Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a level-set based segmentation method for medical images with intensity inhomogeneity. Maximum a Posteriori estimation is adopted to combine image segmentation and bias field correction into a unified framework. Within this framework, both contour prior and bias field prior can be fully used. In order to restrict bias field,  introduce an adaptive regularization. Based on this new adaptive regularization, the bias field is estimated more smooth and the input medical image with intensity inhomogeneity is recovered more clearly. Especially, the estimated bias field of our method introduces less structure information obtained from input image. Experimental results on both synthetic and real images show the advantages of our method in both segmentation and bias field correction accuracies as compared with the state-of-the-art approaches.
['Xiaomeng Xin', 'Lingfeng Wang', 'Chunhong Pan', 'Shigang Liu']
I am writing a research paper on a idea Adaptive Regularization Level Set Evolution for Medical Image Segmentation and Bias Field Correction
[{"from": "human", "value": "I am writing a research paper on a idea Adaptive Regularization Level Set Evolution for Medical Image Segmentation and Bias Field Correction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a level-set based segmentation method for medical images with intensity inhomogeneity. Maximum a Posteriori estimation is adopted to combine image segmentation and bias field correction into a unified framework. Within this framework, both contour prior and bias field prior can be fully used. In order to restrict bias field,  introduce an adaptive regularization. Based on this new adaptive regularization, the bias field is estimated more smooth and the input medical image with intensity inhomogeneity is recovered more clearly. Especially, the estimated bias field of our method introduces less structure information obtained from input image. Experimental results on both synthetic and real images show the advantages of our method in both segmentation and bias field correction accuracies as compared with the state-of-the-art approaches."}]