期刊文献+

Recognition of Red Cell and Megakaryocyte Based L-Shaped Envelope Function

Recognition of Red Cell and Megakaryocyte Based L-Shaped Envelope Function
原文传递
导出
摘要 In order to explore the cell composition and its metabolism,it is important to let computer recognize the cells and get the counts of different cells for a sample.This paper proposes an L-shaped envelop function and the related fuzzy clustering method as a way to identify the megakaryocyte and the red cell from the sliced marrow image.This method is useful when the staining is insufficient and the color cannot be used as the identifying factor.This method uses the experimental histogram data to fit the L-shaped function and then use it as the envelop for the match test.The fuzzy c-means(FCM) performance index is used to test the adjacent area and get the minimum and finally secure the identification.The new method is not limited to megakaryocyte or red cell and can be used for general purposes of cell recognition.Tests show that this envelop function can ensure the recognition rate with different staining batches and can reach satisfied counting under similar illumination condition. In order to explore the cell composition and its metabolism, it is important to let computer recognize the cells and get the counts of different cells for a sample. This paper proposes an L-shaped envelop function and the related fuzzy clustering method as a way to identify the megakaryocyte and the red cell from the sliced marrow image. This method is useful when the staining is insufficient and the color cannot be used as the identifying factor. This method uses the experimental histogram data to fit the L-shaped function and then use it as the envelop for the match test. The fuzzy c-means (FCM) performance index is used to test the adjacent area and get the minimum and finally secure the identification. The new method is not limited to megakaryocyte or red cell and can be used for general purposes of cell recognition. Tests show that this envelop function can ensure the recognition rate with different staining batches and can reach satisfied counting under similar illumination condition.
出处 《Journal of Shanghai Jiaotong university(Science)》 EI 2012年第6期755-760,共6页 上海交通大学学报(英文版)
基金 the National Nature Science Foundation of China(No.81170507) the Shanghai International Science and Technology Cooperation Foundation Project(No.11140903700)
关键词 L-shaped envelop function marrow cell pattern recognition cell counting 模式识别 计算机 图像识别 理论
  • 相关文献

参考文献9

  • 1ZHENG X T, SHI J, YU Y H, et al. A new method for automatic counting of marrow cells [C]//Proceeding of the 4th International Conference on Biomedical Engi- neering and Informatics. [s.l.]: IEEE, 2011: 44-48.
  • 2ZHENG X T, SHI J, YU Y H, et al. Analysis of leukemia development based on marrow cell images [C]//Proceeding of the 4th International Congress on Image and Signal Processing. [s.1.]: IEEE, 2011: 95-99.
  • 3ABABNEH S Y, PRESCOTT J W, GURCAN M N. Au- tomatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis re- search [J]. Medical Image Analysis, 2011, 15(4): 438-448.
  • 4BROWN E S, CHANT F, BRESSON X. Completely con- vex formulation of the Chan-vese image segmentation model [J]. International Journal of Computer Vision, 2012, 98: 103-121.
  • 5CRuZ-MOTA J, BOGDANOVA I, PAQUIER B, et al. Scale invariant feature transform on the sphere: Theory and applications [J]. International Journal of Computer Vision, 2012, 98: 217-241.
  • 6WANG E Y, GOU Z P, MIAO A M, et al. Recognition of blood cell images based on color fuzzy clustering [J]. Fuzzy Information and Engineering, 2009, 62: 69-75.
  • 7THEERA-UMPON N. Patch-based white blood cell nu- cleus segmentation using fuzzy clustering [J]. Transac- tions on Electrical Engineering, Electronics, and Com- munications, 2005, 3: 15-19.
  • 8艾大萍,尹晓红,刘伯强,刘忠国,袁清伟,李晓梅.一种骨髓细胞识别分类算法的研究[J].中国生物医学工程学报,2009,28(4):549-553. 被引量:4
  • 9ZHENG X T, ZHANG Y W. A fish population counting method using fuzzy artificial neural network [C]//The 2010 International Conference on Progress in Infor- matics and Computing Conference. [s.1.]: IEEE, 2010: 225-228.

二级参考文献8

  • 1刘茜萍,窦万春,蔡士杰,茅长庚.骨髓细胞显微图像的分类认知方法研究[J].计算机应用与软件,2006,23(9):11-13. 被引量:1
  • 2张石,王军辉,董建威,齐晓龙.医学显微图像分割方法研究进展[J].中国生物医学工程学报,2007,26(4):623-629. 被引量:5
  • 3Hou Zhenjie, Ma Shuoshi, Pei Xichun, et al. Studies on segmentation and recognition marrow cells image [ A]. In: Proceedings of International Symposium on Communications and Information Technology [C]. Beijing: IEEE, 2005. 1263 - 1266.
  • 4Yuan Shiqiang, Tan Yonghong. The solutions of equation-based noise detector for an adaptive median filter [ J ]. Pattern Recognition, 2006, 39(11): 2252- 2257.
  • 5Shih P, Liu Chengjun. Comparative assessment of content-based face image retrieval in different color spaces [ J ]. International Journal of Pattern Recognition and Artificial Intelligence, 2005, 19 (7) : 873 - 893.
  • 6Liu Boqiang, Yin Cong, Liu Zhonguo. Automatic segmentation on cell image fusing gray and gradient information [ A ]. In:Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society [ C ]. France: IEEE, 2007. 5624 - 5627.
  • 7Zhang Li, Wang Lu, Tian Yezhuang. Performance assessment based on BP neural networks in knowledge-based companies [A]. In: Proceedings of the 7th World Congress on Intelligent Control and Automation [C]. China: IEEE, 2008. 755- 759.
  • 8Li Panchi,Li Shiyong.Learning algorithm and application of quantum BP neural networks based on universal quantum gates[J].Journal of Systems Engineering and Electronics,2008,19(1):167-174. 被引量:26

共引文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部