Automated red blood cells extraction from holographic images using fully convolutional neural networks

Faliu Yi; Inkyu Moon; Bahram Javidi

doi:10.1364/BOE.8.004466

Biomedical Optics Express
Vol. 8,
Issue 10,
pp. 4466-4479
(2017)
•https://doi.org/10.1364/BOE.8.004466

Automated red blood cells extraction from holographic images using fully convolutional neural networks

Faliu Yi, Inkyu Moon, and Bahram Javidi

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Faliu Yi, Inkyu Moon, and Bahram Javidi, "Automated red blood cells extraction from holographic images using fully convolutional neural networks," Biomed. Opt. Express 8, 4466-4479 (2017)

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Table of Contents Category
- Microscopy
Optics & Photonics Topics
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The topics in this list come from the Optics and Photonics Topics applied to this article.
Previously assigned OCIS codes
- Digital holography (090.1995)
- Three-dimensional image processing (100.6890)
- Machine vision (150.0150)
- Algorithms (150.1135)
- Medical and biological imaging (170.3880)

About this Article
History
- Original Manuscript: June 13, 2017
- Revised Manuscript: August 7, 2017
- Manuscript Accepted: August 23, 2017
- Published: September 12, 2017

Abstract

In this paper, we present two models for automatically extracting red blood cells (RBCs) from RBCs holographic images based on a deep learning fully convolutional neural network (FCN) algorithm. The first model, called FCN-1, only uses the FCN algorithm to carry out RBCs prediction, whereas the second model, called FCN-2, combines the FCN approach with the marker-controlled watershed transform segmentation scheme to achieve RBCs extraction. Both models achieve good segmentation accuracy. In addition, the second model has much better performance in terms of cell separation than traditional segmentation methods. In the proposed methods, the RBCs phase images are first numerically reconstructed from RBCs holograms recorded with off-axis digital holographic microscopy. Then, some RBCs phase images are manually segmented and used as training data to fine-tune the FCN. Finally, each pixel in new input RBCs phase images is predicted into either foreground or background using the trained FCN models. The RBCs prediction result from the first model is the final segmentation result, whereas the result from the second model is used as the internal markers of the marker-controlled transform algorithm for further segmentation. Experimental results show that the given schemes can automatically extract RBCs from RBCs phase images and much better RBCs separation results are obtained when the FCN technique is combined with the marker-controlled watershed segmentation algorithm.

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Year
Author
Publication

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[Crossref]
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B. Javidi, I. Moon, S. Yeom, and E. Carapezza, “Three-dimensional imaging and recognition of microorganism using single-exposure on-line (SEOL) digital holography,” Opt. Express 13(12), 4492–4506 (2005).
[Crossref] [PubMed]
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F. Yi, I. Moon, and Y. H. Lee, “Extraction of target specimens from bioholographic images using interactive graph cuts,” J. Biomed. Opt. 18(12), 126015 (2013).
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F. Yi, I. Moon, and Y. H. Lee, “Three-dimensional counting of morphologically normal human red blood cells via digital holographic microscopy,” J. Biomed. Opt. 20(1), 016005 (2015).
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B. Rappaz, I. Moon, F. Yi, B. Javidi, P. Marquet, and G. Turcatti, “Automated multi-parameter measurement of cardiomyocytes dynamics with digital holographic microscopy,” Opt. Express 23(10), 13333–13347 (2015).
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[Crossref]
B. Kemper, A. Bauwens, A. Vollmer, S. Ketelhut, P. Langehanenberg, J. Müthing, H. Karch, and G. von Bally, “Label-free quantitative cell division monitoring of endothelial cells by digital holographic microscopy,” J. Biomed. Opt. 15(3), 036009 (2010).
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T. Colomb, E. Cuche, F. Charrière, J. Kühn, N. Aspert, F. Montfort, P. Marquet, and C. Depeursinge, “Automatic procedure for aberration compensation in digital holographic microscopy and applications to specimen shape compensation,” Appl. Opt. 45(5), 851–863 (2006).
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[Crossref] [PubMed]
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[Crossref] [PubMed]
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[Crossref]
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A. Anand, I. Moon, and B. Javidi, “Automated Disease Identification With 3-D Optical Imaging: A Medical Diagnostic Tool,” Proc. IEEE 105(5), 924–946 (2017).
[Crossref]

2017 (2)

