Optic disc and optic cup segmentation method based on machine learning double-region contour evolution model

Abstract

The invention discloses an optic disc and optic cup segmentation method based on a machine learning double-region contour evolution model. The method comprises the following steps: firstly preprocessing a retina image, and constructing an edge guide model of an optic disc and an optic cup through a machine learning algorithm; and finally, proposing a double-region contour evolution model according to the edge guiding model and an energy function constructed based on the strength, region and edge features of the optic disc and the optic cup, and further obtaining optic disc and optic cup regions. The method has the following advantages: 1, a segmentation algorithm based on machine learning and a segmentation algorithm based on an energy functional model are combined, so that the problems that a machine learning method is sensitive to label data and the energy functional is trapped in a local minimum are solved, and an accurate segmentation result is obtained; and 2, the optic disc and the optic cup can be segmented at the same time in the retina image segmentation, so that the segmentation efficiency is effectively improved.

Description

technical field [0001] The invention relates to the field of retinal image segmentation, in particular to an optic disc and optic cup segmentation method based on a machine learning dual-area contour evolution model. Background technique [0002] Glaucoma is a chronic eye disease with irreversible blindness that is difficult to diagnose early. The cup-to-disk ratio is an important diagnostic indicator for glaucoma screening, so the segmentation of optic disc and optic cup is very important for the diagnosis of glaucoma. Due to factors such as differences in retinal imaging equipment and complex internal structures of the human body, when obtaining retinal images, there are usually characteristics such as uneven gray distribution, blurred edges, and high noise intensity. , active contour methods and machine learning (ML) methods. [0003] Shape-based methods use circular or elliptical transformations to fit edges extracted from retinal images. BirendraBiswal et al. used st...

AI Technical Summary

Problems solved by technology

However, the optic cup cannot be segmented accurately when the optic cup area is blurred and has complex vascular structures

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