首页> 外文期刊>Neurocomputing >Active contours driven by localizing region and edge-based intensity fitting energy with application to segmentation of the left ventricle in cardiac CT images
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Active contours driven by localizing region and edge-based intensity fitting energy with application to segmentation of the left ventricle in cardiac CT images

机译:由局部区域和基于边缘的强度拟合能量驱动的主动轮廓,并应用于心脏CT图像中的左心室分割

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摘要

Dual source CT of the heart is a well-known and accepted method for detection of cardiac disease. However, weak edges, touching characters, intensity inhomogeneities and complex background lead LV segmentation to leakage and false boundary, in cardiac CT images. This difficult task is accomplished in this work by establishing a new active contour model in a variational level set formulation. Its external energy functional incorporates an edge-based information fitting term, which is an adaptive diffusion flow (ADF), and responsible for extracting object boundaries, especially segmenting the weak and missing borders, and a localizing region intensity fitting, which localizes the Chan-Vese external energy against intensity inhomogeneity and complex background to improve the robustness of the proposed method. For improving the adaptability of the model, the weighted parameter between them is then designed according to the gradient information of image. Besides, the regularity of the level set function is intrinsically preserved by the length regularization term to ensure the curve smooth. Experimental results demonstrate desirable performance of our extension method for real-world dual source cardiac CT images.
机译:心脏的双源CT是检测心脏疾病的众所周知的公认方法。然而,在心脏CT图像中,边缘较弱,接触特性,强度不均匀和复杂的背景导致LV分割导致渗漏和假边界。在这项工作中,通过以变化的水平集公式建立新的活动轮廓模型,完成了这项艰巨的任务。其外部能量功能包括基于边缘的信息拟合项,即自适应扩散流(ADF),负责提取对象边界(尤其是对弱边界和缺失边界进行分段),以及局部区域强度​​拟合,用于对Chan-借助外部能量来防止强度不均匀和复杂背景,从而提高了该方法的鲁棒性。为了提高模型的适应性,然后根据图像的梯度信息设计它们之间的加权参数。此外,水平集函数的规则性由长度正则项固有地保留,以确保曲线平滑。实验结果证明了我们的扩展方法在实际双源心脏CT图像方面的理想性能。

著录项

  • 来源
    《Neurocomputing》 |2015年第25期|199-210|共12页
  • 作者单位

    College of Automation, Chongqing University, Chongqing 400030, Chongqing, PR China,Department of Mathematics, College of Biomedical Engineering, Third Military Medical University, Chongqing 400038, Chongqing, PR China;

    College of Automation, Chongqing University, Chongqing 400030, Chongqing, PR China;

    Department of Radiology, Southwest Hospital, The Third Military Medical University, Chongqing 400038, Chongqing PR China;

    Department of Radiology, Southwest Hospital, The Third Military Medical University, Chongqing 400038, Chongqing PR China;

    Digital Medicine Research Institute, The Third Military Medical University, Chongqing 400038, Chongqing, PR China;

    Digital Medicine Research Institute, The Third Military Medical University, Chongqing 400038, Chongqing, PR China;

    Department of Mathematics, College of Biomedical Engineering, Third Military Medical University, Chongqing 400038, Chongqing, PR China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Cardiac CT images; Left ventricle; Image segmentation; Level set; Active contours;

    机译:心脏CT图像;左心室;图像分割水平设置;活动轮廓;

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