Arterial radius estimation from microscopic data using a new algorithm for circle parameter estimation

We developed and tested a new method for automatic determination of vessel wall diameters from image stacks obtained using Two-photon Laser Scanning Microscopy (TPLSM) on viable arteries in perfusion flow chambers. To this extent, a new method has been proposed for estimating the parameters of a circle describing the inner diameter of the blood vessels. The new method is based on the Hough Transform and the observation that three points that are not co-linear uniquely define a circle. By only storing the estimated center location, the computational and memory costs of the Hough Transform can be greatly reduced. We tested the algorithm on twenty images and compared the result with a ground-truth established by human volunteers, and a standard Least Squares technique. With errors of 3-5%, the algorithm allows accurate estimation of the vessel diameters from image stacks containing only small parts of the vessel cross section. Combined with TPLSM imaging of anatomical vessel wall properties, potentially, the algorithm enables the correlation of structural and functional properties of large intact arteries simultaneously, without requirements for additional experiments

Brain-inspired algorithms for retinal image analysis

Retinal image analysis is a challenging problem due to the precise quantification required and the huge numbers of images produced in screening programs. This paper describes a series of innovative brain-inspired algorithms for automated retinal image analysis, recently developed for the RetinaCheck project, a large-scale screening program for diabetic retinopathy and other retinal diseases in Northeast China. The paper discusses the theory of orientation scores, inspired by cortical multi-orientation pinwheel structures, and presents applications for automated quality assessment, optic nerve head detection, crossing-preserving enhancement and segmentation of retinal vasculature, arterio-venous ratio, fractal dimension, and vessel tortuosity and bifurcations. Many of these algorithms outperform state-of-the-art techniques. The methods are currently validated in collaborating hospitals, with a rich accompanying base of metadata, to phenotype and validate the quantitative algorithms for optimal classification power

Brain-inspired algorithms for retinal image analysis

\u3cp\u3eRetinal image analysis is a challenging problem due to the precise quantification required and the huge numbers of images produced in screening programs. This paper describes a series of innovative brain-inspired algorithms for automated retinal image analysis, recently developed for the RetinaCheck project, a large-scale screening program for diabetic retinopathy and other retinal diseases in Northeast China. The paper discusses the theory of orientation scores, inspired by cortical multi-orientation pinwheel structures, and presents applications for automated quality assessment, optic nerve head detection, crossing-preserving enhancement and segmentation of retinal vasculature, arterio-venous ratio, fractal dimension, and vessel tortuosity and bifurcations. Many of these algorithms outperform state-of-the-art techniques. The methods are currently validated in collaborating hospitals, with a rich accompanying base of metadata, to phenotype and validate the quantitative algorithms for optimal classification power.\u3c/p\u3