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33 results about "Retina vessels" patented technology
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Apparatus and method for non-invasive diabetic retinopathy detection and monitoring
ActiveUS20120249959A1Robust identificationEnhance vesselImage analysisAcquiring/recognising eyesDiabetes retinopathyOphthalmology
A fundus camera using infrared light sources, which included an imaging optical path and an optical path for focusing and positioning and two optical paths share a common set of retina objective lens, and a computer-assisted method for retinal vessel segmentation in general and diabetic retinopathy (DR) image segmentation in particular. The method is primarily based on Multiscale Production of the Matched Filter (MPMF) scheme, which together the fundus camera, is useful for non-invasive diabetic retinopathy detection and monitoring.
Owner:THE HONG KONG POLYTECHNIC UNIV
Retinal vessel segmentation method in fundus image and computer readable storage medium
InactiveCN112233135AEasy to divide and locatePrecise Segmentation and PositioningImage enhancementImage analysisData setNetwork architecture
The invention provides a retinal vessel segmentation method in a fundus image and a computer readable storage medium. The method comprises the steps of preprocessing the fundus image of a public dataset; cutting and expanding the preprocessed fundus image and dividing the fundus image into a training set and a cross validation set; building a residual non-local attention network, wherein the residual non-local attention network comprises the steps of adopting a coding network decoding network architecture with jump connection as a basic model, and embedding a residual non-local attention module to capture a non-local context dependency relationship between different areas of input features; adding a pyramid pooling module to the encoding network to capture multi-scale features; training aresidual non-local attention network by adopting the training set; determining model parameters of the residual non-local attention network through the cross validation set; and adopting the trainedresidual non-local attention network to segment the fundus image to be processed, so that a retinal vessel segmentation image can be obtained. Segmentation performance is better.
Owner:SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
Retinal vessel image segmentation method based on deep learning
InactiveCN111862056AMaximize retentionAchieving Feature ReuseImage enhancementImage analysisData setFeature extraction
The invention discloses a retinal vessel image segmentation method based on deep learning, and the method comprises the steps: carrying out the enhancement of a fundus image, amplifying the data of atraining set, constructing a dense connection convolution block, replacing a conventional convolution block with the dense connection convolution block, achieving the feature reuse, and improving thefeature extraction capability; constructing an attention mechanism module, and performing adaptive adjustment on the feature map to highlight important features so as to suppress invalid features; building a model, building a DA-Unet network, using the processed data set to perform training and parameter adjustment, and obtaining and storing an optimal segmentation model; and carrying out actual segmentation, segmenting the eye fundus image needing retinal vessel segmentation into 48 * 48 sub-block images by using a sliding window, inputting the 48 * 48 sub-block images into a DA-Uet network for segmentation, outputting segmented sub-block image results, and splicing the segmented small block images into a complete retinal vessel segmentation image. The blood vessel segmentation method canautomatically segment blood vessels and has a good segmentation effect on tiny blood vessels.
Owner:DONGGUAN UNIV OF TECH
Blood vessel segmentation network and method based on generative adversarial network
ActiveCN111127447AAvoid the problem of difficult gradient disappearance trainingImprove stabilityImage enhancementImage analysisPattern recognitionColor image
The invention discloses a blood vessel segmentation network and method based on a generative adversarial network. The segmentation network comprises a generation model and a discrimination model. Residual connection is added to the generation model on the basis of adopting a U-shaped encoding-decoding structure; the discrimination model adopts a full convolution form of a VGG network, and replacesthe convolution layer of the middle part with a dense connection module; according to the segmentation method, a generation sample of a color fundus image is generated through a generation model, thegeneration sample and a corresponding real sample are input into a discrimination model, alternate training optimization is performed on the generation model and the discrimination model, and finally, a to-be-segmented retinal vessel color image is input into the trained and optimized model, so that a vessel segmentation result can be output. According to the method, more tiny capillaries in theretinal vessel image can be detected, the vessel edge can be positioned more accurately, the vessel segmentation precision is greatly improved, and the vessel image segmentation sensitivity, effectiveness and stability are greatly improved.
