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首页> 外文期刊>Medical image analysis >Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach
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Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach

机译：使用概率方法对血管内超声B型图像中的腔边界进行分割

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Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels and is particularly useful for studying atherosclerosis. In this paper, we present a computational method for the delineation of the luminal border in IVUS B-mode images. The method is based in the minimization of a probabilistic cost function (that deforms a parametric curve) which defines a probability field that is regularized with respect to the given likelihoods of the pixels belonging to blood and non-blood. These likelihoods are obtained by a Support Vector Machine classifier trained using samples of the lumen and non-lumen regions provided by the user in the first frame of the sequence to be segmented. In addition, an optimization strategy is introduced in which the direction of the steepest descent and Broyden-Fletcher-Goldfarb-Shanno optimization methods are linearly combined to improve convergence. Our proposed method (MRK) is capable of segmenting IVUS B-mode images from different systems and transducer frequencies without the need of any parameter tuning, and it is robust with respect to changes of the B-mode reconstruction parameters which are subjectively adjusted by the interventionist. We validated the proposed method on six 20. MHz and six 40. MHz IVUS stationary sequences corresponding to regions with different degrees of stenosis, and evaluated its performance by comparing the segmentation results with manual segmentation by two observers. Furthermore, we compared our method with the segmentation results on the same sequences as provided by the authors of three other segmentation methods available in the literature. The performance of all methods was quantified using Dice and Jaccard similarity indexes, Hausdorff distance, linear regression and Bland-Altman analysis. The results indicate the advantages of our method for the segmentation of the lumen contour.
机译：血管内超声（IVUS）是一种基于导管的医学成像技术，可产生血管的横截面图像，对研究动脉粥样硬化特别有用。在本文中，我们提出了一种在IVUS B模式图像中描绘腔边界的计算方法。该方法基于最小化概率成本函数（使参数曲线变形）的概率，该函数定义了相对于属于血液和非血液的像素的给定似然性而规则化的概率场。这些可能性是通过使用用户在要分割的序列的第一帧中提供的管腔和非管腔区域的样本训练的支持向量机分类器获得的。此外，引入了一种优化策略，其中将最陡下降方向与Broyden-Fletcher-Goldfarb-Shanno优化方法线性组合以提高收敛性。我们提出的方法（MRK）能够对来自不同系统和换能器频率的IVUS B模式图像进行分割，而无需任何参数调整，并且对于B模式重建参数的变化（由主观调整可以改变）具有鲁棒性。干预主义者。我们在与狭窄程度不同的区域相对应的六个20. MHz和六个40. MHz IVUS固定序列上验证了该方法，并通过比较分割结果和两名观察员的手动分割结果，评估了该方法的性能。此外，我们将我们的方法与在相同序列上的分割结果进行了比较，该结果与文献中其他三种分割方法的作者所提供的结果相同。使用Dice和Jaccard相似性指数，Hausdorff距离，线性回归和Bland-Altman分析来量化所有方法的性能。结果表明了我们的方法在管腔轮廓分割方面的优势。

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