Detection of epithelial cells in leucorrhea microscopic images based on LBP texture features

Xiao-hui DU; Lin LIU; Jing ZHANG; Xiang-zhou WANG; Guang-ming NI; Juan-xiu LIU

doi:10.3788/YJYXS20193409.0871

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Detection of epithelial cells in leucorrhea microscopic images based on LBP texture features

Image Processing | 更新时间：2020-07-05

- Detection of epithelial cells in leucorrhea microscopic images based on LBP texture features
- Chinese Journal of Liquid Crystals and Displays Vol. 34, Issue 9, Pages: 871-878(2019)
- 作者机构：
  
  电子科技大学光电科学与工程学院, 四川成都 610054
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20193409.0871
  CLC： TP751.1
- Received：30 April 2019，
  
  Accepted：20 May 2019，
  
  Published：05 September 2019
- 稿件说明：
移动端阅览
Xiao-hui DU, Lin LIU, Jing ZHANG, et al. Detection of epithelial cells in leucorrhea microscopic images based on LBP texture features[J]. Chinese journal of liquid crystals and displays, 2019, 34(9): 871-878.
DOI：

Xiao-hui DU, Lin LIU, Jing ZHANG, et al. Detection of epithelial cells in leucorrhea microscopic images based on LBP texture features[J]. Chinese journal of liquid crystals and displays, 2019, 34(9): 871-878. DOI： 10.3788/YJYXS20193409.0871.

摘要

白带常规中显微图像细胞的自动识别一直是悬而未决的难题。上皮细胞是白带显微图像中的主要有型成分，能够直接反应清洁度等指标。针对目前白带常规中人为主观判断效率低的特点，本文提出了一种基于纹理特征的白带显微图像中上皮细胞检测方法。首先，应用形态学方法实现对上皮细胞等前景目标的提取；其次，分析前景目标的局部二值模式纹理特征；最后，用支持向量机实现对上皮细胞的精确分类。实验证明，LBP纹理特征在上皮细胞的检测和识别方面较其他的纹理特征提取器均取得了很好的检测效果，精确率为89.5%，召回率为86.0%。检测效率高，检测时间为304 ms。本文算法已经应用于临床测试中，并取得了很好的临床实验效果。

Abstract

The automatic recognition of microscopic image cells in leucorrhea routine has been an unsolved problem. Epithelial cells are the main visible components in leucorrhea microscopic images

which can reflect the indicators such as cleanliness directly. Aiming at the low efficiency of subjective judgment in leucorrhea routine

a new method for detecting epithelial cells in leucorrhea microscopic images based on texture features was proposed. Firstly

the morphological method was used to extract the foreground target such as epithelial cells. Then

the texture features of the foreground target were analyzed based on local binary pattern. Finally

the support vector machine was used to classify epithelial cells accurately. The experimental results show that LBP achieves better detection results than other texture feature extractors in recognition precision and recall rate with the precision of 89.5% and recall of 86.0%. The detection efficiency is high and the detection time is 304 ms. The proposed algorithm has been applied to clinical testing

and achieved good clinical experimental results.

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Keywords

references

LAOSAI J, CHAMNONGTHAI K. Acute leukemia classification by using SVM and K-Means clustering[C]// Proceedings of 2014 International Electrical Engineering Congress . Chonburi, Thailand: IEEE, 2014: 1-4.

GHOSH P, BHATTACHARJEE D, NASIPURI M. Blood smear analyzer for white blood cell counting: a hybrid microscopic image analyzing technique[J]. Applied Soft Computing , 2016, 46: 629-638.

GAUTAM A, SINGH P, RAMAN B, et al . Automatic classification of leukocytes using morphological features and naïve bayes classifier[C]// Proceedings of 2016 IEEE Region 10 Conference . Singapore: IEEE, 2016: 1023-1027.

HUNG J, CARPENTER A. Applying faster R-CNN for object detection on malaria images[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition . Honolulu, Hawaii, USA: IEEE, 2017: 56-61.

WU M, YAN C B, LIU H Q, et al . Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks[J]. Bioscience Reports , 2018, 38(3): BSR20180289.

ZHANG J, LU S H, WANG X Z, et al . Automatic identification of fungi in microscopic leucorrhea images[J]. Journal of the Optical Society of America A , 2017, 34(9): 1484-1489.

LIU W, ANGUELOV D, ERHAN D, et al . SSD: single shot MultiBox detector[C]// Proceedings of the 14 th European Conference on Computer Vision . Amsterdam, Netherlands: Springer, 2016: 21-37.

REN S Q, HE K M, GIRSHICK R, et al . Faster R-CNN: towards real-time object detection with region proposal network[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017, 39(6): 1137-1149.

REDMON J, DIVVALA S, GIRSHICK R, et al . You only look once: unified, real-time object detection[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas, USA: IEEE, 2016: 779-788.

于跃, 李东明.MRF细胞图像自动分割方法设计[J].液晶与显示, 2019, 34(2):207-214.

YU Y, LI D M. Design of MRF cell image automatic segmentation method[J]. Chinese Journal of Liquid Crystals and Displays , 2019, 34(2): 207-214. (in Chinese)

UIJLINGS J R R, VAN DE SANDE K E A, GEVERS T, et al . Selective search for object recognition[J]. International Journal of Computer Vision , 2013, 104(2): 154-171.

FELZENSZWALB P F, HUTTENLOCHER D P. Efficient graph-based image segmentation[J]. International Journal of Computer Vision , 2004, 59(2): 167-181.

SERRA J. Image Analysis and Mathematical Morphology [M]. London: Academic, 1982.

OJALA T, PIETIKÄINEN M, HARWOOD D. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions[C]// Proceedings of the 12 th International Conference on Pattern Recognition . Jerusalem, Israel, Israel: IEEE, 1994: 582-585.

OJALA T, PIETIKAINEN M, MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2002, 24(7): 971-987.

PLATT J C. Fast training of support vector machines using sequential minimal optimization[M] //SCHÖLKOPF B, BURGES C J C, SMOLA A J. Advances in Kernel Methods . Cambridge: MIT Press, 1999: 185-208.

ZHANG G, MA Z M. Texture feature extraction and description using Gabor wavelet in content-based medical image retrieval[C]// Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition . Beijing: IEEE, 2007: 169-173.

DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]// Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . San Diego, CA, USA: IEEE, 2005: 886-893.

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