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 proceedingConference PaperResearchpeer-review

4 Citations (Scopus)


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 languageEnglish
Title of host publicationProceedings of 2017 International Conference on Robotics, Automation and Sciences ICORAS 2017
EditorsSim Kok Swee
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781538619087
ISBN (Print)9781538619094
Publication statusPublished - 2017
Externally publishedYes
EventInternational Conference on Robotics, Automation and Sciences 2017 - Melaka, Malaysia
Duration: 27 Nov 201729 Nov 2017 (Proceedings)


ConferenceInternational Conference on Robotics, Automation and Sciences 2017
Abbreviated titleICORAS 2017
Internet address


  • image processing
  • mass detection
  • mass segmentation
  • medical imaging

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