Abstract
Liver segmentation is a main step in all automated liver diagnosis systems. This paper aims to propose an approach for liver segmentation. It combines the usage of grey wolf optimization, statistical image of liver and simple region growing to segment the whole liver. Starting with Grey Wolf optimization algorithm, it calculates the centroid values of different clusters in CT images. A statistical image of liver is used to extract the potential area that liver might exist in. Then the segmented liver is enhanced using simple region growing technique (RG). A set of 38 images, taken in pre-contrast phase, was used to segment the liver and test the proposed approach. Similarity index is used to validate the success of the approach. The experimental results showed that the overall accuracy offered by the proposed approach, results in 94.08 % accuracy.
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Mostafa, A. et al. (2017). A Hybrid Grey Wolf Based Segmentation with Statistical Image for CT Liver Images. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_81
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DOI: https://doi.org/10.1007/978-3-319-48308-5_81
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