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基于分组主成分分析的光学相干图像降斑算法

Optical Coherent Image Despeckling Algorithm Based on Grouping Principal Component Analysis

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摘要

通过分析临床医学图像中光学相干断层成像(OCT)的相干斑噪声模型,提出了一种基于局部分组主成分分析的光学相干断层图像降斑算法。根据相干图像的统计特征,利用同态滤波将乘性噪声转换为加性噪声;将训练集中待处理的像素及其邻域表示成子块向量,利用块相似性度量对子块进行分组,并用于主成分分析。为有效抑制相干图像中病灶的噪声干扰,将该算法执行两次。实验结果表明:所提算法在降斑的同时较好地保留了图像的细节信息,而且获得了较高的客观评价指标。

Abstract

By analyzing a speckle noise model of optical coherence tomography (OCT) in clinical medical imaging,we propose a despeckling method for OCT images based on the local grouping principal component analysis. On the basis of the statistical characteristics of coherent images, the multiplicative noise is converted into additive noise by homomorphic filtering. By modeling pixel to be processed in the training set and its neighborhoods as a vector, we group the vectors based on the block similarity measure. Then, the principal component analysis is performed. Considering the noise interference in coherent images with the lesion, we perform the algorithm twice. Experimental results show that the proposed algorithm has better results in terms of speckle noise reduction as well as detail preservation, and satisfying objective evaluation index.

Newport宣传-MKS新实验室计划
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中图分类号:TN911.7

DOI:10.3788/aos201838.1017002

所属栏目:医用光学与生物技术

基金项目:国家自然科学基金(61471226)、泰山学者人才工程项目(tsqn20161023)、山东省自然科学杰出青年基金(JQ201516)、山东省自然科学基金(ZR2016FQ04)

收稿日期:2018-03-16

修改稿日期:2018-05-03

网络出版日期:2018-05-08

作者单位    点击查看

方敬:山东师范大学物理与电子科学学院, 山东省医学物理图像处理技术重点实验室,山东省光学与光子器件重点实验室, 山东 济南 250358
滕树云:山东师范大学物理与电子科学学院, 山东省医学物理图像处理技术重点实验室,山东省光学与光子器件重点实验室, 山东 济南 250358
牛四杰:济南大学信息与科学工程学院, 山东 济南 250022
李登旺:山东师范大学物理与电子科学学院, 山东省医学物理图像处理技术重点实验室,山东省光学与光子器件重点实验室, 山东 济南 250358

联系人作者:李登旺(dengwang@sdnu.edu.cn); 方敬(fangjing@sdnu.edu.cn);

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引用该论文

Fang Jing,Teng Shuyun,Niu Sijie,Li Dengwang. Optical Coherent Image Despeckling Algorithm Based on Grouping Principal Component Analysis[J]. Acta Optica Sinica, 2018, 38(10): 1017002

方敬,滕树云,牛四杰,李登旺. 基于分组主成分分析的光学相干图像降斑算法[J]. 光学学报, 2018, 38(10): 1017002

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