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文摘
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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. |
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其他语种文摘
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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. |
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来源
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电子学报
,2018,46(7):1601-1608 【核心库】
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DOI
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10.3969/j.issn.0372-2112.2018.07.009
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关键词
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医学图像分析
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机器学习
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随机决策森林
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k-均值聚类
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局部形状结构
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心血管内超声
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中-外膜边界检测
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地址
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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
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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自动化技术、计算机技术 |
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基金
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国家自然科学基金
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文献收藏号
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CSCD:6285012
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