Sun Zheng, Wang Lixin, Zhou Ya
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2016 Apr;33(2):287-302.
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.
研究了从血管内超声（IVUS）灰度图像中自动识别包括动脉粥样硬化斑块、分支和支架在内的不同血管壁组织的方法。本研究首先使用局部二值模式（LBP）、类哈尔（Haar-like）和伽柏（Gabor）滤波器检测每一帧的纹理特征。然后，设计了一个Gentle Adaboost分类器对组织特征进行分类。这些方法通过临床获取的图像数据进行了验证。以经验丰富的医生获得的手动特征描述结果作为评估准确性的金标准。结果表明，脂质斑块的识别准确率达到94.54%，纤维斑块和钙化斑块的分类精度达到93.08%。分支和支架的识别准确率分别高达93.20%和93.50%。
