Selective Level Set Segmentation Using Fuzzy Region Competition

Deformable models and level set methods have been extensively investigated for computerized image segmentation. However, medical image segmentation is yet one of open challenges owing to diversified physiology, pathology and imaging modalities. Existing level set methods suffer from some inherent drawbacks in face of noise, ambiguity and inhomogeneity. It is also refractory to control level set segmentation that is dependent on image content and evolutional strategies. In this study, a new level set formulation is proposed by using fuzzy region competition for selective image segmentation. It is able to detect and track the arbitrary combination of selected objects or image components. To the best of our knowledge, this new formulation should be one of the first proposals in a framework of region competition for selective segmentation.
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B.N. Li, C.K. Chui, S.H. Ong, T. Numano, T. Washio, K. Homma, S. Chang, S. Venkatesh, E. Kobayashi (2012) Modeling shear modulus distribution in magnetic resonance elastography with piecewise constant level sets. Magnetic Resonance Imaging 30(3) 390-401.

B.N. Li, J. Qin, R. Wang, M. Wang, X. Li (2016) Selective Level Set Segmentation Using Fuzzy Region Competition. IEEE Access 4: 4777-4788.
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