Y. Song, E. L. Tan, X. Jiang, J. Z. Cheng, D. Ni, S. Chen, B. Lei, and T. Wang, “Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images,” IEEE Trans. Med. Imaging 36(1), 288–300 (2017).
[Crossref] [PubMed]

A. Anand, I. Moon, and B. Javidi, “Automated Disease Identification With 3-D Optical Imaging: A Medical Diagnostic Tool,” Proc. IEEE 105(5), 924–946 (2017).
[Crossref]

2016 (6)

H. Su, Z. Yin, S. Huh, T. Kanade, and J. Zhu, “Interactive cell segmentation based on active and semi-supervised learning,” IEEE Trans. Med. Imaging 35(3), 762–777 (2016).
[Crossref] [PubMed]

X. He, C. V. Nguyen, M. Pratap, Y. Zheng, Y. Wang, D. R. Nisbet, R. J. Williams, M. Rug, A. G. Maier, and W. M. Lee, “Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy,” Biomed. Opt. Express 7(8), 3111–3123 (2016).
[Crossref] [PubMed]

B. Gu, X. Sun, and V. Sheng, “Structural minimax probability machine,” IEEE Trans. Neural Netw. Learn. Syst. 99, 1–11 (2016).
[PubMed]

A. Takeki, T. Trinh, R. Yoshihashi, R. Kawakami, M. Iida, and T. Naemura, “Combining deep features for object detection at various scales: finding small birds in landscape images,” IPSJ Transactions on Computer Vision and Applications 8(1), 5 (2016).
[Crossref]

F. Merola, P. Memmolo, L. Miccio, R. Savoia, M. Mugnano, A. Fontana, G. D’Ippolito, A. Sardo, A. Iolascon, A. Gambale, and P. Ferraro, “Tomographic flow cytometry by digital holography,” Light Sci. Appl. 6(4), e16241 (2016).
[Crossref]

P. Y. Liu, L. K. Chin, W. Ser, H. F. Chen, C. M. Hsieh, C. H. Lee, K. B. Sung, T. C. Ayi, P. H. Yap, B. Liedberg, K. Wang, T. Bourouina, and Y. Leprince-Wang, “Cell refractive index for cell biology and disease diagnosis: past, present and future,” Lab Chip 16(4), 634–644 (2016).
[Crossref] [PubMed]

2015 (3)

F. Yi, I. Moon, and Y. H. Lee, “Three-dimensional counting of morphologically normal human red blood cells via digital holographic microscopy,” J. Biomed. Opt. 20(1), 016005 (2015).
[Crossref] [PubMed]

B. Rappaz, I. Moon, F. Yi, B. Javidi, P. Marquet, and G. Turcatti, “Automated multi-parameter measurement of cardiomyocytes dynamics with digital holographic microscopy,” Opt. Express 23(10), 13333–13347 (2015).
[Crossref] [PubMed]

J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Netw. 61, 85–117 (2015).
[Crossref] [PubMed]

2014 (5)

X. Yu, J. Hong, C. Liu, M. Cross, D. T. Haynie, and M. K. Kim, “Four-dimensional motility tracking of biological cells by digital holographic microscopy,” J. Biomed. Opt. 19(4), 045001 (2014).
[Crossref] [PubMed]

X. Yu, J. Hong, C. Liu, and M. Kim, “Review of digital holographic microscopy for three-dimensional profiling and tracking,” Opt. Eng. 53(11), 112306 (2014).
[Crossref]

P. Memmolo, L. Miccio, F. Merola, O. Gennari, P. A. Netti, and P. Ferraro, “3D morphometry of red blood cells by digital holography,” Cytometry A 85(12), 1030–1036 (2014).
[Crossref] [PubMed]

P. Roma, L. Siman, F. Amaral, U. Agero, and O. Mesquita, “Total three-dimensional imaging of phase objects using defocusing microscopy: Application to red blood cells,” Appl. Phys. Lett. 104(25), 251107 (2014).
[Crossref]

C. Lu and M. Mandal, “Toward automatic mitotic cell detection and segmentation in multispectral histopathological images,” IEEE J. Biomed. Health Inform. 18(2), 594–605 (2014).
[Crossref] [PubMed]

2013 (4)

F. Yi, I. Moon, and Y. H. Lee, “Extraction of target specimens from bioholographic images using interactive graph cuts,” J. Biomed. Opt. 18(12), 126015 (2013).
[Crossref] [PubMed]

I. Moon, A. Anand, M. Cruz, and B. Javidi, “Identification of malaria-infected red blood cells via digital shearing interferometry and statistical inference,” IEEE Photonics J. 5(5), 6900207 (2013).
[Crossref]

