Nuclei segmentation of microscopic breast cancer image using Gram-Schmidt and cluster validation algorithm

Chastine Fatichah; Nanik Suciati; Bilqis Amaliah; Nuru Aini

doi:10.1109/ICCSCE.2015.7482190

Nuclei segmentation of microscopic breast cancer image using Gram-Schmidt and cluster validation algorithm

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

A combination of Gram-Schmidt method and cluster validation algorithm based Bayesian is proposed for nuclei segmentation on microscopic breast cancer image. Gram-Schmidt is applied to identify the cell nuclei on a microscopic breast cancer image and the cluster validation algorithm based Bayesian method is used for separating the touching nuclei. The microscopic image of the breast cancer cells are used as dataset. The segmented cell nuclei results on microscopic breast cancer images using Gram-Schmidt method shows that the most of MSE values are below 0.1 and the average MSE of segmented cell nuclei results is 0.08. The average accuracy of separated cell nuclei counting using cluster validation algorithm is 73% compares with the manual counting.

Original language	English
Title of host publication	Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	236-241
Number of pages	6
ISBN (Electronic)	9781479982523
DOIs	https://doi.org/10.1109/ICCSCE.2015.7482190
Publication status	Published - 31 May 2016
Event	5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015 - Batu Ferringhi, Penang, Malaysia Duration: 27 Nov 2015 → 29 Nov 2015

Publication series

Name	Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015

Conference

Conference	5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015
Country/Territory	Malaysia
City	Batu Ferringhi, Penang
Period	27/11/15 → 29/11/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bayesian
Breast Cancer
Gram-Schmidt
Microscopic Image
Nuclei Segmentation

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10.1109/ICCSCE.2015.7482190

Cite this

Fatichah, C., Suciati, N., Amaliah, B., & Aini, N. (2016). Nuclei segmentation of microscopic breast cancer image using Gram-Schmidt and cluster validation algorithm. In Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015 (pp. 236-241). Article 7482190 (Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCSCE.2015.7482190

Fatichah, Chastine ; Suciati, Nanik ; Amaliah, Bilqis et al. / Nuclei segmentation of microscopic breast cancer image using Gram-Schmidt and cluster validation algorithm. Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 236-241 (Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015).

@inproceedings{967a90528800492a9cca8ac358c2751e,

title = "Nuclei segmentation of microscopic breast cancer image using Gram-Schmidt and cluster validation algorithm",

abstract = "A combination of Gram-Schmidt method and cluster validation algorithm based Bayesian is proposed for nuclei segmentation on microscopic breast cancer image. Gram-Schmidt is applied to identify the cell nuclei on a microscopic breast cancer image and the cluster validation algorithm based Bayesian method is used for separating the touching nuclei. The microscopic image of the breast cancer cells are used as dataset. The segmented cell nuclei results on microscopic breast cancer images using Gram-Schmidt method shows that the most of MSE values are below 0.1 and the average MSE of segmented cell nuclei results is 0.08. The average accuracy of separated cell nuclei counting using cluster validation algorithm is 73\% compares with the manual counting.",

keywords = "Bayesian, Breast Cancer, Gram-Schmidt, Microscopic Image, Nuclei Segmentation",

author = "Chastine Fatichah and Nanik Suciati and Bilqis Amaliah and Nuru Aini",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015 ; Conference date: 27-11-2015 Through 29-11-2015",

year = "2016",

month = may,

day = "31",

doi = "10.1109/ICCSCE.2015.7482190",

language = "English",

series = "Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "236--241",

booktitle = "Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015",

address = "United States",

}

Fatichah, C , Suciati, N , Amaliah, B & Aini, N 2016, Nuclei segmentation of microscopic breast cancer image using Gram-Schmidt and cluster validation algorithm. in Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015., 7482190, Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015, Institute of Electrical and Electronics Engineers Inc., pp. 236-241, 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015, Batu Ferringhi, Penang, Malaysia, 27/11/15. https://doi.org/10.1109/ICCSCE.2015.7482190

Nuclei segmentation of microscopic breast cancer image using Gram-Schmidt and cluster validation algorithm. / Fatichah, Chastine ; Suciati, Nanik ; Amaliah, Bilqis et al.
Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 236-241 7482190 (Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Nuclei segmentation of microscopic breast cancer image using Gram-Schmidt and cluster validation algorithm

AU - Fatichah, Chastine

AU - Suciati, Nanik

AU - Amaliah, Bilqis

AU - Aini, Nuru

PY - 2016/5/31

Y1 - 2016/5/31

N2 - A combination of Gram-Schmidt method and cluster validation algorithm based Bayesian is proposed for nuclei segmentation on microscopic breast cancer image. Gram-Schmidt is applied to identify the cell nuclei on a microscopic breast cancer image and the cluster validation algorithm based Bayesian method is used for separating the touching nuclei. The microscopic image of the breast cancer cells are used as dataset. The segmented cell nuclei results on microscopic breast cancer images using Gram-Schmidt method shows that the most of MSE values are below 0.1 and the average MSE of segmented cell nuclei results is 0.08. The average accuracy of separated cell nuclei counting using cluster validation algorithm is 73% compares with the manual counting.

AB - A combination of Gram-Schmidt method and cluster validation algorithm based Bayesian is proposed for nuclei segmentation on microscopic breast cancer image. Gram-Schmidt is applied to identify the cell nuclei on a microscopic breast cancer image and the cluster validation algorithm based Bayesian method is used for separating the touching nuclei. The microscopic image of the breast cancer cells are used as dataset. The segmented cell nuclei results on microscopic breast cancer images using Gram-Schmidt method shows that the most of MSE values are below 0.1 and the average MSE of segmented cell nuclei results is 0.08. The average accuracy of separated cell nuclei counting using cluster validation algorithm is 73% compares with the manual counting.

KW - Bayesian

KW - Breast Cancer

KW - Gram-Schmidt

KW - Microscopic Image

KW - Nuclei Segmentation

UR - https://www.scopus.com/pages/publications/84978870766

U2 - 10.1109/ICCSCE.2015.7482190

DO - 10.1109/ICCSCE.2015.7482190

M3 - Conference contribution

AN - SCOPUS:84978870766

T3 - Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015

SP - 236

EP - 241

BT - Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015

Y2 - 27 November 2015 through 29 November 2015

ER -

Fatichah C , Suciati N , Amaliah B, Aini N. Nuclei segmentation of microscopic breast cancer image using Gram-Schmidt and cluster validation algorithm. In Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 236-241. 7482190. (Proceedings - 5th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2015). doi: 10.1109/ICCSCE.2015.7482190

Nuclei segmentation of microscopic breast cancer image using Gram-Schmidt and cluster validation algorithm

Abstract

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UN SDGs

Keywords

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