Owner:HENAN UNIVERSITY OF TECHNOLOGY
Device and method for measuring blood oxygen saturation of fundus retina
InactiveCN102028477AAchieving Longitudinal ChromatographyNon-destructive measurement of blood oxygen saturationDiagnostic recording/measuringSensorsRetina vesselsBlood vessel
The invention relates to a method and device for measuring blood oxygen saturation of a fundus retina. The method is characterized by comprising the following steps of: taking a platform based on an ASOLO (adaptive optics confocal scanning laser ophthalmoscope), selecting a light with at least two different wavelengths as the light source of the ASOLO, enabling the retina to be sequentially imaged after correcting a fundus aberration by adopting the adaptive optics; generating defocus via a distorting lens for realizing the longitudinal chromatography of the retina so as to make the same position of a retina vessel imaged; registering the obtained high-resolution retinal image with multiple wavelengths, extracting a plurality of darkest spots and the spot arranged at a fixed distance from the darkest spots and in a tissue along with the vessel; and processing data to obtain the blood oxygen saturation of the vessel. In the invention, the high-resolution retinal image can be obtained by adopting the adaptive optics to correct the fundus aberration, and the blood oxygen saturation of the arteries, veins and capillaries of the fundus retina can be measured by processing a multi-wavelength image.
Owner:INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
Retinal vessel segmentation method fusing W-net and conditional generative adversarial network
ActiveCN110930418AIncrease contrastEasy extractionImage enhancementImage analysisPattern recognitionData set
The invention relates to application of a deep learning algorithm in the field of medical image analysis, in particular to a retinal vessel segmentation algorithm fusing W-net and a conditional generative adversarial network. According to the method, the problems of low segmentation sensitivity and insufficient micro blood vessel segmentation are well solved, great progress is made in the aspectsof the parameter utilization rate, the information circulation and the feature analysis ability of the network, complete segmentation of the main blood vessel and fine segmentation of the micro bloodvessel are facilitated, the blood vessel intersection is not prone to breakage, and a focus and an optic disc are not prone to being segmented into the blood vessel by mistake. According to the invention, a plurality of network models are fused under the condition of low complexity; the whole segmentation performance on a DRIVE data set is excellent, the sensitivity and accuracy of the method are87.18% and 96.95% respectively, the ROC curve value reaches 98.42%, the method can be used for computer-aided diagnosis in the medical field, and rapid and automatic retinal vessel segmentation is achieved.
Owner:JIANGXI UNIV OF SCI & TECH
Retinal vessel image segmentation method based on improved UNet + +
ActiveCN112508864AIncrease weightEnhanced Semantic InformationImage enhancementImage analysisContrast levelData set
The invention relates to a retinal vessel image segmentation method based on improved UNet + +, and belongs to the technical field of medical image processing. According to the method, a deep supervision network UNet + + is selected as an image segmentation network model, so that the use efficiency of features is improved; a MultiRes feature extraction module is introduced to improve the feature learning effect of small blood vessels in a low-contrast environment, the generalization ability of a network and the expression ability of a network structure are further improved by coordinating features learned by an image in different scales, and a SeNet channel attention module is added to perform extrusion and excitation operation after feature extraction to improve the accuracy of feature extraction of the small blood vessels in the low-contrast environment. Therefore, the receptive field in the feature extraction stage is enhanced, and the weight of a target related feature channel is improved. The improved UNet + + network model is verified based on a DRIVE retina image data set, and compared with an existing model, the evaluation indexes such as the overlapping ratio, the cross-parallel ratio, the accuracy and the sensitivity are improved to a certain extent.
