A nucleus location method based on distance estimation_Journal of Biomedical Engineering

Authors：

SUN Wenhao , LU Fuxiang ,  MA Yide

School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, P.R.China;

Corresponding author：

MA Yide, Email: ydma@lzu.edu.cn

Keywords：

localization of nuclei; medical image processing; single target location; distance estimation; boundary matrix; matrix sequence of distance rough estimating

DOI：

10.7507/1001-5515.201708041

Video：

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Abstract Full text Figures/Tables Video References Cited by

To locate the nuclei in hematoxylin-eosin (HE) stained section images more simply, efficiently and accurately, a new method based on distance estimation is proposed in this paper, which shows a new mind on locating the nuclei from a clump image. Different from the mainstream methods, proposed method avoids the operations of searching the combined singles. It can directly locate the nuclei in a full image. Furthermore, when the distance estimation built on the matrix sequence of distance rough estimating (MSDRE) is combined with the fact that a center of a convex region must have the farthest distance to the boundary, it can fix the positions of nuclei quickly and precisely. In addition, a high accuracy and efficiency are achieved by this method in experiments, with the precision of 95.26% and efficiency of 1.54 second per thousand nuclei, which are better than the mainstream methods in recognizing nucleus clump samples. Proposed method increases the efficiency of nuclear location while maintaining the location's accuracy. This can be helpful for the automatic analysis system of HE images by improving the real-time performance and promoting the application of related researches.

Citation： SUN Wenhao, LU Fuxiang, MA Yide. A nucleus location method based on distance estimation. Journal of Biomedical Engineering, 2018, 35(3): 435-442. doi: 10.7507/1001-5515.201708041 Copy

1.	Kirubarajan T, Bar-Shalom Y. Combined segmentation and tracking of overlapping objects with feedback//2001 IEEE Workshop on Multi-Object Tracking, Washington, 2001: 77-84.
2.	Wang W X. Binary image segmentation aggregates based on polygonal approximation and classification of concavities, 1998, 31(10): 1503-1524.
3.	Ong S H, Jayasooriah R, Yeow H H, et al. Decomposition of digital clumps into convex parts by contour tracing and labelling. Pattern Recognit Lett, 1992, 13(11): 789-795.
4.	Beucher S, Lantu Joul C. Use of watershed in contour detection. International workshop on image processing, real-time edge and motion detection, 1979: 391-396.
5.	Liu Lifeng, Sclaroff S. Shape-Guided split and merge of image regions. Visual Form 2001, Heidelberg: Springer, 2001: 367-377.
6.	Ip H S, Yu R P K. Recursive splitting of active contours in multiple clump segmentation. Electron Lett, 1996, 32(17): 1564-1566.
7.	Wang Hui, Zhang Hong, Ray N. Clump splitting via bottleneck detection and shape classification. Pattern Recognit, 2012, 45(7): 2780-2787.
8.	Farhan M, Yli-Harja O, Niemisto A. A novel method for splitting clumps of convex objects incorporating image intensity and using rectangular window-based concavity point-pair search. Pattern Recognit, 2013, 46(3): 741-751.
9.	Latorre A, Alonso-Nanclares L, Muelas S, et al. Segmentation of neuronal nuclei based on clump splitting and a two-step binarization of images. Expert Syst Appl, 2013, 40(16): 6521-6530.
10.	Samsi S, Trefois C, Antony P M, et al. Automated nuclei clump splitting by combining local concavity orientation and graph partitioning//2014 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2014: 412-415.
11.	Ulle A R, Nagabushan T N, Basavaraj V. Clump splitting in histopathological images based on concave points//2015 International Conference on Cognitive Computing and Information Processing (CCIP), 2015: 1-6.
12.	Kumar S, Ong S H, Ranganath S, et al. A rule-based approach for robust clump splitting. Pattern Recognit, 2006, 39(6): 1088-1098.
13.	Janssens T, Antanas L, Derde S, et al. Charisma: an integrated approach to automatic H&E-stained skeletal muscle cell segmentation using supervised learning and novel robust clump splitting. Med Image Anal, 2013, 17(8): 1206-1219.
14.	Bai X Z, Sun C M, Zhou F G. Splitting touching cells based on concave points and ellipse fitting. Pattern Recognit, 2009, 42(11): 2434-2446.
15.	Yeo T E, Jin X C, Ong S H, et al. Clump splitting through concavity analysis. Pattern Recognit Lett, 1994, 15(10): 1013-1018.
16.	Wen Q, Chang H, Parvin B. A Delaunay triangulation approach for segmenting clumps of nuclei//2009 IEEE International Symposium on Biomedical Imaging: From Nano To Macro, 2009, 1-2: 9-12.
17.	Dorfer M, Mattes J. Recursive water flow: a shape decomposition approach for cell clump splitting// 12th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016: 811-815.
18.	Liu X, Wang Y, Tang Z. A clump splitting based method to localize speech balloons in comics//International Conference on Document Analysis and Recognition, 2015: 901-905.
19.	Macenko M, Niethammer M, Marron J S, et al. A method for normalizing histology slides for quantitative analysis//2009 IEEE International Symposium on Biomedical Imaging: From Nano To Macro, 2009, 1-2: 1107.
20.	Cosatto E, Miller M, Graf H P, et al. Grading nuclear pleomorphism on histological micrographs//19th International Conference On Pattern Recognition, 2008, 1-6: 672.

