36 results about "Optic disc segmentation" patented technology

The invention discloses a fundus image optic disc segmentation method and a system for assisting glaucoma screening, and relates to the technical field of image information processing. The fundus image optic disc segmentation method comprises the steps that a plurality of fundus images are collected and preprocessed, and a training image sample set and a verification image sample set are obtained;training of a constructed W-Net-Mcon full convolutional neural network by using the training image sample set to obtain an optimal W-Net-Mcon full convolutional neural networkis carried out; preprocessing the fundus image to be segmented, and inputting the preprocessed fundus image to be segmented into the optimal W-Net-Mcon full convolutional neural network to obtain a prediction target result image; Processing prediction target result graph by utilizing polar coordinate inverse transformation and ellipse fitting to obtain final segmentation result so as to obtain cup-to-disk ratio and finally obtain glaucoma preliminary screening result. According to the method, image semantic information can be effectively extracted in a multi-size mode, fusion of features of different levels, fusion of global features and detail features and encouragement of feature multiplexing are carried out, gradient back propagation is improved, and the image segmentation precision is improved.

The invention discloses a retinal fundus image cup/disc ratio automatic evaluation method. The method comprises the following steps: A, extracting an optic disk area image from a retinal fundus image;B, establishing and training a optic disc optic cup segmentation network based on the deep convolutional neural network; C, acquiring a to-be-detected optic disc area image from the to-be-detected retina fundus image according to the step A, and inputting the to-be-detected optic disc area image into an optic disc optic cup segmentation network to output a to-be-detected optic disc segmentation mask image and a to-be-detected optic cup segmentation mask image; And step D, calculating the cup-disc ratio of the retinal fundus image according to the to-be-detected optic disc segmentation mask image and the to-be-detected optic cup segmentation mask image. The method is high in operation speed, good in effect, free of manual participation, low in cost and high in universality, and can be widely applied to auxiliary screening of glaucoma.

The invention provides a fundus retinal vessel recognition and quantification method, device and equipment and a storage medium, and the method comprises the steps: inputting an original fundus imageinto a pre-trained U-shaped convolutional neural network model for processing, and obtaining a target feature map; performing optic disk segmentation based on the target feature map; segmenting the original fundus image to obtain an arteriovenous blood vessel recognition result; carrying out region-of-interest positioning based on the optic disk segmentation result; extracting a blood vessel center line according to the arteriovenous blood vessel recognition result, detecting key points in the blood vessel center line, removing the key points to obtain a plurality of mutually independent bloodvessel sections, and correcting arteriovenous category information on each blood vessel section; and based on the extracted blood vessel center line, obtaining the blood vessel diameter of each bloodvessel section after category information correction, and then quantifying arteriovenous blood vessels in the region of interest. According to the embodiment of the invention, the fundus retina artery and vein vessel identification precision is improved, and the quantization precision is further improved.

The invention discloses a glaucoma optic disc segmentation map obtaining method. The method comprises the steps of acquiring and classifying a known color fundus image and a fundus image data set; constructing an optic disc preliminary segmentation deep learning network, and training, testing and correcting the network by utilizing the classification data to obtain an optic disc segmentation model; carrying out segmentation and screenshot on the test data set and the training data set; processing the screenshot; constructing an interpretable glaucoma preliminary auxiliary network; utilizing the screenshot to train and correct the interpreted glaucoma preliminary auxiliary network to obtain an interpretable glaucoma auxiliary network; segmenting the to-be-analyzed color fundus image data byusing an optic disc segmentation model to obtain an optic disc screenshot; processing the view disk screenshot by using an interpretable glaucoma auxiliary network to obtain a gray scale thermodynamic diagram; And processing the gray scale thermodynamic diagram to obtain a final view disk segmentation map. According to the method, the optic disc segmentation map for auxiliary diagnosis can be rapidly provided for doctors, and the method is high in reliability and good in effect.