L. B. Dorini, R. Minetto, and N. J. Leite, “Semiautomatic white blood cell segmentation based on multiscale analysis,” IEEE J. Biomed. Health Inform. 17(1), 250–256 (2013).
[Crossref] [PubMed]

F. Yi, I. Moon, B. Javidi, D. Boss, and P. Marquet, “Automated segmentation of multiple red blood cells with digital holographic microscopy,” J. Biomed. Opt. 18(2), 026006 (2013).
[Crossref] [PubMed]

2012 (4)

F. Merola, L. Miccio, P. Memmolo, M. Paturzo, S. Grilli, and P. Ferraro, “Simultaneous optical manipulation, 3-D tracking, and imaging of micro-objects by digital holography in microfluidics,” IEEE Photonics J. 4(2), 451–454 (2012).
[Crossref]

I. Moon, B. Javidi, F. Yi, D. Boss, and P. Marquet, “Automated statistical quantification of three-dimensional morphology and mean corpuscular hemoglobin of multiple red blood cells,” Opt. Express 20(9), 10295–10309 (2012).
[Crossref] [PubMed]

A. Anand, V. Chhaniwal, N. Patel, and B. Javidi, “Automatic identification of malaria infected RBC with digital holographic microscopy using correlation algorithms,” IEEE Photonics J. 4(5), 1456–1464 (2012).
[Crossref]

G. Nehmetallah and P. Banerjee, “Applications of digital and analog holography in three-dimensional imaging,” Adv. Opt. Photonics 4(4), 472–553 (2012).
[Crossref]

2010 (2)

B. Kemper, A. Bauwens, A. Vollmer, S. Ketelhut, P. Langehanenberg, J. Müthing, H. Karch, and G. von Bally, “Label-free quantitative cell division monitoring of endothelial cells by digital holographic microscopy,” J. Biomed. Opt. 15(3), 036009 (2010).
[Crossref] [PubMed]

N. T. Shaked, L. L. Satterwhite, N. Bursac, and A. Wax, “Whole-cell-analysis of live cardiomyocytes using wide-field interferometric phase microscopy,” Biomed. Opt. Express 1(2), 706–719 (2010).
[Crossref] [PubMed]

2009 (2)

I. Moon, M. Daneshpanah, B. Javidi, and A. Stern, “Automated three-dimensional identification and tracking of micro/nanobiological organisms by computational holographic microscopy,” Proc. IEEE 97(6), 990–1010 (2009).
[Crossref]

Y. S. Choi and S. J. Lee, “Three-dimensional volumetric measurement of red blood cell motion using digital holographic microscopy,” Appl. Opt. 48(16), 2983–2990 (2009).
[Crossref] [PubMed]

2008 (1)

I. Moon and B. Javidi, “3-D visualization and identification of biological microorganisms using partially temporal incoherent light in-line computational holographic imaging,” IEEE Trans. Med. Imaging 27(12), 1782–1790 (2008).
[Crossref] [PubMed]

2007 (2)

I. Moon and B. Javidi, “3D identification of stem cells by computational holographic imaging,” J. R. Soc. Interface 4(13), 305–313 (2007).
[Crossref] [PubMed]

W. Choi, C. Fang-Yen, K. Badizadegan, S. Oh, N. Lue, R. R. Dasari, and M. S. Feld, “Tomographic phase microscopy,” Nat. Methods 4(9), 717–719 (2007).
[Crossref] [PubMed]

2006 (4)

T. Colomb, E. Cuche, F. Charrière, J. Kühn, N. Aspert, F. Montfort, P. Marquet, and C. Depeursinge, “Automatic procedure for aberration compensation in digital holographic microscopy and applications to specimen shape compensation,” Appl. Opt. 45(5), 851–863 (2006).
[Crossref] [PubMed]

I. Moon and B. Javidi, “Volumetric three-dimensional recognition of biological microorganisms using multivariate statistical method and digital holography,” J. Biomed. Opt. 11(6), 064004 (2006).
[Crossref] [PubMed]

F. Dubois, C. Yourassowsky, O. Monnom, J. C. Legros, O. Debeir, P. Van Ham, R. Kiss, and C. Decaestecker, “Digital holographic microscopy for the three-dimensional dynamic analysis of in vitro cancer cell migration,” J. Biomed. Opt. 11(5), 054032 (2006).
[Crossref] [PubMed]

