Abstract
This paper presents the mean shift segmentation algorithm for segmenting the extracted sputum cells into nuclei and cytoplasm regions. The segmentation results will be used as a base for a Computer Aided Diagnosis (CAD) system for early detection and diagnosis of lung cancer. The mean shift is a mode seeking process on a surface design with a kernel. Also it will be used as a strategy to perform multistart global optimization. The histogram analysis is used to find the best distribution of the nuclei and cytoplasm sputum cell pixels and to find the best color space that can be used to perform the mean shift segmentation. The Mena shift method offers better performance compared to other segmentation algorithm including Hopefield Neural Network (HNN). The new method is validated on a set of manually defined ground truths sputum images.
| Original language | British English |
|---|---|
| Title of host publication | IEEE AFRICON 2013 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Print) | 9781467359405 |
| DOIs | |
| State | Published - 2013 |
| Event | IEEE AFRICON 2013 - Pointe-Aux-Piments, Mauritius Duration: 9 Sep 2013 → 12 Sep 2013 |
Publication series
| Name | IEEE AFRICON Conference |
|---|---|
| ISSN (Print) | 2153-0025 |
| ISSN (Electronic) | 2153-0033 |
Conference
| Conference | IEEE AFRICON 2013 |
|---|---|
| Country/Territory | Mauritius |
| City | Pointe-Aux-Piments |
| Period | 9/09/13 → 12/09/13 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- histogram analysis
- lung cancer
- Mean shift segmentation
- sputum image
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