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

YUAN Shao-feng; YANG Feng; LIU Shu-jie; JI Fei; HUANG Jing

doi:10.3969/j.issn.0372-2112.2018.07.009

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

更新时间：2025-07-16

- Media-Adventitia Border Detection Based on Local Shape Structure Classification for Intravascular Ultrasound Images
- Acta Electronica Sinica Vol. 46, Issue 7, Pages: 1601-1608(2018)
- 作者机构：
  
  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
  4. 南方医科大学生物医学工程学院,广东,广州,510515
  5. 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
  6. 华南理工大学电子与信息学院,广东,广州,510641
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61771233, No.61271155)
- DOI：10.3969/j.issn.0372-2112.2018.07.009
  CLC： TP391.4
- Published Online：25 July 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
YUAN Shao-feng, YANG Feng, LIU Shu-jie, et al. Media-Adventitia Border Detection Based on Local Shape Structure Classification for Intravascular Ultrasound Images[J]. Acta Electronica Sinica, 2018, 46(7): 1601-1608.
DOI：

YUAN Shao-feng, YANG Feng, LIU Shu-jie, et al. Media-Adventitia Border Detection Based on Local Shape Structure Classification for Intravascular Ultrasound Images[J]. Acta Electronica Sinica, 2018, 46(7): 1601-1608. DOI： 10.3969/j.issn.0372-2112.2018.07.009.

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

-均值（

-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.

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

-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 detecte

d 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.

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