Owner:KUNMING UNIV OF SCI & TECH
Retinal vessel segmentation method combining U-Net and adaptive PCNN
PendingCN111815562AQuality improvementKeep informationImage enhancementImage analysisPattern recognitionData set
The invention discloses a retinal vessel segmentation method combining U-Net and adaptive PCNN. The method comprises the steps of performing data augmentation on a fundus image database selected in anexperiment; graying processing is carried out on the data set pictures; carrying out CLAHE processing on the data set pictures to increase the contrast between retinal vessels and the background; partitioning the image; constructing and training a U-Net neural network model, and enhancing a picture; building a self-adaptive PCNN neural network model; and carrying out blood vessel segmentation byusing the adaptive PCNN. On one hand, the invention provides the fundus blood vessel image enhancement method based on the improved U-Net quadratic iteration, the background can be significantly inhibited, the blood vessel region is highlighted, the noise interference is weakened, and the picture contrast is increased, so that the picture quality of a data set is improved. The invention further provides a fundus blood vessel image segmentation method based on the self-adaptive PCNN. Accurate parameters are estimated by using an Otsu algorithm, then a U-Net secondary iteration enhancement output result is sent to an adaptive PCNN, and effective segmentation of a complete fundus vessel is realized.
Owner:CHINA THREE GORGES UNIV
Retina vessel segmentation system based on combination of hessian matrix and region growing
ActiveCN108109159AAchieve segmentationImprove breakageImage enhancementImage analysisRetina vesselsSegmentation system
The invention discloses a retina vessel segmentation system based on combination of a hessian matrix and region growing. The retina vessel segmentation system comprises a retina image preprocessing unit which is used for extracting a retina image green channel and enhancing the extracted image to improve the contrast; a hessian matrix enhancement unit which re-enhances the image through the hessian matrix and extracts the vessel direction in the retina image; a connected domain classification unit which morphologically classifies the image enhanced by the hessian matrix so as to extract smallvessels; and a region growing unit which performs region growing on the classified image to connect the broken vessel structures in the image so as to enhance the segmentation image and improve the segmentation result. Vessel segmentation of the retina fundus image can be realized by the system, and the algorithm of combination of the hessian matrix and region growing is put forward by using the mode of combining the hessian matrix and region growing for aiming at the problem of appearance of breaking points in the segmentation result of the segmentation algorithm so that the situation of vessel breaking in the extracted image can be further improved and the accuracy of vessel segmentation can be enhanced.
Owner:NORTHEASTERN UNIV
Fundus image retinal vessel segmentation method based on evolutionary neural architecture search
ActiveCN112258486AImprove Segmentation AccuracyValid ComplicationsImage enhancementImage analysisPattern recognitionComputation complexity
According to a fundus image retinal vessel segmentation method based on evolutionary neural architecture search provided by the invention, a U-shaped decoding and coding structure is used as a backbone network to search and optimize the internal structures of modules in the backbone network, so that structures and operations which are better than those of artificial design and have lower computational complexity are found for the modules; according to the invention, the neural network model can more effectively process the complex condition of the fundus image, has higher robustness for interference in the complex fundus image focus, the vascular center reflection phenomenon and the illumination imbalance phenomenon, and can more accurately extract the features of retinal vessels, therebyimproving the segmentation accuracy of the whole image. According to the method, the flexibility of the architecture is guaranteed, the searching efficiency of the architecture is improved, the crossoperation in the genetic algorithm is improved, the searching capability of the genetic algorithm in the architecture searching process is improved, and the method has more potential to be applied toclinical disease diagnosis.
Owner:SHANTOU UNIV
Fundus retina image segmentation method based on generative adversarial network
InactiveCN110570446AFew parametersDetail captureImage enhancementImage analysisGenerative adversarial networkThree vessels
An existing fundus retina image segmentation technology has defects in retina vessel segmentation precision, and a good method for segmenting small capillaries does not exist. In order to realize accurate segmentation of a fundus retina image, the invention provides a fundus retina image segmentation method based on a generative adversarial network (C-GAN). The method comprises: a generation modelextracting abstract features through a coding-decoding network structure and carrying out detail recovery to obtain a blood vessel segmentation probability graph close to a sample true value; the discrimination network inputting the sample probability graph obtained by the generation model and a sample true value label into a deep convolutional network, giving a higher label to a real sample, andgiving a lower label to a generated sample. The network parameter weight is adjusted through mutual counterbalance of the generation model and the discrimination model, and the network converges thewhole network when the generation model and the discrimination model reach balance. According to the invention, tiny capillaries of the fundus image can be segmented, and the segmentation precision ofthe whole retinal vessel is improved.
