文献类型：期刊文献

英文题名：Intravascular optical coherence tomography image segmentation based on Gaussian mixture model and adaptive fourth-order PDE

作者：Wang, Pengyu[1];Zhu, Hongqing[1];Ling, Xiaofeng[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China

年份：2020

卷号：14

期号：1

起止页码：29

外文期刊名：SIGNAL IMAGE AND VIDEO PROCESSING

收录：;EI(收录号：20192707129677);WOS:【SCI-EXPANDED(收录号:WOS:000512100500004)】；

基金：The authors would like to thank the anonymous reviewers and the associate editor for their insightful comments that significantly improved the quality of this paper. This work was supported by the National Nature Science Foundation of China under Grant 61872143.

语种：英文

外文关键词：Fibrotic plaque; Optical coherence tomography; Partial differential equation; Gaussian mixture model; Image segmentation

摘要：The accuracy of the fibrotic plaque segmentation is vital in identifying the coronary artery stenosis. In this paper, we address an automated approach (APDE-GMM) for separating the fibrotic plaque area of intravascular optical coherence tomography (IV-OCT) images. Under this approach, an objective function consisting of a new energy functional with Rayleigh distribution and the negative log-likelihood function of Gaussian mixture model (GMM) is developed. Also, the study presents an adaptive diffusivity function where the gradient threshold can be associated to suppress the effect of speckle noise. The parameter estimation is carried out by the expectation-maximization technology. In addition, this paper derives a fourth-order partial differential equation (PDE) via Euler-Lagrange equation to obtain the optimal solutions. It has been compared to other segmentation approaches on synthetic and clinical IV-OCT images. The results demonstrate that APDE-GMM segmentates more accurately.