Eyelid topology morphological feature extraction method based on deep learning

Abstract

The invention discloses an eyelid topology morphological feature extraction method based on deep learning. The method specifically comprises the following steps: collecting an electronic digital photo of a normal person, processing the electronic digital photo, constructing an ROI image training set, and inputting the ROI image training set into a to-be-trained convolutional neural network to obtain a trained convolutional neural network; positioning an eye region-of-interest (ROI) position by using a facial recognition method for a to-be-detected electronic digital photo to obtain a to-be-detected ROI image; inputting the to-be-detected ROI image into the trained convolutional neural network to output an image with an eyelid contour line and a cornea contour line, determining a circular scale and a pupil center of the to-be-detected electronic digital photo, and extracting eyelid topology morphological characteristics of a single eye. According to the method, the eyelid and cornea structures are segmented by using the convolutional neural network; after the center of the pupil is determined by using Mean Shift clustering, the parameters of the related structure of the eyelid are automatically calculated, so that the accuracy equivalent to that of manual measurement is obtained.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for extracting eyelid topological features based on deep learning. Background technique [0002] Normal eyelid position is the basis for normal eyeball function. Evaluation of eyelid shape and position is of great significance for ophthalmoplasty (such as ptosis, trichiasis), ocular surface diseases (such as exposure keratitis) and Graves ophthalmopathy. . [0003] Currently, the scales are commonly used in clinical practice to manually measure the patient's upper eyelid margin reflex distance (MRD1), lower eyelid margin reflex distance (MRD2) and palpebral fissure size (PF) to assess the eyelid position. However, accurate measurement requires the long-term experience of the measurer and the high cooperation of the measured person, and the reproducibility and stability of manual measurement are poor. At the same time, these linear indicators cannot...

AI Technical Summary

Problems solved by technology

However, accurate measurement requires the long-term experience of the measurer and the high cooperation of the measured person, and the reproducibility and stability of manual measurement are poor

At the same time, these linear indicators cannot fully reflect the complete morphological characteristics of the eyelid contour

By analyzing electronic photos, the problems of poor reproducibility and stability of manual measurement can be overcome. However, traditional automatic analysis methods such as Canny boundary detection algorithm will encounter interference from eyelashes, making it impossible to accurately identify the boundary of the eyelid, and the iris is not perfect circle. It also makes the common three-point fitting circle center method to determine the center of the pupil subject to certain defects

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