Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Tree-like structures such as retinal images are widely studied in computer-aided diagnosis systems in large-scale screening programs. Despite several segmentation and tracking methods proposed in the literature, there still exist several limitations specifically when two or more curvilinear structures cross or bifurcate, or in the presence of interrupted lines or highly curved blood vessels. ,  propose a novel approach based on multi-orientation scores augmented with a contextual affinity matrix, which both are inspired by the geometry of the primary visual cortex (V1) and their contextual connections. The connectivity is described with a four-dimensional kernel obtained as the fundamental solution of the Fokker-Planck equation modelling the cortical connectivity in the lifted space of positions, orientations and curvatures. It is further used in a self-tuning spectral clustering step to identify the main perceptual units in the stimuli. The proposed method has been validated on several easy and challenging structures in a set of artificial images and actual retinal patches. Supported by quantitative and qualitative results, the method is capable of overcoming the limitations of current state-of-the-art techniques.
['Samaneh Abbasi-Sureshjani', 'Marta Favali', 'Giovanna Citti', 'Alessandro Sarti', 'Bart M. ter Haar Romeny']
I am writing a research paper on a idea Cortically-Inspired Spectral Clustering for Connectivity Analysis in Retinal Images: Curvature Integration
[{"from": "human", "value": "I am writing a research paper on a idea Cortically-Inspired Spectral Clustering for Connectivity Analysis in Retinal Images: Curvature Integration"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Tree-like structures such as retinal images are widely studied in computer-aided diagnosis systems in large-scale screening programs. Despite several segmentation and tracking methods proposed in the literature, there still exist several limitations specifically when two or more curvilinear structures cross or bifurcate, or in the presence of interrupted lines or highly curved blood vessels. ,  propose a novel approach based on multi-orientation scores augmented with a contextual affinity matrix, which both are inspired by the geometry of the primary visual cortex (V1) and their contextual connections. The connectivity is described with a four-dimensional kernel obtained as the fundamental solution of the Fokker-Planck equation modelling the cortical connectivity in the lifted space of positions, orientations and curvatures. It is further used in a self-tuning spectral clustering step to identify the main perceptual units in the stimuli. The proposed method has been validated on several easy and challenging structures in a set of artificial images and actual retinal patches. Supported by quantitative and qualitative results, the method is capable of overcoming the limitations of current state-of-the-art techniques."}]