摘要
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%。
Automated characterization of different vessel wall tissues including atherosclerotic plaques,branchings and stents from intravascular ultrasound（IVUS）gray-scale images was addressed.The texture features of each frame were firstly detected with local binary pattern（LBP）,Haar-like and Gabor filter in the present study.Then,a Gentle Adaboost classifier was designed to classify tissue features.The methods were validated with clinically acquired image data.The manual characterization results obtained by experienced physicians were adopted as the golden standard to evaluate the accuracy.Results indicated that the recognition accuracy of lipidic plaques reached 94.54%,while classification precision of fibrous and calcified plaques reached 93.08%.High recognition accuracy can be reached up to branchings 93.20% and stents 93.50%,respectively.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2016年第2期287-294,共8页
Journal of Biomedical Engineering
基金
国家自然科学基金资助项目(61372042)
中央高校基本科研业务费专项资金资助项目(2014ZD31)
