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 |
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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 | |
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 |
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Abbreviated title | ICORAS 2017 |
Country/Territory | Malaysia |
City | Melaka |
Period | 27/11/17 → 29/11/17 |
Internet address |
Keywords
- image processing
- mass detection
- mass segmentation
- medical imaging