CT Liver Segmentation Using Artificial Bee Colony Optimisation

The automated segmentation of the liver area is an essential phase in liver diagnosis from medical images. In this paper, we propose an artificial bee colony (ABC) optimisation algorithm that is used as a clustering technique to segment the liver in CT images. In our algorithm, ABC calculates the ce...

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Bibliographic Details
Published in:Procedia computer science 2015, Vol.60, p.1622-1630
Main Authors: Mostafa, Abdalla, Fouad, Ahmed, Elfattah, Mohamed Abd, Hassanien, Aboul Ella, Hefny, Hesham, Zhu, Shao Ying, Schaefer, Gerald
Format: Article
Language:English
Subjects:
Artificial bee colony optimisation
Image segmentation
Medical imaging
Region growing
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Summary:The automated segmentation of the liver area is an essential phase in liver diagnosis from medical images. In this paper, we propose an artificial bee colony (ABC) optimisation algorithm that is used as a clustering technique to segment the liver in CT images. In our algorithm, ABC calculates the centroids of clusters in the image together with the region corresponding to each cluster. Using mathematical morphological operations, we then remove small and thin regions, which may represents flesh regions around the liver area, sharp edges of organs or small lesions inside the liver. The extracted regions are integrated to give an initial estimate of the liver area. In a final step, this is further enhanced using a region growing approach. In our experiments, we employed a set of 38 images, taken in pre-contrast phase, and the similarity index calculated to judge the performance of our proposed approach. This experimental evaluation confirmed our approach to afford a very good segmentation accuracy of 93.73% on the test dataset.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2015.08.272