The embodiment of the invention relates to an MCASPP neural network fundus image optic cup and optic disc segmentation model based on an Attention mechanism. The model comprises a model body, the system comprises a feature extraction module, an attention mapping module, a multi-scale cavity convolution module and an output module. Extracting a first image feature in the input image through a feature extraction module; the attention mapping module is used for extracting a second image feature of the input image; and obtaining a first feature according to the advanced feature, the low-level feature and the second image feature in the first image feature, through the multi-scale cavity convolution module, the advanced features are subjected to multiple convolution to obtain the second features, and the output module obtains the prediction probability graph according to the first features and the second features, so that the feature extraction precision of the image segmentation network can be improved, and the technical problem of relatively low optic disc precision of optic cups segmented by a full convolution network in related technologies is avoided.

The invention discloses a fundus image optic disc segmentation method based on rapid mean shift, belongs to the technical field of image processing, and solves the problems of low accuracy, low robustness and low segmentation efficiency of an optic disc segmentation method in the prior art. The method comprises the steps of inputting and pre-processing an original fundus image wherein the originalfundus image is a to-be-analyzed fundus image; performing color correction on the fundus image subjected to color correction by using a multi-light-source color constancy algorithm; and carrying outoptic disc positioning by combining the blood vessel information in the preprocessed image, and segmenting out an optic disc region by utilizing rapid mean shift based on the fundus image after colorcorrection to obtain an optic disc image. The method is used for optic disc segmentation of the fundus image.

The invention relates to an optic disc segmentation method with the combination of fundus image edge information and brightness information. The method comprises a step of carrying out preprocessing on a fundus image, a step of using an Isotropic Sobel operator to extract the vertical edge and horizontal edge images of the fundus image with enhanced contrast, and positioning the horizontal coordinate of an optic disc according to an edge difference curve, a step of extracting a vascular intensiveness characteristic and an optic disc brightness characteristic, a step of defining the product of vascular intensiveness characteristic and the optic disc brightness characteristic as a comprehensive characteristic value, obtaining a comprehensive characteristic value curve about the vertical coordinate, wherein the coordinate corresponding to a maximum value in the curve is a optic disc vertical coordinate, and a step of cutting the optic disc by using a CV horizontal set model according to the positioned optic disc center coordinate. The method has high segmentation efficiency.

The invention discloses an automatic segmentation method for an optic disc optic cup of a color fundus image. The method comprises the following steps: acquiring a known color fundus image and a corresponding fundus image data set; Constructing and obtaining an optic disc segmentation model; segmentating and intercepting The data set, and obtaining a screenshot; Constructing and obtaining an optic disc optic cup segmentation model under the Euclidean coordinates; Performing polar coordinate transformation on the screenshot to obtain a polar coordinate screenshot; Constructing and obtaining anoptic disc optic cup segmentation model under polar coordinates; Segmenting the to-be-analyzed data by using two types of models to obtain an Euclidean coordinate optic disc optic cup segmentation result and a polar coordinate optic disc optic cup segmentation result; And fusing the two types of segmentation results to obtain a final optic disc and optic cup segmentation result of the fundus image. According to the method, automatic optic disk and optic cup segmentation can be more accurately carried out on the color fundus image, and the method is simple, reliable and good in applicability.

The invention relates to the field of medical science and technology, and provides a hypertension risk prediction method, device and equipment and a medium. The method comprises the following steps: obtaining the individual information and a fundus image of a to-be-detected target; respectively inputting the fundus image into a preset arteriovenous segmentation model, an optic disk segmentation model and a lesion recognition model, and respectively extracting arteriovenous blood vessels, optic disk regions and fundus lesion features in the fundus image; quantifying the diameters of arteriovenous blood vessels in the optic disc area to obtain blood vessel diameter quantized values corresponding to the arteriovenous blood vessels respectively; using a machine learning regression method to construct a risk prediction model for predicting the hypertension of the target to be detected based on the blood vessel diameter quantized value, the fundus lesion features and the individual information; and inputting the fundus image corresponding to the to-be-detected target into the risk prediction model for detection to obtain a hypertension prediction result of the to-be-detected target. The risk prediction model is trained by adopting a plurality of variable factors, so that the accuracy of hypertension risk prediction is greatly improved.