1. Kirubarajan T, Bar-Shalom Y. Combined segmentation and tracking of overlapping objects with feedback//2001 IEEE Workshop on Multi-Object Tracking, Washington, 2001: 77-84.
2. Wang W X. Binary image segmentation aggregates based on polygonal approximation and classification of concavities, 1998, 31(10): 1503-1524.
3. Ong S H, Jayasooriah R, Yeow H H, et al. Decomposition of digital clumps into convex parts by contour tracing and labelling. Pattern Recognit Lett, 1992, 13(11): 789-795.
4. Beucher S, Lantu Joul C. Use of watershed in contour detection. International workshop on image processing, real-time edge and motion detection, 1979: 391-396.
5. Liu Lifeng, Sclaroff S. Shape-Guided split and merge of image regions. Visual Form 2001, Heidelberg: Springer, 2001: 367-377.
6. Ip H S, Yu R P K. Recursive splitting of active contours in multiple clump segmentation. Electron Lett, 1996, 32(17): 1564-1566.
7. Wang Hui, Zhang Hong, Ray N. Clump splitting via bottleneck detection and shape classification. Pattern Recognit, 2012, 45(7): 2780-2787.
8. Farhan M, Yli-Harja O, Niemisto A. A novel method for splitting clumps of convex objects incorporating image intensity and using rectangular window-based concavity point-pair search. Pattern Recognit, 2013, 46(3): 741-751.
9. Latorre A, Alonso-Nanclares L, Muelas S, et al. Segmentation of neuronal nuclei based on clump splitting and a two-step binarization of images. Expert Syst Appl, 2013, 40(16): 6521-6530.
10. Samsi S, Trefois C, Antony P M, et al. Automated nuclei clump splitting by combining local concavity orientation and graph partitioning//2014 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2014: 412-415.
11. Ulle A R, Nagabushan T N, Basavaraj V. Clump splitting in histopathological images based on concave points//2015 International Conference on Cognitive Computing and Information Processing (CCIP), 2015: 1-6.
12. Kumar S, Ong S H, Ranganath S, et al. A rule-based approach for robust clump splitting. Pattern Recognit, 2006, 39(6): 1088-1098.
13. Janssens T, Antanas L, Derde S, et al. Charisma: an integrated approach to automatic H&E-stained skeletal muscle cell segmentation using supervised learning and novel robust clump splitting. Med Image Anal, 2013, 17(8): 1206-1219.
14. Bai X Z, Sun C M, Zhou F G. Splitting touching cells based on concave points and ellipse fitting. Pattern Recognit, 2009, 42(11): 2434-2446.
15. Yeo T E, Jin X C, Ong S H, et al. Clump splitting through concavity analysis. Pattern Recognit Lett, 1994, 15(10): 1013-1018.
16. Wen Q, Chang H, Parvin B. A Delaunay triangulation approach for segmenting clumps of nuclei//2009 IEEE International Symposium on Biomedical Imaging: From Nano To Macro, 2009, 1-2: 9-12.
17. Dorfer M, Mattes J. Recursive water flow: a shape decomposition approach for cell clump splitting// 12th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016: 811-815.
18. Liu X, Wang Y, Tang Z. A clump splitting based method to localize speech balloons in comics//International Conference on Document Analysis and Recognition, 2015: 901-905.
19. Macenko M, Niethammer M, Marron J S, et al. A method for normalizing histology slides for quantitative analysis//2009 IEEE International Symposium on Biomedical Imaging: From Nano To Macro, 2009, 1-2: 1107.
20. Cosatto E, Miller M, Graf H P, et al. Grading nuclear pleomorphism on histological micrographs//19th International Conference On Pattern Recognition, 2008, 1-6: 672.

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