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Segmentation of the Left Ventricle Using Active Contour Method with Gradient Vector Flow Forces in Short-Axis MRI

机译:主动轮廓法在短轴MRI中使用梯度矢量流力对左心室进行分割

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摘要

In this paper a left ventricle segmentation approach in short-axis MRI is proposed. It is based on an active contour method and gradient vector flow field forces. Firstly, algorithm delineates endocardium using active contour method approach assisted by gradient vector flow field forces. After that, the epicardium is outlined by proposed divergence rays method and corrected by Fourier descriptors to smoothen an epicardium curve. An algorithm has been tested on eight healthy patients and compared to a manual delineation of endo- and epicardium boundaries. Validity of an algorithm is checked by linear regression analysis, correlation coefficients, and RSME errors. Sample Pearson product-moment correlation coefficients between automatic and manual delineation are γen do = 0.95 and γepi = 0.86. The coefficients of determination and RMSEs are R_(ENDO)~2 = 0.9, R_(BPI)~2 = 0.74 and RMSE_(endo) = 5.303 ml, RMSE_(epi) = 21.973 ml, respectively. These experiments confirm accuracy and robustness of the proposed approach.
机译:本文提出了一种短轴MRI中的左心室分割方法。它基于主动轮廓法和梯度矢量流场力。首先,算法在梯度矢量流场力的辅助下利用主动轮廓法描述心内膜。之后,通过提出的发散射线方法勾勒出心外膜,并通过傅立叶描述符进行校正,以使心外膜曲线变得平滑。已经对八名健康患者进行了算法测试,并将其与手动划定内膜和心外膜边界相比较。通过线性回归分析,相关系数和RSME误差检查算法的有效性。自动和手动定界之间的样本Pearson乘积矩相关系数为γendo = 0.95和γepi= 0.86。测定系数和RMSEs分别为R_(ENDO)〜2 = 0.9,R_(BPI)〜2 = 0.74和RMSE_(endo)= 5.303 ml,RMSE_(epi)= 21.973 ml。这些实验证实了所提出方法的准确性和鲁棒性。

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