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局部特征与多示例学习结合的超声图像分类方法

丁建睿 黄剑华 刘家锋 张英涛

丁建睿, 黄剑华, 刘家锋, 张英涛. 局部特征与多示例学习结合的超声图像分类方法. 自动化学报, 2013, 39(6): 861-867. doi: 10.3724/SP.J.1004.2013.00861
引用本文: 丁建睿, 黄剑华, 刘家锋, 张英涛. 局部特征与多示例学习结合的超声图像分类方法. 自动化学报, 2013, 39(6): 861-867. doi: 10.3724/SP.J.1004.2013.00861
DING Jian-Rui, HUANG Jian-Hua, LIU Jia-Feng, ZHANG Ying-Tao. Combining Local Features and Multi-instance Learning for Ultrasound Image Classification. ACTA AUTOMATICA SINICA, 2013, 39(6): 861-867. doi: 10.3724/SP.J.1004.2013.00861
Citation: DING Jian-Rui, HUANG Jian-Hua, LIU Jia-Feng, ZHANG Ying-Tao. Combining Local Features and Multi-instance Learning for Ultrasound Image Classification. ACTA AUTOMATICA SINICA, 2013, 39(6): 861-867. doi: 10.3724/SP.J.1004.2013.00861

局部特征与多示例学习结合的超声图像分类方法

doi: 10.3724/SP.J.1004.2013.00861
基金项目: 

国家自然科学基金(61073128, 61100097, 60973077)资助

详细信息
    通讯作者:

    丁建睿

Combining Local Features and Multi-instance Learning for Ultrasound Image Classification

Funds: 

Supported by National Natural Science Foundation of China(61073128, 61100097, 60973077)

  • 摘要: 利用全局特征对超声图像进行描述具有一定的局限性,而且对图像进行手工标注的成本过高, 为解决上述问题,本文提出了一种利用局部特征描述超声图像,并结合多示例学习对超声图像进行分类的新方法. 粗略定位图像中的感兴趣区域 (Region of interest, ROI),并提取局部特征,将感兴趣区域看作由局部特征构成的示例包, 采用自组织映射(Self-organizing map, SOM)的方法对示例特征进行矢量量化,采用Bag of words方法将示例特征映射到示例包空间,进而采用传统的支持向量机对示例包进行分类.本文提出的方法在临床超声图像上进行了实验,实验结果表明,该方法具有良好的泛化能力和较高的准确性.
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出版历程
  • 收稿日期:  2011-08-26
  • 修回日期:  2012-06-29
  • 刊出日期:  2013-06-20

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