Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a novel region-based active contour model in the level set formulation for medical image segmentation.  define a unified fitting energy framework based on Gaussian probability distributions to obtain the maximum a posteriori probability (MAP) estimation. The energy term consists of a global energy term to characterize the fitting of global Gaussian distribution according to the intensities inside and outside the evolving curve, as ll as a local energy term to characterize the fitting of local Gaussian distribution based on the local intensity information. In the resulting contour evolution that minimizes the associated energy, the global energy term accelerates the evolution of the evolving curve far away from the objects, while the local energy term guides the evolving curve near the objects to stop on the boundaries. In addition, a ighting function beten the local energy term and the global energy term is proposed by using the local and global variances information, which enables the model to select the ights adaptively in segmenting images with intensity inhomogeneity. Extensive experiments on both synthetic and real medical images are provided to evaluate our method, show significant improvements on both efficiency and accuracy, as compared with the popular methods. HighlightsBoth global and local intensity information are incorporated into our method to segment images with intensity inhomogeneity.Size information of local neighborhood partition is used to build the a priori probability model.A ight function beten local energy term and global energy term is proposed.
['Sanping Zhou', 'Jinjun Wang', 'Shun Zhang', 'Yudong Liang', 'Yihong Gong']
I am writing a research paper on a idea Active contour model based on local and global intensity information for medical image segmentation
[{"from": "human", "value": "I am writing a research paper on a idea Active contour model based on local and global intensity information for medical image segmentation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a novel region-based active contour model in the level set formulation for medical image segmentation.  define a unified fitting energy framework based on Gaussian probability distributions to obtain the maximum a posteriori probability (MAP) estimation. The energy term consists of a global energy term to characterize the fitting of global Gaussian distribution according to the intensities inside and outside the evolving curve, as ll as a local energy term to characterize the fitting of local Gaussian distribution based on the local intensity information. In the resulting contour evolution that minimizes the associated energy, the global energy term accelerates the evolution of the evolving curve far away from the objects, while the local energy term guides the evolving curve near the objects to stop on the boundaries. In addition, a ighting function beten the local energy term and the global energy term is proposed by using the local and global variances information, which enables the model to select the ights adaptively in segmenting images with intensity inhomogeneity. Extensive experiments on both synthetic and real medical images are provided to evaluate our method, show significant improvements on both efficiency and accuracy, as compared with the popular methods. HighlightsBoth global and local intensity information are incorporated into our method to segment images with intensity inhomogeneity.Size information of local neighborhood partition is used to build the a priori probability model.A ight function beten local energy term and global energy term is proposed."}]