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A Novel Method for Echocardiogram Boundary Detection Using Adaptive Neuro-Fuzzy Systems

机译:一种使用自适应神经模糊系统的超声心动图边界检测方法

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Echocardiogram is one of the effective diagnostics tool for cardiac investigations. Artifacts such as speckle, grating lobes, and shadowing in ultrasound images can hamper experts interpretation and impede automated analysis. This is because noise and artifacts cause edges to manifest themselves in different ways from the typical definition; hence they pose challenge to conventional edge detection and noise suppression methods. Thus, a method is proposed to resolve the ambiguous edge definitions in noisy echocardiogram by applying the Adaptive Neuro-Fuzzy Inference System (ANFIS) to determine edgeness based on local image characteristics that are defined by operators using local statistics. The performance of the proposed method is compared to that of a conventional edge detector and another similar method but without learning capability.
机译:超声心动图是心脏调查的有效诊断工具之一。超声图像中的斑点,光栅裂片和阴影等伪影可以妨碍专家解释并阻碍自动分析。这是因为噪音和伪像导致边缘以不同的方式从典型定义中表现出来;因此,它们对传统边缘检测和噪声抑制方法构成挑战。因此,提出了一种方法来通过应用自适应神经模糊推理系统(ANFIS)来解决噪声超声心动图中的模糊边缘定义来确定基于使用本地统计的运算符定义的局部图像特征的编辑。将所提出的方法的性能与传统边缘检测器和另一种类似方法的性能进行比较,但是没有学习能力。

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