Abstract
A graph based segmentation approach is proposed in this study to segment nucleus from cytology images. This approach utilizes a novel method applying weighted circular shape prior adaptively in efficient graph based image segmentation. The proposed method was evaluated by segmenting
nucleus from two public Pap smear image datasets: ISBI 2014 challenge dataset (945 images) and DTU/Herlev intermediate squamous cell dataset (70 images). Segmentation results of the proposed method outperformed the standard one in terms of Dice similarity coefficient, pixel-based precision and recall, Hausdorff distance, and Ht metric. Quantitative measures and visual results indicate that the proposed technique produces better nucleus boundaries.
nucleus from two public Pap smear image datasets: ISBI 2014 challenge dataset (945 images) and DTU/Herlev intermediate squamous cell dataset (70 images). Segmentation results of the proposed method outperformed the standard one in terms of Dice similarity coefficient, pixel-based precision and recall, Hausdorff distance, and Ht metric. Quantitative measures and visual results indicate that the proposed technique produces better nucleus boundaries.
| Original language | English |
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| Pages | 1-8 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2018 |
| Event | 2018 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Canberra, Australia Duration: 10 Dec 2018 → 13 Dec 2018 |
Conference
| Conference | 2018 International Conference on Digital Image Computing: Techniques and Applications (DICTA) |
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| Abbreviated title | DICTA |
| Country/Territory | Australia |
| City | Canberra |
| Period | 10/12/18 → 13/12/18 |