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Detection of Luminal Contour Using Fuzzy Clustering and Mathematical Morphology in Intravascular Ultrasound Images

机译:基于模糊聚类和数学形态学的血管内超声图像光亮轮廓检测

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An innovative application of fuzzy clustering and mathematical morphology for the problem of luminal contour detection in intravascular ultrasound images is presented. Median and standard deviation are used as features for segmentation process. Comparison was made with gold standard segmented images obtained from the average of images segmented by experienced medical doctors. Tests were carried out with 20 in vivo coronary images obtained from different patients. High correlation coefficients were found between lumen regions manually and automatically defined when area, mean gray level, and standard deviation of the lumen regions were compared
机译:提出了模糊聚类和数学形态学在血管内超声图像腔轮廓检测问题上的创新应用。中位数和标准差用作细分过程的特征。与从经验丰富的医生所分割图像的平均值中获得的黄金标准分割图像进行了比较。使用从不同患者获得的20张体内冠状动脉图像进行了测试。在比较管腔区域的面积,平均灰度和标准偏差时,手动和自动定义的管腔区域之间具有较高的相关系数

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