Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  studies the retinal vessel radius estimation and proposes a segmentation method for vessel center lines based on ridge descriptors. The study on radius estimation reveals that the radius estimation by the matched filters based on the second order derivatives of Gaussian kernels is only correct at the vessel center. The relation beten the vessel radius and the scale of the Gaussian kernel in the estimation method based on the normalized largest curvature is also studied. The ridge descriptor proposed in this paper contains the normalized largest curvature and the orientations of gradients in the local neighborhood. For vessels of a certain scale, the distribution of the descriptors is assumed to be a normal distribution and is learned from a training set with known truth. Vessel center line segmentation can be then performed based on the distance beten the ridge descriptor at candidate pixels and the learned model. Evaluation of the vessel center line segmentation based on the descriptors is done on both DRIVE and STARE databases using the receiver operating characteristic (ROC) curves. The areas under the ROC curves on DRIVE and STARE databases are 0.9584 and 0.9421 respectively.
['Changhua Wu', 'Jennifer J. Kang Derwent', 'Peter Stanchev']
I am writing a research paper on a idea Retinal Vessel Radius Estimation and a Vessel Center Line Segmentation Method Based on Ridge Descriptors
[{"from": "human", "value": "I am writing a research paper on a idea Retinal Vessel Radius Estimation and a Vessel Center Line Segmentation Method Based on Ridge Descriptors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  studies the retinal vessel radius estimation and proposes a segmentation method for vessel center lines based on ridge descriptors. The study on radius estimation reveals that the radius estimation by the matched filters based on the second order derivatives of Gaussian kernels is only correct at the vessel center. The relation beten the vessel radius and the scale of the Gaussian kernel in the estimation method based on the normalized largest curvature is also studied. The ridge descriptor proposed in this paper contains the normalized largest curvature and the orientations of gradients in the local neighborhood. For vessels of a certain scale, the distribution of the descriptors is assumed to be a normal distribution and is learned from a training set with known truth. Vessel center line segmentation can be then performed based on the distance beten the ridge descriptor at candidate pixels and the learned model. Evaluation of the vessel center line segmentation based on the descriptors is done on both DRIVE and STARE databases using the receiver operating characteristic (ROC) curves. The areas under the ROC curves on DRIVE and STARE databases are 0.9584 and 0.9421 respectively."}]