电子学报 ›› 2018, Vol. 46 ›› Issue (7): 1601-1608.DOI: 10.3969/j.issn.0372-2112.2018.07.009

所属专题: 机器学习与智慧医疗

• 学术论文 • 上一篇    下一篇

基于局部形状结构分类的心血管内超声图像中-外膜边界检测

袁绍锋1,2, 杨丰1,2, 刘树杰3, 季飞3, 黄靖1,2   

  1. 1. 南方医科大学生物医学工程学院, 广东广州 510515;
    2. 南方医科大学广东省医学图像处理重点实验室, 广东广州 510515;
    3. 华南理工大学电子与信息学院, 广东广州 510641
  • 收稿日期:2017-05-19 修回日期:2017-12-15 出版日期:2018-07-25 发布日期:2018-07-25
  • 通讯作者: 杨丰
  • 作者简介:袁绍锋,男,1991年1月出生于广东省东莞市.南方医科大学生物医学工程学院硕士研究生,主要研究方向为机器学习与医学图像处理、深度学习与计算机视觉等.E-mail:shaofeng.yuan.smu@gmail.com
  • 基金资助:
    国家自然科学基金(No.61771233,No.61271155)

Media-Adventitia Border Detection Based on Local Shape Structure Classification for Intravascular Ultrasound Images

YUAN Shao-feng1,2, YANG Feng1,2, LIU Shu-jie3, JI Fei3, HUANG Jing1,2   

  1. 1. School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China;
    2. Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China;
    3. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, Guangdong 510641, China
  • Received:2017-05-19 Revised:2017-12-15 Online:2018-07-25 Published:2018-07-25

摘要: 本文提出了一种基于局部形状结构分类的心血管内超声(Intravascular Ultrasound,IVUS)图像中-外膜边界检测方法.首先利用k-均值(k-means)聚类方法,确定局部形状结构类别;其次通过类别标号索引图像块,并对其进行积分通道特征和自相似性特征提取,构建多分类随机决策森林模型;最后由分类模型寻找IVUS图像的关键点,采用曲线拟合方法,实现IVUS图像中-外膜边界检测.实验结果表明,本文方法能够有效地解决IVUS图像中斑块、伪影和血管分支等造成边缘难以准确检测的问题,与已有算法相比,其JM (Jaccard Measure,JM)达到了88.9%,PAD (Percentage of Area Difference,PAD)降低了19.1%,HD (Hausdorff Distance,HD)减少了9.7%,更准确地识别目标边界的关键点,成功地检测出完整的中-外膜边界.

关键词: 医学图像分析, 机器学习, 随机决策森林, k-均值聚类, 局部形状结构, 心血管内超声, 中-外膜边界检测

Abstract: This paper presents an efficient and effective approach based on local shape structure classification for detecting media-adventitia border in intravascular ultrasound (IVUS) images.First,the category of local shape structures is found by using k-means clustering method.Second,patches from IVUS images indexed by the category are extracted by two kinds of features including integral channel and self-similarities features,and therefore a random decision forest model is constructed.Finally,the key points of testing IVUS images are detected using the trained classification model.Then with the help of curve fitting methods,detection of media-adventitia border is acquired.Experimental results demonstrate that the proposed algorithm effectively relieves the difficulties of interference factors such as plaques,artifacts and side vessel,and more accurately recognizes the key points of target border compared with existing algorithms,detects the whole target border successfully.The Jaccard Measure (JM) of media-adventitia border detected by the algorithm is 88.9%,Percentage of Area Difference (PAD) and Hausdorff Distance (HD) measures are reduced by 19.1% and 9.7% respectively.

Key words: medical image analysis, machine learning, random decision forest, k-means clustering, local shape structure, intravascular ultrasound, media-adventitia border detection

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