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Anisotropic diffusion filter based edge enhancement for the segmentation of carotid intima-media layer in ultrasound images using variational level set method without re-initialisation

机译:基于各向异性扩散滤波器的边缘增强,用于使用变分电平集方法在超声图像中进行颈动脉内膜介质层的分割,而无需重新初始化

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In this work an attempt has been made to enhance the edges and segment the boundary of intima-media layer of Common Carotid Artery (CCA) using anisotropic diffusion filter and level set method. Ultrasound B mode longitudinal images of normal and abnormal images of common carotid arteries are used in this study. The images are subjected to anisotropic diffusion filter to generate edge map. This edge map is used as a stopping boundary in variational level set method without re-initialisation to segment the intima-media layer. Geometric features are extracted from this layer and analyzed statistically. Results show that anisotropic diffusion filtering is able to extract the edges in both normal and abnormal images. The obtained edge maps are found to have high contrast and sharp edges. The edge based variational level set method is able to segment the intima-media layer precisely from common carotid artery. The extracted geometrical features such as major axis and extent are found to be statistically significant in differentiating normal and abnormal images. Thus this study seems to be clinically useful in diagnosis of cardiovascular disease.
机译:在这项工作中,已经尝试使用各向异性扩散滤波器和水平设定方法来增强常见颈动脉(CCA)的内膜介质层的边界和分段。本研究使用超声B模式常见颈动脉的正常和异常图像的纵向图像。对图像进行各向异性扩散滤波器以产生边缘图。该边缘映射用作变分级别设置方法中的停止边界,而无需重新初始化以对内部介质层进行分割。从该层中提取几何特征并统计分析。结果表明,各向异性扩散滤波能够在正常和异常图像中提取边缘。找到所获得的边缘图具有高对比度和锋利的边缘。基于边缘的变形水平设定方法能够精确地从常见的颈动脉段分段。在区分正常和异常的图像中,发现诸如主轴和范围的提取的几何特征在统计学上具有统计学意义。因此,该研究似乎在诊断心血管疾病的临床上有用。

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