Wang Guanglei, Wang Pengyu, Han Yechen, Liu Xiuling, Li Yan, Lu Qian
College of Eletronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China.
College of Eletronic and Information Engineering, Hebei University, Baoding, Hebei 071002,
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2017 Jun 1;34(6):869-875. doi: 10.7507/1001-5515.201706030.
In recent years, optical coherence tomography (OCT) has developed into a popular coronary imaging technology at home and abroad. The segmentation of plaque regions in coronary OCT images has great significance for vulnerable plaque recognition and research. In this paper, a new algorithm based on -means clustering and improved random walk is proposed and Semi-automated segmentation of calcified plaque, fibrotic plaque and lipid pool was achieved. And the weight function of random walk is improved. The distance between the edges of pixels in the image and the seed points is added to the definition of the weight function. It increases the weak edge weights and prevent over-segmentation. Based on the above methods, the OCT images of 9 coronary atherosclerotic patients were selected for plaque segmentation. By contrasting the doctor's manual segmentation results with this method, it was proved that this method had good robustness and accuracy. It is hoped that this method can be helpful for the clinical diagnosis of coronary heart disease.
近年来，光学相干断层扫描（OCT）已发展成为国内外一种流行的冠状动脉成像技术。冠状动脉OCT图像中斑块区域的分割对于易损斑块的识别和研究具有重要意义。本文提出了一种基于均值聚类和改进随机游走的新算法，实现了钙化斑块、纤维斑块和脂质池的半自动分割。并对随机游走的权重函数进行了改进。将图像中像素边缘与种子点之间的距离添加到权重函数的定义中。它增加了弱边缘权重并防止过分割。基于上述方法，选取9例冠状动脉粥样硬化患者的OCT图像进行斑块分割。通过将医生的手动分割结果与该方法进行对比，证明该方法具有良好的鲁棒性和准确性。希望该方法能对冠心病的临床诊断有所帮助。