Owner:HENAN UNIVERSITY OF TECHNOLOGY
Mobile medical personalized health service method and system
ActiveCN111435612AReduce riskAlleviate the difficulty of seeing a doctorImage enhancementImage analysisPersonalizationOphthalmology
The invention relates to the technical field of fundus image analysis, mobile medical treatment and health service, and particularly relates to a mobile medical personalized health service method andsystem. The mobile medical personalized health service method comprises the steps: obtaining to-be-analyzed information, preprocessing the to-be-analyzed information, and performing optic disc positioning and macular foveal fovea positioning on a preprocessed fundus image; segmenting a retinal vessel network and a main vessel of the preprocessed fundus image; extracting and identifying retinal feature data of the fundus image, forming a structured quantitative index of the retinal feature data, and analyzing and processing the structured quantitative index of the retinal feature data; and performing interactive information exchange and processing with a remote terminal mechanism and the user. The method can realize personalized health services.
Owner:FUZHOU YIYING HEALTH TECH CO LTD
Multichannel retinal vessel image segmentation method based on U-net network
PendingCN112465842ARemove background noiseEnhanced Feature LearningImage enhancementImage analysisData setBlood vessel feature
The invention discloses a multichannel retinal vessel image segmentation method based on a Unet network. The method comprises the following steps: firstly, performing amplification processing and a series of preprocessing on a data set image to improve the image quality; secondly, combining a multi-scale matched filtering algorithm with an improved morphological algorithm to construct a multi-channel feature extraction structure of the Unet network; and then, carrying out network training on the three channels to obtain a required segmentation network, and carrying out adaptive threshold processing on an output result. According to the method, the Unet network and the multi-scale matched filtering algorithm are combined, compared with a pure Unet network, more blood vessel features can beextracted, higher segmentation accuracy and sensitivity are achieved, and the problems of insufficient segmentation and wrong segmentation of small blood vessels of the retinal blood vessel image arerelieved.
Owner:HANGZHOU DIANZI UNIV
Function of erythropoietin in the preventing and treating of retinal injury
InactiveCN101062407AProtect neuronsReduce dosageSenses disorderPeptide/protein ingredientsMammalNeuron
The invention discloses a method to prevent and treat retina injury of mammal, which comprises the following steps: giving effective dispense erythrocyte-stimulating factor (EPO) into vitreous chamber of injured eye; finishing the method. The EPO possesses the function of protecting retina vessel and neuron, which does not cause imperfect effect for other organ or tissue with local give medicine.
Owner:SHANGHAI INST OF BIOLOGICAL SCI CHINESE ACAD OF SCI
Retinal blood vessel image segmentation method and device and related equipment
PendingCN114419054AImprove Segmentation AccuracyThe segmentation result is accurateImage enhancementImage analysisImaging processingRadiology
The invention relates to the field of computer vision image processing, and discloses a retinal blood vessel image segmentation method and device. The method comprises the following steps: in a data processing stage, firstly, acquiring a to-be-detected retinal blood vessel image; then, performing data enhancement and preprocessing on the retina fundus image; in the training stage, firstly, a network model based on Transform optimization is constructed, and then network model training is carried out by using a processed training image; in a test stage, inputting the retinal blood vessel image into a trained network model for image segmentation; and finally, weighted average is carried out on prediction results of a plurality of retinal blood vessel images output by the network model to obtain the classification probability of each pixel, a final segmentation result graph is obtained, and the segmentation precision of the retinal blood vessel images is improved by adopting the retinal blood vessel image segmentation method.
Owner:XINJIANG UNIVERSITY
Method for segmenting capillaries in microscopic image based on generative adversarial network
InactiveCN112070767AAccelerated trainingCancel noiseImage enhancementImage analysisMicroscopic imageColor image
The invention discloses a method for segmenting capillaries in a microscopic image based on a generative adversarial network. The objective of the invention is to solve the problems that a vascular fracture phenomenon often occurs in an algorithm segmentation result and vascular branch details are redundant due to the limitation of an algorithm or low actual imaging contrast of a microvascular image. The method comprises the steps of establishing a training model and a sample set based on a generative adversarial network; inputting the color fundus image in the sample set into a generation model, extracting image feature information, and outputting a microvascular probability image under a microscopic image as a generation sample; carrying out enhancement processing on the microscopic image comparison adaptive histogram equalization; increasing the data volume of training, and carrying out enhancement processing on the microscopic image processed by the preprocessing unit again; distinguishing the real sample from the generated sample; inputting a to-be-segmented retinal vessel color image into the segmentation model, and outputting a vessel segmentation result. The method is applied to capillary segmentation in microscopic images.