X. Yang, H. Li, and X. Zhou, “Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy,” IEEE Trans. Circ. Syst. 53(11), 2405–2414 (2006).
[Crossref]

2005 (2)

P. Marquet, B. Rappaz, P. J. Magistretti, E. Cuche, Y. Emery, T. Colomb, and C. Depeursinge, “Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy,” Opt. Lett. 30(5), 468–470 (2005).
[Crossref] [PubMed]

B. Javidi, I. Moon, S. Yeom, and E. Carapezza, “Three-dimensional imaging and recognition of microorganism using single-exposure on-line (SEOL) digital holography,” Opt. Express 13(12), 4492–4506 (2005).
[Crossref] [PubMed]

2002 (2)

C. Ruberto, A. Dempster, S. Khan, and B. Jarra, “Analysis of infected blood cell images using morphological operators,” Image Vis. Comput. 20(2), 133–146 (2002).
[Crossref]

N. Meuleau and M. Dorigo, “Ant colony optimization and stochastic gradient descent,” Artif. Life 8(2), 103–121 (2002).
[Crossref] [PubMed]

2001 (1)

G. Ongun, U. Halici, K. Leblebicioğlu, V. Atalay, S. Beksaç, and M. Beksaç, “Automated contour detection in blood cell images by an efficient snake algorithm,” Nonlinear Anal Theory Methods Appl. 47(9), 5839–5847 (2001).
[Crossref]

1999 (1)

E. Cuche, P. Marquet, and C. Depeursinge, “Simultaneous amplitude-contrast and quantitative phase-contrast microscopy by numerical reconstruction of Fresnel off-axis holograms,” Appl. Opt. 38(34), 6994–7001 (1999).
[Crossref] [PubMed]

1992 (1)

S. S. Poon, R. K. Ward, and B. Palcic, “Automated image detection and segmentation in blood smears,” Cytometry 13(7), 766–774 (1992).
[Crossref] [PubMed]

Agero, U.

Ahmadi, S.

P. Christ, M. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. Sommer, S. Ahmadi, and B. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” In Proceedings of MICCAI, (2016), pp. 415–423.
[Crossref]

Alliez, P.

E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Fully convolutional neural networks for remote sensing image classification,” In Proceedings of IGARSS, (2016), pp. 5071–5074.
[Crossref]

Amaral, F.

Anand, A.

A. Anand, I. Moon, and B. Javidi, “Automated Disease Identification With 3-D Optical Imaging: A Medical Diagnostic Tool,” Proc. IEEE 105(5), 924–946 (2017).
[Crossref]

Armbruster, M.

Aspert, N.

Atalay, V.

Ayi, T. C.

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Adv. Opt. Photonics (1)

G. Nehmetallah and P. Banerjee, “Applications of digital and analog holography in three-dimensional imaging,” Adv. Opt. Photonics 4(4), 472–553 (2012).
[Crossref]

Appl. Opt. (3)

Y. S. Choi and S. J. Lee, “Three-dimensional volumetric measurement of red blood cell motion using digital holographic microscopy,” Appl. Opt. 48(16), 2983–2990 (2009).
[Crossref] [PubMed]

Appl. Phys. Lett. (1)

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Biomed. Opt. Express (2)

Cytometry (1)

S. S. Poon, R. K. Ward, and B. Palcic, “Automated image detection and segmentation in blood smears,” Cytometry 13(7), 766–774 (1992).
[Crossref] [PubMed]

Cytometry A (1)

P. Memmolo, L. Miccio, F. Merola, O. Gennari, P. A. Netti, and P. Ferraro, “3D morphometry of red blood cells by digital holography,” Cytometry A 85(12), 1030–1036 (2014).
[Crossref] [PubMed]

IEEE J. Biomed. Health Inform. (2)

L. B. Dorini, R. Minetto, and N. J. Leite, “Semiautomatic white blood cell segmentation based on multiscale analysis,” IEEE J. Biomed. Health Inform. 17(1), 250–256 (2013).
[Crossref] [PubMed]

IEEE Photonics J. (3)

IEEE Trans. Circ. Syst. (1)

IEEE Trans. Med. Imaging (3)

H. Su, Z. Yin, S. Huh, T. Kanade, and J. Zhu, “Interactive cell segmentation based on active and semi-supervised learning,” IEEE Trans. Med. Imaging 35(3), 762–777 (2016).
[Crossref] [PubMed]

IEEE Trans. Neural Netw. Learn. Syst. (1)

