Publication: Image segmentation using ant and bee algorithms
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering | |
| dc.contributor.author | Khor, Wai Peng | |
| dc.date.accessioned | 2024-10-02T07:33:43Z | |
| dc.date.available | 2024-10-02T07:33:43Z | |
| dc.date.issued | 2012-06-01 | |
| dc.description.abstract | With breast cancer being one of the main cause of cancer death, identifying tumour cells in breast Magnetic Resonance Imaging (MRI) images is very helpful for patients to undergo treatment at an early stage of cancer and thus reduce the rate of fatalities due to last minute treatment. In this respect, it is very important to identify accurately the tumour cells in the MRI images of breasts. This includes differentiating the skin tissues from breast tissues as well as segmenting the tumour cells from the breast tissues. Creatures such as ants and bees have been able to identify best food sources to survive. Their methods have been used as subjects of studies and evolved into animal inspired algorithms. Applying the Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) Optimization algorithms is helpful in identifying tumour cells from among the breast tissues. By treating the tumour cells as the best food source, both the ant and bee algorithms are applied to find the tumour cells with the help of some preprocessing operations. Preprocessing operations such as using median filter to reduce noise, thresholding to reduce area of search for region of interest, morphological operations to close holes and remove dots and also labelling of regions of interest prepares the area of search for the application of the ant and bee algorithms. Both algorithms are able to identify the tumour cells from the breast MRI images, with a difference in speed whereby the bee algorithm takes lesser time. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/20674 | |
| dc.language.iso | en | |
| dc.title | Image segmentation using ant and bee algorithms | |
| dc.type | Resource Types::text::report | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |