高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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 & 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 & 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。
  • TAALIMI A, ENSAFI S, QI Hairong, et al. Multimodal dictionary learing and joint sparse representation for HEp-2 cell classification[C]. 18th International Conference, Munich, Germany, 2015, 9351: 308-315. doi: 10.1007/978-3-319-24574- 4_37.
    ENSAFI S, LU Shijian, KASSIM A A, et al. Accurate HEp-2 cell classification based on sparse bag of words coding[J]. Computerized Medical Imaging and Graphics, 2016. doi: 10.1016/j.compmedimag.2016.08.002.
    HOBSON P, LOVELL B C, PERCANNELLA G, et al. HEp-2 staining pattern recognition at cell and specimen levels: Datasets, algorithms and results[J]. Pattern Recognition Letters, 2016, 82: 12-15. doi: 10.1016/j.patrec. 2016.07.013.
    KUSHWAHA AKS, SRIVASTAVA S, and SRIVASTAVA R. Multi-view human activity recognition based on silhouette and uniform rotation invariant local binary patterns[J]. Multimedia Systems, 2016: 1-17. doi: 10.1007/s00530-016- 0505-x.
    QI Xianbiao, ZHAO Guoying, CHEN Jie, et al. HEp-2 cell classification: The role of Gaussian scale space theory as a pre-processing approach[J]. Pattern Recognition Letters, 2016, 82(1): 36-43. doi: 10.1016/j.patrec.2015.12.011.
    LIU Anan, LU Yao, SU Yuting, et al. HEp-2 cells classification via clustered multi-task learning[J]. Neurocomputing, 2016, 195(26): 195-201. doi: 10.1016/j. neucom.2015.06.108.
    PONOMAREV G V and KAZANOV M D. Classification of ANA HEp-2 slide images using morphological features of stained patterns[J]. Pattern Recognition Letters, 2016, 82: 79-84. doi: 10.1016/j.patrec.2016.03.010.
    OJALA T, PIETIKAAINEN M, and HARWOOD D. A comparative study of texture measures with classification based on featured distributions[J]. Pattern Recognition, 1996, 29(1): 51-59. doi: 10.1016/0031-3203(95)00067-4.
    GUO Zhenhua and ZHANG Lei. A completed modeling of local binary pattern operator for texture classification[J]. IEEE Transactions on Image Processing, 2010, 19(6): 1657-1663. doi: 10.1109/TIP.2010.2044957.
    NOSAKA R, OHKAWA Y, and FUKUI K. Feature extraction based on co-occurrence of adjacent local binary pterns[J]. Advances in Image Video Technology-Pacific Rim Symposium, 2011, 7088: 82-91. doi: 10.1007/978-3-642- 25346-1_8.
    QI Xianbiao, XIAO Rong, LI Chunguang, at al. Pairwise rotation invariant co-occurrence local binary pattern[J]. IEEE Transactions on Pattern Analysis and Machine Itegence, 2014, 36(11): 2199-2213. doi: 10.1109/TPAMI.2014. 2316826.
    VARMA M and ZISSERMAN A. A statistical approach to texture classification from single images[J]. International Journal of Computer Vision, 2005, 62(1): 61-81. doi: 10.1007 /s11263-005-4635-4.
    HARALICK RM, SHANMUGAM K, and DINSTEIN I. Textural features for image classification[J]. IEEE Transtions on Systems, Man Cybernetics, 1973, 3(6): 610-621. doi: 10.1109/TSMC.1973.4309314.
    PINHEIRO A M G. Image descriptors based on the edge orientation[C]. The Fourth International Work shop on Semantic Media Adaptation and Personalization, San Sebastain, Spain, 2009: 73-78. doi: 10.1109/SMAP.2009.27.
    SIM D G, KIM H K, and PARK R H. Invariant texture retrieval using modified Zernike moments[J]. Image and Vision Computing, 2004, 22(4): 331342. doi: 10.1016/j. imavis.2003.11.003.
    BIANCONI F, FERNANDEZ A, and MANCINI A. Assessment of rotation-invariant texture classification through Gabor filters and discrete Fourier transform [C]. Proceedings of 20th International Congress on Graphical Engineering, Valencia, Spain, 2008.
    CATALDO S D, BOTTINO A, ISLAM I U, et al. Subclass discriminant analysis of morphological and textural features for HEp-2 staining pattern classification[J]. Pattern Recognition, 2014, 47(7): 2389-2399. doi: 10.1016/j.patcog. 2013.09.024.
    PONOMAREV G V, ARLAZAROV V L, GELFAND M S, et al. ANA HEp-2 cells image classification using number, size, shape and localization of targeted cell regions[J]. Pattern Recognition, 2014, 47(7): 2360-2366. doi: 10.1016/j.patcog. 2013.09.027.
    STOKLASA R, MAJTNER T, and SVOBODA D. Efficient k-NN based HEp-2 cells classifier[J]. Pattern Recognition, 2014, 47(7):2409-2418. doi: 10.1016/j.patcog.2013.09.021.
    SNELL V, CHRISTMAS W, and KITTLER J. HEp-2 fluorescence pattern classification[J]. Pattern Recognition, 2014, 47(7): 2338-2347. doi: 10.1016/j.patcog.2013.10.012.
    QI Xianbiao, XIAO Rong, LI Chunguang, et al. HEp-2 cell classification via fusing texture and shape information[OL]. https://arxiv.org/pdf/1502.04658v1.pdf.
    THEODORAKOPOULOS I, KASTANIOTIS D, et al. HEp-2 cells classification via sparse representation of textural features fused into dissimilarity space[J]. Pattern Recognition, 2013, 47(7): 2367-2378. doi: 10.1016/j.patcog.2013.09.026.
    KONG Xiangfei, LI Kuan, CAO Jingjing, et al. HEp-2 cell pattern classification with discriminative dictionary learning [J]. Pattern Recognition, 2014, 47(7): 2379-2388. doi: 10. 1016/j.patcog.2013.09.025.
    NOSAKA R and FUKUI K. HEp-2 cell classification using rotation invariant co-occurrence among local binary patterns[J]. Pattern Recognition, 2014, 47(7): 2428-2436. doi: 10.1016/j.patcog.2013.09.018.
  • 加载中
计量
  • 文章访问数:  1562
  • HTML全文浏览量:  136
  • PDF下载量:  359
  • 被引次数: 0
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

目录

    /

    返回文章
    返回