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融合纹理与形状特征的HEp-2细胞分类

文登伟 张东波 汤红忠 许海霞

文登伟, 张东波, 汤红忠, 许海霞. 融合纹理与形状特征的HEp-2细胞分类[J]. 电子与信息学报, 2017, 39(7): 1599-1605. doi: 10.11999/JEIT161090
引用本文: 文登伟, 张东波, 汤红忠, 许海霞. 融合纹理与形状特征的HEp-2细胞分类[J]. 电子与信息学报, 2017, 39(7): 1599-1605. doi: 10.11999/JEIT161090
WEN Dengwei, ZHANG Dongbo, TANG Hongzhong, XU Haixia. HEp-2 Cell Classification by Fusing Texture and Shape Features[J]. Journal of Electronics and Information Technology, 2017, 39(7): 1599-1605. doi: 10.11999/JEIT161090
Citation: WEN Dengwei, ZHANG Dongbo, TANG Hongzhong, XU Haixia. HEp-2 Cell Classification by Fusing Texture and Shape Features[J]. Journal of Electronics and Information Technology, 2017, 39(7): 1599-1605. doi: 10.11999/JEIT161090

融合纹理与形状特征的HEp-2细胞分类

doi: 10.11999/JEIT161090
基金项目: 

国家自然科学基金(61602397),湖南省自然科学基金(2017JJ2251, 2017JJ3315),湖南省重点学科建设项目

HEp-2 Cell Classification by Fusing Texture and Shape Features

Funds: 

The National Natural Science Foundation of China (61602397), The Natural Science Foundation of Hunan Province (2017JJ2251, 2017JJ3315), The Key Discipline Construction Project of Hunan Province

  • 摘要: 间接免疫荧光(IIF)HEp-2细胞图像分析是自身免疫疾病诊断的重要依据,然而由于类内的变化与类间的相似性,HEp-2细胞染色模式分类具有很大难度。该文提出一种结合纹理和形状信息的有效分类方法,借鉴CLBP原理,提出具有完整信息描述能力的局部三值模式CLTP(Completed Local Triple Pattern)描述子来提取纹理信息,同时采用IFV(Improved Fisher Vector)模型和Rootsift特征来描绘形状信息,通过纹理和形状信息的结合,最终训练得到SVM分类器在ICPR 2012与ICIP 2013数据集上进行了对比试验。结果表明,所提方法在细胞级测试中优于其它方法,拥有竞争性的分类性能。
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    出版历程
    • 收稿日期:  2016-10-17
    • 修回日期:  2017-04-06
    • 刊出日期:  2017-07-19

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