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首页> 外文期刊>Multimedia Tools and Applications >Segmentation of cardiac tagged MR images using a snake model based on hybrid gradient vector flow
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Segmentation of cardiac tagged MR images using a snake model based on hybrid gradient vector flow

机译：基于混合梯度矢量流的蛇形模型对心脏标记MR图像的分割

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摘要

In the segmentation of cardiac tagging magnetic resonance (tMR) images, it is difficult to segment the left ventricle automatically by using the traditional segmentation model because of the interference caused by the tags. A new snake model based on hybrid gradient vector flow (HGVF) is proposed by us to improve this segmentation. Due to the different characteristics between endocardium and epicardium of the left ventricle (LV), several gradient vector flows (GVFs) with distinctive boundary information would be fused to segment these two sub regions individually. For segmentation of endocardium, we construct a new HGVF in snake model fused by three independent GVFs. These flows are respectively exported from the original cardiac tMR image, the tags-removed image and the local-filtered image. On the other hand, since the epicardium is with a nearly-circle shape, we construct the other HGVF which is composed of two different GVFs. One of them is derived from the tags-removed image either and the other one is derived from the ideal circle-shape image. Some experiments have been done to validate our new segmentation model. The average overlap of the endocardium segmentation is 89.67% (its mean absolute distance is 1.86 pixels), and the average overlap of the epicardium segmentation is 95.88% (its mean absolute distance is 1.64 pixels). Experimental results show that the proposed method improves the segmentation performance compared to some available methods effectively.
机译：在心脏标记磁共振（tMR）图像的分割中，由于标记的干扰，难以使用传统的分割模型自动分割左心室。我们提出了一种基于混合梯度矢量流（HGVF）的新蛇模型来改进这种分割。由于左心室的心内膜和心外膜之间的特性不同，将融合具有独特边界信息的多个梯度向量流（GVF），以分别分割这两个子区域。对于心内膜的分割，我们在蛇模型中构建了由三个独立的GVF融合的新HGVF。这些流分别从原始心脏tMR图像，标签去除图像和局部过滤图像中导出。另一方面，由于心外膜具有近似圆形的形状，因此我们构建了由两个不同GVF组成的另一个HGVF。其中一个从标签去除图像中导出，另一个从理想的圆形图像中导出。已经进行了一些实验以验证我们的新细分模型。心内膜分割的平均重叠率为89.67％（其平均绝对距离为1.86像素），而心外膜分割的平均重叠为95.88％（其平均绝对距离为1.64像素）。实验结果表明，与现有方法相比，该方法可以有效提高分割效果。

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  • 来源
    《Multimedia Tools and Applications》 |2018年第17期|21879-21904|共26页
  • 作者

    Zhuo Yu; Qian Wang; Wei Xiong; Chengde Zhang; Huaifei Hu;

  • 作者单位

    School of Information and Safety Engineering, Zhongnan University of Economics and Law;

    School of Information and Safety Engineering, Zhongnan University of Economics and Law,Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment;

    College of Computer Science, South-Central University for Nationalities,Faculty of Science, Engineering and building environment, Deakin University;

    School of Information and Safety Engineering, Zhongnan University of Economics and Law;

    The Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Department of Electronic and Electrical Engineering, The University of Sheffield,School of Biomedical Engineering, South-Central University for Nationalities;

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  • 正文语种 eng
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  • 关键词

    The segmentation of left ventricle; Tagging magnetic resonance image; Notch bandstop filtering; GVF snake;

    机译：左心室分割;标记磁共振图像;陷波带阻滤波;GVF蛇;

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