The invention discloses a U-Net and region growing PCNN combined retinal optic disk segmentation method. The method comprises the steps of performing graying processing on a retinal optic disk data set picture; performing CLAHE processing on the data set picture after graying processing to enhance the contrast between the optic disc and the background in the retinal optic disc image; partitioningthe retinal optic disc image; constructing and training a U-Net neural network model and roughly extracting pictures; constructing a regional growth PCNN neural network model; and carrying out retinaloptic disc segmentation by using the region growing PCNN. On one hand, the invention provides an improved U-Net retina optic disc image rough extraction method, and through the rough extraction, thebackground is significantly inhibited, the optic disc area is highlighted, and the picture contrast is increased, so that the picture quality of a data set is improved; on the other hand, the invention provides an optic disk image segmentation method based on the improved region growing PCNN, the PCNN segmentation performance is improved by changing a seed selection mode, a PCNN initial ignition threshold selection mode and a region growing end condition, and the segmentation of the complete optic disk is realized.

The invention discloses an eye fundus image optic cup and optic disk segmentation method under a unified framework, and the method comprises the steps: obtaining an eye fundus image before segmentation, and carrying out the image preprocessing operation, such as cutting and rotating; generating a corresponding mask image according to an optic cup and optic disk area marked on the fundus color photo by an ophthalmologist; constructing a deep network for segmenting an optic cup and an optic disk; iteratively training the deep segmentation network by using the mask image and the fundus image, and optimizing network parameters; and segmenting the optic cup and the optic disc, and obtaining the segmentation results of the optic cup and the optic disc by using the trained segmentation network model. The invention provides a deep neural network for optic cup and optic disc segmentation. The deep neural network comprises a multi-scale feature extractor, a multi-scale feature transition and an attention pyramid structure. According to the method, the optic cup and the optic disc can be effectively segmented, the segmentation precision is high, and meanwhile, a new thought is provided for segmentation of fundus images and segmentation of other medical images.

The invention provides an image processing method and device, a computer readable storage medium and electronic equipment, and relates to the technical field of image processing. The image processing method comprises the following steps: determining optic disc position information corresponding to a fundus image to be processed based on a preset target detection algorithm; determining an optic disc segmentation image and optic disc boundary information corresponding to the fundus image to be processed based on the optic disc position information and a preset edge detection algorithm; and determining an optic cup segmentation image and optic cup boundary information corresponding to the fundus image to be processed based on the optic disc position information. According to the image processing method provided by the embodiment of the invention, the eye fundus image optic disk and optic cup identification and extraction can be realized, and the segmentation precision reaches a sub-pixel level or above.

The invention discloses an optic cup and optic disk segmentation method based on depth level set learning, comprising the following steps: S1, inputting an eye fundus image into a U-shaped neural network, the U-shaped neural network outputting a level set function value of each pixel in the image, a corresponding zero level set being a contour of a target object; s2, using a level set loss function to calculate an error between the output of the U-shaped neural network and a real label and update parameters of the U-shaped neural network, and introducing a contour length constraint and a region consistency constraint into the loss function, so that the neural network can obtain a level set meeting priori knowledge; and S3, training the two U-shaped neural networks to segment the optic disc and the optic cup respectively. The high-precision optic cup and optic disc segmentation method is realized, and the method can be effectively applied to automatic diagnosis of retinal diseases.

The invention provides a fundus optic disc segmentation method based on a U-Net neural network. The fundus optic disc segmentation method based on the U-Net neural network comprises the following steps: collecting a plurality of original fundus images; preprocessing the plurality of original eye fundus images; marking optic disc areas of the plurality of preprocessed original eye fundus images; constructing a U-Net neural network, and performing training verification on the original fundus image and the fundus image marked with the optic disk area by using the U-Net neural network to obtain a U-Net neural network model; and using the U-Net neural network model to identify and segment the optic disc region of the fundus image. The method can assist a doctor in obtaining an accurate and effective cup-to-disk ratio, assists the doctor in making a treatment method, accelerates the speed of distinguishing the degree of glaucoma of a patient, and reduces misdiagnosis caused by subjective factors of the doctor.