Owner:HARBIN UNIV OF SCI & TECH
Fundus image-based health insurance fee control method and system
ActiveCN111325631AAvoid running back and forthImprove experienceImage enhancementImage analysisOphthalmologyComputer vision
The invention relates to the technical field of fundus image analysis and health big data service, in particular to a fundus image-based health insurance fee control method and system. The fundus image-based health insurance fee control method comprises the steps of obtaining a fundus image file and related body index data; encrypting the related private data; delineating an optic disc and macular; segmenting a retinal vessel network and a main vessel; extracting and identifying retinal feature data of the eye fundus image, and forming a quantitative index of retinal feature data structuralization; identifying fundus image changes and realizing quantitative analysis; related health insurance risk control, fee control and personalized health big data service and health management suggestions are given.
Owner:FUZHOU YIYING HEALTH TECH CO LTD
Device and method for dyeing retinal vessels of lead-exposed mouse
PendingCN111141575AEasy to handleReduce breakagePreparing sample for investigationFluorescence/phosphorescenceMouse RetinaStaining
The invention discloses a device and a method for dyeing retinal vessels of a lead-exposed mouse, which belong to the technical field of experimental devices. The dyeing device comprises a box body, abox cover and a middle layer, wherein the middle layer is provided with a first through hole which is through up and down, matched with the retina in size and of a cylindrical structure; a pluralityof sliding grooves extending in the radial direction are formed in the outer edge of the first through hole at intervals in the circumferential direction of the first through hole; a sliding rail anda sliding rod are arranged in each sliding groove; two bearing belts are detachably connected between each sliding rod and the inner wall face, on the opposite side, of the first through hole; and themultiple bearing belts intersect with one another to form a containing cavity used for containing the retina. The invention also discloses a dyeing method by using the device for dyeing the retinal vessels of the lead-exposed azithromycin. The dyeing device is simple in structure, the retina does not need to be repeatedly moved or flattened in the fixing, permeating, dyeing and photographing processes of the mouse retina, and damage to the retina is reduced.
Owner:GUILIN MEDICAL UNIVERSITY
Retinal vessel image segmentation system based on global information convolutional neural network
ActiveCN111598894AVulnerable Feature EnhancementImprove performanceImage enhancementImage analysisImaging processingImage segmentation
The invention discloses a retinal vessel image segmentation system based on a global information convolutional neural network, and relates to a retinal vessel image segmentation system, and aims to solve the problems of limited global information utilization and easy loss of important features in existing convolutional neural network retinal vessel image segmentation. The system comprises an imageprocessing main module, a neural network main module, a training main module and a detection main module. The image processing main module is used for acquiring an original retina image, preprocessing the acquired original retina image and inputting the processed image into the training main module and the detection main module; the neural network main module is used for establishing a convolutional neural network capable of extracting global information and strengthening features; the training main module is used for initializing network parameters to obtain a trained convolutional neural network model; and the detection main module is used for testing by using the trained model and calculating a model performance index. The invention belongs to the field of retinal vessel image segmentation systems.
Owner:HARBIN INST OF TECH
Retinal vessel segmentation method and terminal based on residual network feature extraction
PendingCN113487615AImprove the evaluation indexImage enhancementImage analysisData setFeature extraction
The invention provides a retina vessel segmentation method based on residual network feature extraction, which is applied to a neural network model, and comprises the following steps: enabling an original retina vessel image to pass through a pre-trained VGG coding layer to obtain a plurality of images, wherein the number of the images is five, the sizes of the images have a preset proportional relation with the sizes of the original retinal blood vessel images. Therefore, the segmentation precision of the network is obviously improved, and the fitting ability and generalization ability of the model are better optimized. Compared with a Unet, the network also generally has better performance in other data sets.
