Auto-probing breast cancer mass segmentation for early detection

F. F. Ting; K. S. Sim; S. S. Chong

doi:10.1109/ICORAS.2017.8308077

Auto-probing breast cancer mass segmentation for early detection

F. F. Ting, K. S. Sim, S. S. Chong

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

6 Citations (Scopus)

Abstract

In this paper, an auto-probing breast cancer mass segmentation (ABC-MS) is proposed to assist medical doctors in breast cancer diagnosis. Manual segmentation is implemented as standard diagnosis procedure for medical doctors. This algorithm can detect and segment the breast cancer abnormality without prior knowledge regarding its presence. Automated single seed point region growing is utilised in this algorithm to perform the mass detection and segmentation automatically. Comparison with commercial semi-automated segmentation application is performed. Tabulated experiment results showed the proposed method outperformed the compared method by having accuracy at 90% and area under curve (AUC) at 0.895 ± 0.0338.

Original language	English
Title of host publication	Proceedings of 2017 International Conference on Robotics, Automation and Sciences ICORAS 2017
Editors	Sim Kok Swee
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Number of pages	5
ISBN (Electronic)	9781538619087
ISBN (Print)	9781538619094
DOIs	https://doi.org/10.1109/ICORAS.2017.8308077
Publication status	Published - 2017
Externally published	Yes
Event	International Conference on Robotics, Automation and Sciences 2017 - Melaka, Malaysia Duration: 27 Nov 2017 → 29 Nov 2017 https://ieeexplore.ieee.org/xpl/conhome/8303174/proceeding (Proceedings)

Conference

Conference	International Conference on Robotics, Automation and Sciences 2017
Abbreviated title	ICORAS 2017
Country/Territory	Malaysia
City	Melaka
Period	27/11/17 → 29/11/17
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8303174/proceeding (Proceedings)

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

image processing
mass detection
mass segmentation
medical imaging

Access to Document

10.1109/ICORAS.2017.8308077

Cite this

@inproceedings{224178945e70432abcc3eea0d92b7320,

title = "Auto-probing breast cancer mass segmentation for early detection",

abstract = "In this paper, an auto-probing breast cancer mass segmentation (ABC-MS) is proposed to assist medical doctors in breast cancer diagnosis. Manual segmentation is implemented as standard diagnosis procedure for medical doctors. This algorithm can detect and segment the breast cancer abnormality without prior knowledge regarding its presence. Automated single seed point region growing is utilised in this algorithm to perform the mass detection and segmentation automatically. Comparison with commercial semi-automated segmentation application is performed. Tabulated experiment results showed the proposed method outperformed the compared method by having accuracy at 90\% and area under curve (AUC) at 0.895 ± 0.0338.",

keywords = "image processing, mass detection, mass segmentation, medical imaging",

author = "Ting, \{F. F.\} and Sim, \{K. S.\} and Chong, \{S. S.\}",

note = "Funding Information: We would like to acknowledge Mammographic Image Analysis Society (MIAS) [5] for providing the real patient digital mammogram dataset. This research is funded by Fundamental Research Grant Scheme (FRGS ), it is a research grant from the Ministry of Education (MOE), Malaysia. Publisher Copyright: {\textcopyright} 2017 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; International Conference on Robotics, Automation and Sciences 2017, ICORAS 2017 ; Conference date: 27-11-2017 Through 29-11-2017",

year = "2017",

doi = "10.1109/ICORAS.2017.8308077",

language = "English",

isbn = "9781538619094",

editor = "Swee, \{Sim Kok\}",

booktitle = "Proceedings of 2017 International Conference on Robotics, Automation and Sciences ICORAS 2017",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

url = "https://ieeexplore.ieee.org/xpl/conhome/8303174/proceeding",

}

Ting, FF, Sim, KS & Chong, SS 2017, Auto-probing breast cancer mass segmentation for early detection. in SK Swee (ed.), Proceedings of 2017 International Conference on Robotics, Automation and Sciences ICORAS 2017. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, International Conference on Robotics, Automation and Sciences 2017, Melaka, Malaysia, 27/11/17. https://doi.org/10.1109/ICORAS.2017.8308077

Auto-probing breast cancer mass segmentation for early detection. / Ting, F. F.; Sim, K. S.; Chong, S. S.
Proceedings of 2017 International Conference on Robotics, Automation and Sciences ICORAS 2017. ed. / Sim Kok Swee. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2017.

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

TY - GEN

T1 - Auto-probing breast cancer mass segmentation for early detection

AU - Ting, F. F.

AU - Sim, K. S.

AU - Chong, S. S.

N1 - Funding Information: We would like to acknowledge Mammographic Image Analysis Society (MIAS) [5] for providing the real patient digital mammogram dataset. This research is funded by Fundamental Research Grant Scheme (FRGS ), it is a research grant from the Ministry of Education (MOE), Malaysia. Publisher Copyright: © 2017 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.

PY - 2017

Y1 - 2017

N2 - In this paper, an auto-probing breast cancer mass segmentation (ABC-MS) is proposed to assist medical doctors in breast cancer diagnosis. Manual segmentation is implemented as standard diagnosis procedure for medical doctors. This algorithm can detect and segment the breast cancer abnormality without prior knowledge regarding its presence. Automated single seed point region growing is utilised in this algorithm to perform the mass detection and segmentation automatically. Comparison with commercial semi-automated segmentation application is performed. Tabulated experiment results showed the proposed method outperformed the compared method by having accuracy at 90% and area under curve (AUC) at 0.895 ± 0.0338.

AB - In this paper, an auto-probing breast cancer mass segmentation (ABC-MS) is proposed to assist medical doctors in breast cancer diagnosis. Manual segmentation is implemented as standard diagnosis procedure for medical doctors. This algorithm can detect and segment the breast cancer abnormality without prior knowledge regarding its presence. Automated single seed point region growing is utilised in this algorithm to perform the mass detection and segmentation automatically. Comparison with commercial semi-automated segmentation application is performed. Tabulated experiment results showed the proposed method outperformed the compared method by having accuracy at 90% and area under curve (AUC) at 0.895 ± 0.0338.

KW - image processing

KW - mass detection

KW - mass segmentation

KW - medical imaging

UR - https://www.scopus.com/pages/publications/85050740942

U2 - 10.1109/ICORAS.2017.8308077

DO - 10.1109/ICORAS.2017.8308077

M3 - Conference Paper

AN - SCOPUS:85050740942

SN - 9781538619094

BT - Proceedings of 2017 International Conference on Robotics, Automation and Sciences ICORAS 2017

A2 - Swee, Sim Kok

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - International Conference on Robotics, Automation and Sciences 2017

Y2 - 27 November 2017 through 29 November 2017

ER -

Auto-probing breast cancer mass segmentation for early detection

Abstract

Conference

UN SDGs

Keywords

Access to Document

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