Automatic segmentation and classification of tendon nuclei from IHC stained images

Chan Pang Kuok, Po-Ting Wu, I. Ming Jou, Fong-chin Su, Yung-Nien Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Immunohistochemical (IHC) staining is commonly used for detecting cells in microscopy. It is used for analyzing many types of diseases, e.g. breast cancer. Dispersion problem often exist at cell staining which will affect the accuracy of automatic counting. In this paper, we introduce a new method to overcome this problem. Otsu's thresholding method is first applied to exclude the background, so that only cells with dispersed staining are left at foreground, and then refinement will be applied by local adaptive thresholding method according to the irregularity index of the segmented shape at foreground. The segmentation results are also compared to the refinement results using Otsu's thresholding method. Cell classification based on the shape and color indices obtained from the segmentation result is applied to determine the cell condition into normal, abnormal and suspected abnormal cases.

Original languageEnglish
Title of host publicationSeventh International Conference on Graphic and Image Processing, ICGIP 2015
EditorsXudong Jiang, Xudong Jiang, Yulin Wang, Xudong Jiang, Yulin Wang, Xudong Jiang, Yulin Wang, Yulin Wang
PublisherSPIE
ISBN (Electronic)9781510600584, 9781510600584, 9781510600584, 9781510600584
DOIs
Publication statusPublished - 2015 Jan 1
Event7th International Conference on Graphic and Image Processing, ICGIP 2015 - Singapore, Singapore
Duration: 2015 Oct 232015 Oct 25

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9817
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

Other7th International Conference on Graphic and Image Processing, ICGIP 2015
CountrySingapore
CitySingapore
Period15-10-2315-10-25

Fingerprint

tendons
Tendons
Nucleus
Microscopic examination
Segmentation
Color
staining
nuclei
Cell
cells
Thresholding
Refinement
Adaptive Thresholding
Irregularity
Breast Cancer
irregularities
Microscopy
breast
Counting
counting

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Kuok, C. P., Wu, P-T., Jou, I. M., Su, F., & Sun, Y-N. (2015). Automatic segmentation and classification of tendon nuclei from IHC stained images. In X. Jiang, X. Jiang, Y. Wang, X. Jiang, Y. Wang, X. Jiang, Y. Wang, ... Y. Wang (Eds.), Seventh International Conference on Graphic and Image Processing, ICGIP 2015 [98170J] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9817). SPIE. https://doi.org/10.1117/12.2228579
Kuok, Chan Pang ; Wu, Po-Ting ; Jou, I. Ming ; Su, Fong-chin ; Sun, Yung-Nien. / Automatic segmentation and classification of tendon nuclei from IHC stained images. Seventh International Conference on Graphic and Image Processing, ICGIP 2015. editor / Xudong Jiang ; Xudong Jiang ; Yulin Wang ; Xudong Jiang ; Yulin Wang ; Xudong Jiang ; Yulin Wang ; Yulin Wang. SPIE, 2015. (Proceedings of SPIE - The International Society for Optical Engineering).
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abstract = "Immunohistochemical (IHC) staining is commonly used for detecting cells in microscopy. It is used for analyzing many types of diseases, e.g. breast cancer. Dispersion problem often exist at cell staining which will affect the accuracy of automatic counting. In this paper, we introduce a new method to overcome this problem. Otsu's thresholding method is first applied to exclude the background, so that only cells with dispersed staining are left at foreground, and then refinement will be applied by local adaptive thresholding method according to the irregularity index of the segmented shape at foreground. The segmentation results are also compared to the refinement results using Otsu's thresholding method. Cell classification based on the shape and color indices obtained from the segmentation result is applied to determine the cell condition into normal, abnormal and suspected abnormal cases.",
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Kuok, CP, Wu, P-T, Jou, IM, Su, F & Sun, Y-N 2015, Automatic segmentation and classification of tendon nuclei from IHC stained images. in X Jiang, X Jiang, Y Wang, X Jiang, Y Wang, X Jiang, Y Wang & Y Wang (eds), Seventh International Conference on Graphic and Image Processing, ICGIP 2015., 98170J, Proceedings of SPIE - The International Society for Optical Engineering, vol. 9817, SPIE, 7th International Conference on Graphic and Image Processing, ICGIP 2015, Singapore, Singapore, 15-10-23. https://doi.org/10.1117/12.2228579

Automatic segmentation and classification of tendon nuclei from IHC stained images. / Kuok, Chan Pang; Wu, Po-Ting; Jou, I. Ming; Su, Fong-chin; Sun, Yung-Nien.

Seventh International Conference on Graphic and Image Processing, ICGIP 2015. ed. / Xudong Jiang; Xudong Jiang; Yulin Wang; Xudong Jiang; Yulin Wang; Xudong Jiang; Yulin Wang; Yulin Wang. SPIE, 2015. 98170J (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9817).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Automatic segmentation and classification of tendon nuclei from IHC stained images

AU - Kuok, Chan Pang

AU - Wu, Po-Ting

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AU - Su, Fong-chin

AU - Sun, Yung-Nien

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N2 - Immunohistochemical (IHC) staining is commonly used for detecting cells in microscopy. It is used for analyzing many types of diseases, e.g. breast cancer. Dispersion problem often exist at cell staining which will affect the accuracy of automatic counting. In this paper, we introduce a new method to overcome this problem. Otsu's thresholding method is first applied to exclude the background, so that only cells with dispersed staining are left at foreground, and then refinement will be applied by local adaptive thresholding method according to the irregularity index of the segmented shape at foreground. The segmentation results are also compared to the refinement results using Otsu's thresholding method. Cell classification based on the shape and color indices obtained from the segmentation result is applied to determine the cell condition into normal, abnormal and suspected abnormal cases.

AB - Immunohistochemical (IHC) staining is commonly used for detecting cells in microscopy. It is used for analyzing many types of diseases, e.g. breast cancer. Dispersion problem often exist at cell staining which will affect the accuracy of automatic counting. In this paper, we introduce a new method to overcome this problem. Otsu's thresholding method is first applied to exclude the background, so that only cells with dispersed staining are left at foreground, and then refinement will be applied by local adaptive thresholding method according to the irregularity index of the segmented shape at foreground. The segmentation results are also compared to the refinement results using Otsu's thresholding method. Cell classification based on the shape and color indices obtained from the segmentation result is applied to determine the cell condition into normal, abnormal and suspected abnormal cases.

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Kuok CP, Wu P-T, Jou IM, Su F, Sun Y-N. Automatic segmentation and classification of tendon nuclei from IHC stained images. In Jiang X, Jiang X, Wang Y, Jiang X, Wang Y, Jiang X, Wang Y, Wang Y, editors, Seventh International Conference on Graphic and Image Processing, ICGIP 2015. SPIE. 2015. 98170J. (Proceedings of SPIE - The International Society for Optical Engineering). https://doi.org/10.1117/12.2228579