Owner:SHANGHAI MARITIME UNIVERSITY
Retinal Vascular Image Segmentation System Based on Global Information Convolutional Neural Network
ActiveCN111598894BVulnerable Feature EnhancementImprove performanceImage enhancementImage analysisImaging processingRadiology
Retinal Vascular Image Segmentation System Based on Global Information Convolutional Neural Network. The invention relates to a retinal vessel image segmentation system. The invention aims to solve the problems of limited utilization of global information and easy loss of important features in the existing convolutional neural network retinal vessel image segmentation. The system of the present invention includes: an image processing main module, a neural network main module, a training main module and a detection main module; the image processing main module is used to collect original retinal images, preprocess the collected original retinal images, and process The final image is input into the training main module and the detection main module; the neural network main module is used to establish a convolutional neural network capable of extracting global information and strengthening features; the training main module is used to initialize network parameters and obtain a trained volume A product neural network model; the detection main module is used to use the trained model to test and calculate the model performance index. The invention belongs to the field of retinal blood vessel image segmentation system.
Owner:HARBIN INST OF TECH
Image segmentation method of retinal blood vessels based on improved unet++
ActiveCN112508864BIncrease weightEnhanced Semantic InformationImage enhancementImage analysisContrast levelImage segmentation
Owner:KUNMING UNIV OF SCI & TECH
Device and method for measuring blood oxygen saturation of fundus retina
InactiveCN102028477BNon-destructive measurement of blood oxygen saturationMeasure blood oxygen saturationDiagnostic recording/measuringSensorsRetina vesselsBlood vessel
Owner:INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
Retina vessel measurement
Disclosed is a method for training a neural network to quantify the vessel caliber of retina fundus images. The method involves receiving a plurality of fundus images; pre-processing the fundus images to normalize images features of the fundus images; and training a multi-layer neural network, the neural network comprising a convolutional unit, multiple dense blocks alternating with transition units for down-sampling image features determined by the neural network, and a fully-connected unit, wherein each dense block comprises a series of cAdd units packed with multiple convolutions, and each transition layer comprises a convolution with pooling.
Owner:NAT UNIV OF SINGAPORE +1
A Retinal Vessel Segmentation Method Based on Convolutional Neural Network
ActiveCN109345538BAmplification method is simpleGood segmentation effectImage enhancementImage analysisData setRetina vessels
The invention discloses a retinal blood vessel segmentation method based on a convolutional neural network, which includes: preprocessing the retinal fundus image; performing block extraction on training set images; constructing a convolutional neural network for blood vessel segmentation, and using the extracted image blocks to perform Training; in the prediction stage, multiple consecutive overlapping segments are extracted for each image, and the classification probability of each pixel is obtained by averaging multiple prediction results, and the final segmentation result map is obtained. The new convolutional neural network structure proposed by the present invention for retinal vessel segmentation is a symmetrical network based on the Encoder-Decoder structure, and two skip connections are added between the Encoder part and the Decoder part. The network can not only realize the end-to-end segmentation of retinal images, but also can obtain accurate segmentation results on a limited data set, and can effectively avoid the problem of gradient disappearance, which has certain advantages compared with existing algorithms.
Owner:SOUTH CHINA UNIV OF TECH
Retina image recognition method, device, computer equipment and storage medium
The invention relates to a retina image recognition method, a device, computer equipment and a computer readable storage medium. The method comprises the steps of obtaining a retina image; analyzing retina features of the retina image through a neural network model, wherein the retina features comprise at least one of a blank area of the retina, the number of retina vessels and the width of the retina vessels; and determining a retina category corresponding to the retina image according to the retina features. By adopting the method, category identification can be carried out according to characteristics such as retinal blank areas, the number or width of blood vessels and the like during retinopathy, and the accuracy of retinal category identification is improved.
