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-  2016 

血管内超声灰阶图像的自动组织标定

DOI: doi:10.7507/1001-5515.20160049

Keywords: 血管内超声, 组织标定, 纹理特征提取, 局部二值模式, Haar-like, Gentle Adaboost

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Abstract:

为了实现对血管内超声(IVUS)灰阶图像中的血管壁(包括粥样硬化斑块、血管分叉和支架等)进行自动识别和分类, 分别采用局部二值模式(LBP)、Haar-like和Gabor滤波提取图像的纹理特征, 然后采用Gentle Adaboost分类器对降维后的特征数据进行分类, 并优化分类器参数。对临床图像数据的实验结果表明以人工标定的结果作为金标准, 识别脂质斑块的精度可达94.54%, 区分纤维化斑块和钙化斑块的精度可达93.08%, 对血管分叉和支架的识别精度分别可达93.20%和93.50%

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