Abstract
In this paper, we developed a medical image segmentation system based on hybrid clustering techniques to provide an accurate detection of brain tumor with minimal execution time. Two hybrid techniques have been proposed in our proposed medical image segmentation system. The first hybrid technique is based on k-means and fuzzy c-means (KFCM) while the second is based on k-means and particle swarm optimization (KPSO). We compared the two proposed techniques with k-means; fuzzy c-means, expectation maximization, mean shift, and particle swarm optimization using three different benchmark brain data sets. The results clarify the effectiveness of our second proposed technique.
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Maksoud, E.A.A., Elmogy, M., Al-Awadi, R.M. (2014). MRI Brain Tumor Segmentation System Based on Hybrid Clustering Techniques. In: Hassanien, A.E., Tolba, M.F., Taher Azar, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2014. Communications in Computer and Information Science, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-319-13461-1_38
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DOI: https://doi.org/10.1007/978-3-319-13461-1_38
Publisher Name: Springer, Cham
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