Image segmentation and classification of cytological cells based on multi-features clustering and chain splitting model_Journal of Biomedical Engineering

Authors：

 WANG Pin ¹ , LIU Qianqian ¹ , WANG Lirui ¹ , LI Yongming ^1,2 , LIU Shujun ¹ , YAN Fang ¹

1. College of Communication Engineering, Chongqing University, Chongqing 400030, P.R.China;
2. College of Biomedical Engineering, The Third Military Medical University, Chongqing 400038, P.R.China;

Corresponding author：

WANG Pin, Email: wangpin@cqu.edu.cn

Keywords：

pancreatic cell; microscopic image segmentation of Pap smear slide; Mean-shift clustering algorithm; chain-like agent genetic algorithm

DOI：

10.7507/1001-5515.201605004

Video：

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The diagnosis of pancreatic cancer is very important. The main method of diagnosis is based on pathological analysis of microscopic image of Pap smear slide. The accurate segmentation and classification of images are two important phases of the analysis. In this paper, we proposed a new automatic segmentation and classification method for microscopic images of pancreas. For the segmentation phase, firstly multi-features Mean-shift clustering algorithm (MFMS) was applied to localize regions of nuclei. Then, chain splitting model (CSM) containing flexible mathematical morphology and curvature scale space corner detection method was applied to split overlapped cells for better accuracy and robustness. For classification phase, 4 shape-based features and 138 textural features based on color spaces of cell nuclei were extracted. In order to achieve optimal feature set and classify different cells, chain-like agent genetic algorithm (CAGA) combined with support vector machine (SVM) was proposed. The proposed method was tested on 15 cytology images containing 461 cell nuclei. Experimental results showed that the proposed method could automatically segment and classify different types of microscopic images of pancreatic cell and had effective segmentation and classification results. The mean accuracy of segmentation is 93.46%±7.24%. The classification performance of normal and malignant cells can achieve 96.55%±0.99% for accuracy, 96.10%±3.08% for sensitivity and 96.80%±1.48% for specificity.

Citation： WANG Pin, LIU Qianqian, WANG Lirui, LI Yongming, LIU Shujun, YAN Fang. Image segmentation and classification of cytological cells based on multi-features clustering and chain splitting model. Journal of Biomedical Engineering, 2017, 34(4): 614-621. doi: 10.7507/1001-5515.201605004 Copy

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15.	Li Kuan, Lu Zhi, Liu Wenyin, et al. Cytoplasm and nucleus segmentation in cervical smear images using Radiating GVF Snake. Pattern Recognit, 2012, 45(4): 1255-1264.
16.	Bamford P, Lovell B. Unsupervised cell nucleus segmentation with active contours. Signal Processing, 1998, 71(2): 203-213.
17.	Malek J, Sebri A, Mabrouk S, et al. Automated breast cancer diagnosis based on GVF-Snake segmentation, wavelet features extraction and fuzzy classification. J Signal Process Syst, 2009, 55(1/3): 49-66.
18.	Kudo M, Sklansky J. Comparison of algorithms that select features for pattern classifiers. Pattern Recognit, 2000, 33(1): 25-41.
19.	Filipczuk P, Fevens T, Krzyzak A, et al. Computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. IEEE Trans Med Imaging, 2013, 32(12): 2169-2178.
20.	王品, 胡先玲, 谢文宾, 等. 多尺度区域生长与去粘连模型的乳腺细胞分割. 仪器仪表学报, 2015, 36(7): 1653-1659.
21.	Recky M, Leberl F. Windows detection using k-means in CIE-Lab color space// 2010 20th International Conference on Pattern Recognition. Istanbul, Turkey: IEEE, 2010: 356-359.
22.	Fukunaga K A, Hostetler L D. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 1975, 21(1): 32-40.
23.	Rodenacker K, Bengtsson E. A feature set for cytometry on digitized microscopic images. Anal Cell Pathol, 2003, 25(1): 1-36.
24.	Nielsen B, Albregtsen F, Danielsen H E. Statistical nuclear texture analysis in cancer research: a review of methods and applications. Crit Rev Oncog, 2008, 14(2/3): 89-164.
25.	Li Yongming, Zeng Xiaoping, Han Liang, et al. Two coding based adaptive parallel co-genetic algorithm with double agents structure. Eng Appl Artif Intell, 2010, 23(4): 526-542.

1. Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer, 2015, 136(5): E359-E386.
2. Bray F, Ren Jiansong, Masuyer E, et al. Global estimates of cancer prevalence for 27 sites in the adult population in 2008. Int J Cancer, 2013, 132(5): 1133-1145.
3. 李伟冬, 贾林. 结直肠癌的社区筛查及早期诊治. 中华临床医师杂志(电子版), 2015, 9(5): 724-726.
4. Jefferson A L, Massaro J M, Beiser A S, et al. Inflammatory markers and neuropsychological functioning: the Framingham Heart Study. Neuroepidemiology, 2011, 37(1): 21-30.
5. Ormarsson O T, Gudmundsdottir I, Marvik R. Diagnosis and treatment of gastric heterotopic pancreas. World J Surg, 2006, 30(9): 1682-1689.
6. Kobayashi N. Bioartificial pancreas for the treatment of diabetes. Cell Transplant, 2008, 17(1/2): 11-17.
7. 周传文, 李倩君. 超声内镜联合 EMR 或 ESD 在异位胰腺诊断和治疗中的作用. 世界华人消化杂志, 2010(32): 3476-3479.
8. Saif M W. New developments in the treatment of pancreatic cancer. Highlights from the " 44th ASCO Annual Meeting”. Chicago, IL, USA. May 30-June 3, 2008. JOP, 2008, 9(4): 391-397.
9. Plissiti M E, Nikou C, Charchanti A. Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images. Pattern Recognit Lett, 2011, 32(6): 838-853.
10. Kim K B, Lee S G, Kim G H. Nucleus extraction of uterine cervical Pap-Smears using marker information and watershed algorithm. Journal of Medical Imaging and Health Informatics, 2014, 4(1): 1-7.
11. George Y M, Zayed H H, Roushdy M I, et al. Remote computer-aided breast cancer detection and diagnosis system based on cytological images. IEEE Systems Journal, 2014, 8(3): 949-964.
12. Koyuncu C F, Durmaz I, Cetin A R, et al. A supervised learning model for live cell segmentation//2014 22nd Signal Processing and Communications Applications Conference (SIU). Trabzon, Turkey: IEEE, 2014: 1971-1974.
13. Niwas S I, Palanisamy P, Sujathan K, et al. Analysis of nuclei textures of fine needle aspirated cytology images for breast cancer diagnosis using Complex Daubechies wavelets. Signal Processing, 2013, 93(10, SI): 2828-2837.
14. Cengizler C, Guven M, Avci M. A fluid dynamics-based deformable model for segmentation of cervical cell images. Signal Image and Video Processing, 2014, 8(1): S21-S32.
15. Li Kuan, Lu Zhi, Liu Wenyin, et al. Cytoplasm and nucleus segmentation in cervical smear images using Radiating GVF Snake. Pattern Recognit, 2012, 45(4): 1255-1264.
16. Bamford P, Lovell B. Unsupervised cell nucleus segmentation with active contours. Signal Processing, 1998, 71(2): 203-213.
17. Malek J, Sebri A, Mabrouk S, et al. Automated breast cancer diagnosis based on GVF-Snake segmentation, wavelet features extraction and fuzzy classification. J Signal Process Syst, 2009, 55(1/3): 49-66.
18. Kudo M, Sklansky J. Comparison of algorithms that select features for pattern classifiers. Pattern Recognit, 2000, 33(1): 25-41.
19. Filipczuk P, Fevens T, Krzyzak A, et al. Computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. IEEE Trans Med Imaging, 2013, 32(12): 2169-2178.
20. 王品, 胡先玲, 谢文宾, 等. 多尺度区域生长与去粘连模型的乳腺细胞分割. 仪器仪表学报, 2015, 36(7): 1653-1659.
21. Recky M, Leberl F. Windows detection using k-means in CIE-Lab color space// 2010 20th International Conference on Pattern Recognition. Istanbul, Turkey: IEEE, 2010: 356-359.
22. Fukunaga K A, Hostetler L D. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 1975, 21(1): 32-40.
23. Rodenacker K, Bengtsson E. A feature set for cytometry on digitized microscopic images. Anal Cell Pathol, 2003, 25(1): 1-36.
24. Nielsen B, Albregtsen F, Danielsen H E. Statistical nuclear texture analysis in cancer research: a review of methods and applications. Crit Rev Oncog, 2008, 14(2/3): 89-164.
25. Li Yongming, Zeng Xiaoping, Han Liang, et al. Two coding based adaptive parallel co-genetic algorithm with double agents structure. Eng Appl Artif Intell, 2010, 23(4): 526-542.

Journal of Biomedical Engineering

Image segmentation and classification of cytological cells based on multi-features clustering and chain splitting model

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