanced magnetic resonance imaging,DCE-MRI)的图像中,乳腺癌灶具有对比度低、边界模糊及亮度不均匀等特点,传统的活动轮廓模型方...乳腺癌灶的精确分割是乳腺癌计算机辅助诊断的重要前提.在动态对比增强核磁共振成像(Dynamic contrastenhanced magnetic resonance imaging,DCE-MRI)的图像中,乳腺癌灶具有对比度低、边界模糊及亮度不均匀等特点,传统的活动轮廓模型方法很难取得准确的分割结果.本文提出一种结合马尔科夫随机场(Markov random field,MRF)能量和模糊速度函数的活动轮廓模型的半自动分割方法来完成乳腺癌灶的分割,相对于专业医生的手动分割,本文方法具有速度快、可重复性高和分割结果相对客观等优点.首先,计算乳腺DCE-MRI图像的MRF能量,以增强目标区域与周围背景的差异.其次,在能量图中计算每个像素点的后验概率,建立基于后验概率驱动的活动轮廓模型区域项.最后,结合Gabor纹理特征、DCE-MRI时域特征和灰度特征构建模糊速度函数,将其引入到活动轮廓模型中作为边缘检测项.在乳腺癌灶边界处,该速度函数趋向于零,活动轮廓曲线停止演变,完成对乳腺癌灶的分割.实验结果表明,所提出的方法有助于乳腺癌灶在DCE-MRI图像中的准确分割.展开更多
Statistical and contextual information are typically used to detect moving regions in image sequences for a fixed camera.In this paper,we propose a fast and stable linear discriminant approach based on Gaussian Single...Statistical and contextual information are typically used to detect moving regions in image sequences for a fixed camera.In this paper,we propose a fast and stable linear discriminant approach based on Gaussian Single Model(GSM)and Markov Random Field(MRF).The performance of GSM is analyzed first,and then two main improvements corresponding to the drawbacks of GSM are proposed:the latest filtered data based update scheme of the background model and the linear classification judgment rule based on spatial-temporal feature specified by MRF.Experimental results show that the proposed method runs more rapidly and accurately when compared with other methods.展开更多
文摘乳腺癌灶的精确分割是乳腺癌计算机辅助诊断的重要前提.在动态对比增强核磁共振成像(Dynamic contrastenhanced magnetic resonance imaging,DCE-MRI)的图像中,乳腺癌灶具有对比度低、边界模糊及亮度不均匀等特点,传统的活动轮廓模型方法很难取得准确的分割结果.本文提出一种结合马尔科夫随机场(Markov random field,MRF)能量和模糊速度函数的活动轮廓模型的半自动分割方法来完成乳腺癌灶的分割,相对于专业医生的手动分割,本文方法具有速度快、可重复性高和分割结果相对客观等优点.首先,计算乳腺DCE-MRI图像的MRF能量,以增强目标区域与周围背景的差异.其次,在能量图中计算每个像素点的后验概率,建立基于后验概率驱动的活动轮廓模型区域项.最后,结合Gabor纹理特征、DCE-MRI时域特征和灰度特征构建模糊速度函数,将其引入到活动轮廓模型中作为边缘检测项.在乳腺癌灶边界处,该速度函数趋向于零,活动轮廓曲线停止演变,完成对乳腺癌灶的分割.实验结果表明,所提出的方法有助于乳腺癌灶在DCE-MRI图像中的准确分割.
基金Project (No. 10577017) supported by the National Natural Science Foundation of China
文摘Statistical and contextual information are typically used to detect moving regions in image sequences for a fixed camera.In this paper,we propose a fast and stable linear discriminant approach based on Gaussian Single Model(GSM)and Markov Random Field(MRF).The performance of GSM is analyzed first,and then two main improvements corresponding to the drawbacks of GSM are proposed:the latest filtered data based update scheme of the background model and the linear classification judgment rule based on spatial-temporal feature specified by MRF.Experimental results show that the proposed method runs more rapidly and accurately when compared with other methods.