B. Gu, X. Sun, and V. Sheng, “Structural minimax probability machine,” IEEE Trans. Neural Netw. Learn. Syst. 99, 1–11 (2016).
[PubMed]

Image Vis. Comput. (1)

C. Ruberto, A. Dempster, S. Khan, and B. Jarra, “Analysis of infected blood cell images using morphological operators,” Image Vis. Comput. 20(2), 133–146 (2002).
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IPSJ Transactions on Computer Vision and Applications (1)

J. Biomed. Opt. (7)

F. Yi, I. Moon, and Y. H. Lee, “Extraction of target specimens from bioholographic images using interactive graph cuts,” J. Biomed. Opt. 18(12), 126015 (2013).
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J. R. Soc. Interface (1)

I. Moon and B. Javidi, “3D identification of stem cells by computational holographic imaging,” J. R. Soc. Interface 4(13), 305–313 (2007).
[Crossref] [PubMed]

Lab Chip (1)

Light Sci. Appl. (1)

Nat. Methods (1)

W. Choi, C. Fang-Yen, K. Badizadegan, S. Oh, N. Lue, R. R. Dasari, and M. S. Feld, “Tomographic phase microscopy,” Nat. Methods 4(9), 717–719 (2007).
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Neural Netw. (1)

J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Netw. 61, 85–117 (2015).
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Nonlinear Anal Theory Methods Appl. (1)

Opt. Eng. (1)

X. Yu, J. Hong, C. Liu, and M. Kim, “Review of digital holographic microscopy for three-dimensional profiling and tracking,” Opt. Eng. 53(11), 112306 (2014).
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Opt. Express (3)

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Proc. IEEE (2)

A. Anand, I. Moon, and B. Javidi, “Automated Disease Identification With 3-D Optical Imaging: A Medical Diagnostic Tool,” Proc. IEEE 105(5), 924–946 (2017).
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Fig. 1 Schematic of an off-axis digital holographic microscopy (DHM).

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Fig. 2 Fully convolutional neural networks [38]. Row A: Single-stream net, upsamples stride 32 predictions back to pixels in a single step (FCN-32s); Row B: Fusing predictions from both the final convolutional layer and the pool4 layer for additional prediction (FCN-16s); Row C: Fusing predictions from the final convolutional layer, pool4, and pool3 for additional prediction (FCN-8s).

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Fig. 3 RBC’s phase images and ground truth label images. (a) RBC’s 3D profile obtained by off-axis DHM, (b) A reconstructed RBCs phase image, (c) A ground truth label image for the FCN-1 model, (d) A ground truth label image for the FCN-2 model. Bar is 10μm.

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Fig. 4 Flowchart for (a) FCN-1 model, (b) FCN-2 model.

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Fig. 5 FCN structure used for RBCs phase image segmentation.

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Fig. 6 Segmentation results for the four segmentation algorithms. (a) Original RBC phase images, (b) Segmentation results using FCN-1, (c) Segmentation results using FCN-2, (d) Segmentation results using Yi et al.’s method [26], (e) Segmentation results using Yang et al.’s method [55]).

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Fig. 7 RBCs separation. (a) Connected RBCs region in original RBCs phase images, (b) RBCs separation results using FCN-1, (c) RBCs separation results using FCN-2, (d) RBCs separation results using Yi et al.’s method [26], (e) RBCs separation results using Yang et al.’s method [55].

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Fig. 8 RBCs separation evaluation results.

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Tables (2)

Table 1 Segmentation Accuracy on RBCs Phase Image

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Table 2 RBCs Separation Evaluation

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Equations (3)

Equations on this page are rendered with MathJax. Learn more.

y_{i j} = f_{k s} ({x_{s i + δ i, s j + δ j}} 0 \leq δ i, δ j \leq k)

f_{k s} \circ g_{k^{'} s^{'}} = {(f \circ g)}_{k^{'} + (k - 1) s^{'}, s s^{'}}

S A (S_{s e g}, S_{g t}) = 2 \frac{| S_{s e g} \cap S_{g t} |}{| S_{s e g} | + | S_{g t} |},

	Under Split	Over Split	Encroachment Error
FCN-1	32	2	2
FCN-2	9	1	1
Yi et al. [26]	56	2	4
Yang et al. [55]	34	11	15

	Under Split	Over Split	Encroachment Error
FCN-1	32	2	2
FCN-2	9	1	1
Yi et al. [26]	56	2	4
Yang et al. [55]	34	11	15