Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Deformable models and level set methods have been extensively investigated for computerized image segmentation. Hover, 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. , 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. Experiments on both synthetic and real images validate its advantages in selective level set segmentation.
['Bing Nan Li', 'Jing Qin', 'Rong Wang', 'Meng Wang', 'Xuelong Li']
I am writing a research paper on a idea Selective Level Set Segmentation Using Fuzzy Region Competition
[{"from": "human", "value": "I am writing a research paper on a idea Selective Level Set Segmentation Using Fuzzy Region Competition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Deformable models and level set methods have been extensively investigated for computerized image segmentation. Hover, 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. , 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. Experiments on both synthetic and real images validate its advantages in selective level set segmentation."}]