Segmentation of overlapping cervical nuclei based on the identification

doi:10.19682/j.cnki.1005-8885.2018.0027

中国邮电高校学报(英文) ›› 2018, Vol. 25 ›› Issue (5): 83-92.doi: 10.19682/j.cnki.1005-8885.2018.0027

Segmentation of overlapping cervical nuclei based on the identification

赵晶¹,谢怡宁²,卢玉¹,何勇军²

1. 哈尔滨理工大学计算机科学与技术学院
2. 哈尔滨理工大学

收稿日期:2018-01-10 修回日期:2018-07-29 出版日期:2018-10-18 发布日期:2018-10-18
通讯作者: 何勇军 E-mail:Holywit@163.com
基金资助:
中国国家自然科学基金;中国国家自然科学基金;中国国家自然科学基金;高等学校博士学科点专项科研基金;哈尔滨理工大学青年拔尖创新人才培养计划

Segmentation of overlapping cervical nuclei based on the identification

Received:2018-01-10 Revised:2018-07-29 Online:2018-10-18 Published:2018-10-18
Supported by:
National Nature Science Foundation of China;National Nature Science Foundation of China;National Nature Science Foundation of China

摘要/Abstract

摘要： Image segmentation directly determines the performance of automatic screening technique. However, there are overlapping nuclei in nuclei images. It raises a challenge to nuclei segmentation. To solve the problem, a segmentation method of overlapping cervical nuclei based on the identification is proposed. This method consists of three stages: classifier training, recognition and fine segmentation. In the classifier training, feature selection and classifier selection are used to obtain a classifier with high recognition rate. In the recognition, the outputs of the rough segmentation are classified and processed according to their labels. In the fine segmentation, the severely overlapping nuclei are further segmented based on the prior knowledge provided by the recognition. Experiments show that this method can accurately segment overlapping nuclei.

关键词:

overlapping nuclei segmentation, pits detection, deep learning, segmentation strategy

Abstract: Image segmentation directly determines the performance of automatic screening technique. However, there are overlapping nuclei in nuclei images. It raises a challenge to nuclei segmentation. To solve the problem, a segmentation method of overlapping cervical nuclei based on the identification is proposed. This method consists of three stages: classifier training, recognition and fine segmentation. In the classifier training, feature selection and classifier selection are used to obtain a classifier with high recognition rate. In the recognition, the outputs of the rough segmentation are classified and processed according to their labels. In the fine segmentation, the severely overlapping nuclei are further segmented based on the prior knowledge provided by the recognition. Experiments show that this method can accurately segment overlapping nuclei.

Key words: overlapping nuclei segmentation, pits detection, deep learning, segmentation strategy

参考文献

1. Song Y Y, Zhang L, Chen S P, et al. Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning. IEEE Transactions on Biomedical Engineering, 2015, 62(10): 2421–2433

2. Nandy K, Chellappa R, Kumar A, et al. Segmentation of nuclei from 3D microscopy images of tissue via graphcut optimization. IEEE Journal of Selected Topics in Signal Processing, 2016, 10(1): 140–150

3. Sirinukunwattana K, Raza S E A, Tsang Y W, et al. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Transactions on Medical Imaging, 2016, 35(5): 1196–1206

4. Zhang W J, Li H Q. Automated segmentation of overlapped nuclei using concave point detection and segment grouping. Pattern Recognition, 2017, 71: 349–360

5. Song Y Y, Tan E L, Jiang X D, et al. Accurate cervical cell segmentation from overlapping clumps in pap smear images. IEEE Transactions on Medical Imaging, 2017, 36(1): 288–300

6. Lu Z, Carneiro G, Bradley A P. An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells. IEEE Transactions on Image Processing, 2015, 24(4): 1261–1272

7. Guan T, Zhou D X, Liu Y H. Accurate segmentation of partially overlapping cervical cells based on dynamic sparse contour searching and GVF snake model. IEEE Journal of Biomedical and Health Informatics, 2015, 19(4): 1494–1504

8. Hu Z P. Overlapping cervical cell segmentation based on light absorption model. Master Thesis. Guangzhou, China: South China University of Technology, 2016 (in Chinese)

9. Liao M, Zhao Y Q, Zeng Y Z, et al. Automatic segmentation of cell images based on support vector machine and ellipse fitting. Journal of Zhejiang University: Engineering Science, 2017, 51(4): 722–728 (in Chinese)

10. He X Z. Cervical cytology based on mass detection of nuclear segmentation method. Master Thesis. Guangzhou, China: South China University of Technology, 2016 (in Chinese)

11. He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'16), Jun 27-30, 2016, Las Vegas, NV, USA. Piscataway, NJ, USA: IEEE, 2016: 770–778

12. Gacav C, Benligiray B, Topal C. Sequential forward feature selection for facial expression recognition. Proceedings of the 24th Signal Processing and Communication Application Conference (SIU'16), May 16-19, 2016, Zonguldak, Turkey. Piscataway, NJ, USA: IEEE, 2016: 1481–1484

13. Madian N, Jayanthi K B, Suresh S. Contour based segmentation of chromosomes in G-band metaphase images. Proceedings of the 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP'15), Dec 14-16, 2015, Orlando, FL, USA. Piscataway, NJ, USA: IEEE, 2015: 943–947

14. Ulle A R, Nagabushan T N, Basavaraj V. Clump splitting in Histopathological images based on concave points. Proceedings of the 2015 International Conference on Cognitive Computing and Information Processing (CCIP’15), Mar 3-4, 2015, Noida, India. Piscataway, NJ, USA: IEEE, 2015: 6p

Song J, Liang X, Lian Z C. Boundary-to-marker evidence controlled segmentation and mdl-based contour inference for overlapping nuclei. IEEE Journal of Biomedical and Health Informatics, 2017, 21(2): 451–464

Segmentation of overlapping cervical nuclei based on the identification

Segmentation of overlapping cervical nuclei based on the identification

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