Owner:重庆艾可立安医疗器械有限公司
Automatic retinal vessel segmentation method for glaucoma
ActiveCN108986106BEliminate the effects ofGood segmentation resultImage enhancementImage analysisEye SurgeonImaging processing
The present invention proposes an automatic retinal vessel segmentation method for clinical diagnosis of glaucoma. By fusing five models of matching filter, neural network, multi-scale line detection, scale-space analysis and morphology that depend on different image processing techniques, the optic disc is eliminated. and the effects of bright areas such as exudates. At the same time, the present invention does not require a large amount of data to establish a retinal vessel segmentation model, which greatly reduces the amount and complexity of data to be processed, is easy to implement, and can effectively improve the efficiency of retinal vessel segmentation. The present invention also uses the region growing method and gradient information to iteratively grow the background and blood vessel regions on the basis of the multimodal fusion results. The segmentation results have better continuity and smoothness, and can retain more details of retinal blood vessels and more Complete visual retinal vascular network, thereby effectively assisting ophthalmologists in diagnosing diseases and reducing the burden on ophthalmologists.
Owner:ZHEJIANG CHINESE MEDICAL UNIVERSITY
Retinal vessel reconstruction method based on directed vessel tree
The invention relates to a retinal vessel reconstruction method based on a directed vessel tree. The method comprises the following steps: acquiring retinal image information and segmenting a retinal vessel by adopting a convolutional neural network; extracting a skeleton line of the blood vessel by using a convolution algorithm; intercepting a visual disc area and extracting an end node and a branch node from the skeleton line; segmenting a blood vessel section through branch nodes, and extracting skeleton lines and outer contour information of the blood vessel section to construct a directed blood vessel tree; and reconstructing the skeleton line of the retinal blood vessel and the image information of the blood vessel by using the directed blood vessel tree. According to the invention, blood vessel segments serve as main research objects, recognition and relation construction of the blood vessel segments serve as ultimate targets, blood vessels are segmented through a convolutional neural network, a skeleton of the blood vessels is extracted through a convolution algorithm, the blood vessel skeleton is segmented according to the geometrical relation, and finally a blood vessel structure diagram is reconstructed through a directed blood vessel tree. And different blood vessels are distinguished through the topological structure diagram of the reconstructed blood vessels of the directed blood vessel tree and the image information of the blood vessels.
Owner:CHONGQING UNIV OF ARTS & SCI
Retinal Vessel Segmentation Method Fused with w-net and Conditional Generative Adversarial Network
ActiveCN110930418BNot easy to mis-segmentQuick splitImage enhancementImage analysisPattern recognitionData set
The invention relates to the application of deep learning algorithms in the field of medical image analysis, in particular to a retinal vessel segmentation algorithm that integrates W-net and conditional generation confrontation network. The present invention better solves the problems of low segmentation sensitivity and insufficient segmentation of microvessels, and achieves great progress in network parameter utilization, information flow and feature resolution, and contributes to complete segmentation of main vessels and fine segmentation of microvessels , and the intersection of blood vessels is not easy to break, and the lesion and optic disc are not easy to be mistakenly divided into blood vessels. The present invention integrates multiple network models with relatively low complexity, and has excellent overall segmentation performance on the DRIVE data set, with sensitivity and accuracy rates of 87.18% and 96.95% respectively, and a ROC curve value of 98.42%, which can be used in medical treatment Computer-aided diagnosis in the field, enabling rapid and automated retinal vessel segmentation.
Owner:JIANGXI UNIV OF SCI & TECH
Retinal image arteriovenous classification device and equipment and storage medium
ActiveCN111242933AImprove classification accuracyReduce complexityImage enhancementImage analysisImaging processingRadiology
The invention discloses a retinal image arteriovenous classification device, and relates to the field of medical image processing. The device comprises an image obtaining module which is used for obtaining a to-be-classified image; a preprocessing module used for preprocessing the to-be-classified image to obtain a preprocessed image; an adversarial network module which comprises a pre-trained generator unit used for outputting the arteriovenous classification probability of each pixel point according to the preprocessed image; and a result output module used for generating an arteriovenous classification result according to the arteriovenous classification probability of each pixel point in the preprocessed image. The invention further provides retinal image arteriovenous classification equipment and a storage medium, the complexity of a classification algorithm can be reduced, the characteristics and metric functions of a retinal vessel topological structure do not need to be manually designed, and the classification speed and accuracy of the arteriovenous classification of retinal images are improved.
Owner:SOUTH CHINA UNIV OF TECH
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