Abstract
One of the most important aims in computerized medical image processing is to find out the anatomical structure of the required organ. The hepatic segmentation is very important for surgery planning and diagnosis. The difficulty of segmentation rises from the different volumes, the different lobes and the vascular arteries of liver. This paper proposes a successful approach for liver segmentation. The proposed approach depends on Moth-flame optimization (MFO) algorithm for clustering the abdominal image. The user picks up the required clusters that represent the liver to get the initial segmented image. Then the morphological operations produce the final segmented liver. A set of 70 MRI images, was used to segment the liver and test the proposed approach. Structural Similarity index (SSI) validates the success of the approach. The experimental results showed that the overall accuracy of the proposed approach, results in 95.66% accuracy.
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Said, S., Mostafa, A., Houssein, E.H., Hassanien, A.E., Hefny, H. (2018). Moth-flame Optimization Based Segmentation for MRI Liver Images. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-64861-3_30
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DOI: https://doi.org/10.1007/978-3-319-64861-3_30
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