102 results about "Retinal blood vessel" patented technology

An apparatus and method for safely cannulating a blood vessels, including but not limited retinal blood vessels, is described. In one embodiment, the apparatus can consist of a micropipette/microcannula, micromanipulator and positioner mounted to a base, which is attached to a wrist rest commonly used in eye surgery. The micropipette/microcannula is connected to tubing such that a medication may be injected through the micropipette/microcannula into the blood vessel or conversely, a small quantity of material may be removed from a blood vessel. Alternatively, a catheter, wire or stent may be placed through the micropipette/microcannula to treat or diagnose an area remote from the insertion site. The ability to cannulate a retinal blood vessel should be efficacious in the treatment of vein and artery occlusion, ocular tumors and other retinal, vascular and optic nerve disorders that would benefit from diagnosis and/or treatment. In another embodiment, a self-sealing needle is provided which allows the perforation of a blood vessel or other structure and the minimization of hemorrhaging when the needle is withdrawn from the blood vessel/structure. Another embodiment discloses an ocular implant needle.

The invention relates to an attention mechanism U-shaped densely connected retinal (a novel retinal blood vessel segmentation method fusing DenseNet and Attention U-net network) blood vessel segmentation method, comprising the steps of retinal blood vessel image preprocessing and constructing a retinal blood vessel segmentation model. The method of the invention can effectively solve the problemsthat adjacent blood vessels are easy to connect, the micro blood vessels are too wide, the small blood vessels are easy to break, the blood vessel intersection is insufficient in segmentation, the image noise is too sensitive, the gray value of the object and the background is crossed, the optic disc and the lesion focus are missegmented, and the like. The method of the invention integrates a plurality of network models under the condition of low complexity, the excellent segmentation results are obtained on DRIVE dataset, the accuracy and sensitivity are 96.95% and 85.94%, respectively, whichare about 0.59% and 7.92% higher than those published in the latest literature.

A platform is proposed for automated analysis of retinal images, for obtaining from them information characterizing retinal blood vessels which may be useful in forming a diagnosis of a medical condition. A first aspect of the invention proposes that a plurality of characteristics of the retina are extracted, in order to provide data which is useful for enabling an evaluation of cardiovascular risk prediction, or even diagnosis of a cardiovascular condition. A second aspect uses fractal analysis of retinal images to provide vascular disease risk prediction, such as, but not limited to, diabetes and hypertension.

The invention discloses an arteriovenous retinal blood vessel classification method based on an eye fundus image. The method includes the steps that first, a global blood vessel set and optic disk positioning information of the fundus image are acquired, the global blood vessel set is a set of all blood vessels in the fundus image, and the optic disk positioning information comprises the optic disk center of the fundus image; second, main blood vessels are determined according to the global blood vessel set and the optic disk positioning information and classified so that main blood vessel classification information can be obtained; third, the main blood vessel classification information is used for classifying the blood vessels in the global blood vessel set through a breadth first-search algorithm based on SAT so that global classification information can be obtained. According to the method, the classification information of the main blood vessels around an optic disk is first obtained, external expansion diffusion is performed from the main blood vessels through the breadth first-search algorithm based on SAT so that all the blood vessels can be obtained, a complete automatic blood vessel classification method is achieved, manual intervention is not needed, and classification precision is high.

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 provides a full-automatic retinal blood vessel analysis method and system suitable for intelligent portable equipment. The invention improves a saliency feature method by adding a preprocessing step which comprises performing homogenization on a fundus image and removing the central light reflection treatment of the blood vessel, thereby realizing rapid segmentation of the retinal blood vessel. The method has the advantages of good stability, simple operation and high speed. The invention realizes accurate analysis of various retinal blood vessels and realizes the combination with intelligent portable equipment by optimizing algorithm complexity and operation speed, so computer-aided diagnosis can provide systemic blood vessel analysis with strong applicability, stable calculation and high precision in retinal image analysis.

The invention discloses a method for enhancing blood vessels in retinal images based on the directional field, which includes: step one: the directional field Theta of a retinal image is calculated; step two: the blood vessels in the retinal image are enhanced according to the directional field Theta to obtain the enhanced image of retinal blood vessels; and step three: a Gabor wave filter is used for filtering noise in the enhanced image. The method can effectively enhance small blood vessels with low contrast ratio in the retinal images, which brings great convenience to the subsequent processing of the retinal images. In the invention, normalization processing is carried out to image backgrounds while the blood vessels are enhanced, which effectively overcomes influence of image noise and uneven lightness and saves the single normalization step. The invention has important practical value in the fields of dissection of blood vessels in the retinal and heart images and other fields.

A personal authentication method comprising imaging, on an image sensor as a laser speckle using an optical system, light reflected from retinal blood vessels of the ocular fundus and a blood vessel layer in ocular fundus internal tissue when a laser beam is expanded and made to irradiate the ocular fundus, calculating a quantity that represents the rate of change with respect to time of the amount of light received for each pixel of the laser speckle, obtaining an ocular fundus blood flow map as a two-dimensional map of the numerical values of the quantity, and comparison-checking against pre-registered personal data utilizing at least one, observed in the blood flow map, of blood flow distribution data, a pattern reflecting the course of retinal blood vessels, a pattern reflecting the course of blood vessels in ocular fundus internal tissue observed superimposed thereon, and data on changes thereof over time, and a device therefor. In accordance with the method and device of the present invention utilizing the ocular fundus blood flow rate map, a personal authentication method and device can be obtained that have remarkably higher accuracy than conventional methods and devices.

The invention discloses an eye fundus image arteriovenous retinal blood vessel classification method based on a breadth first-search algorithm. The method includes the steps that first, a global blood vessel set and optic disk positioning information of a fundus image are acquired, the global blood vessel set is a set of all blood vessels in the fundus image, and the optic disk positioning information comprises the optic disk center of the fundus image; second, main blood vessels are determined according to the global blood vessel set and the optic disk positioning information and classified so that main blood vessel classification information can be obtained; third, the main blood vessel classification information is used for classifying the blood vessels in the global blood vessel set through the breadth first-search algorithm based on SAT so that global classification information can be obtained. According to the method, the classification information of the main blood vessels around an optic disk is first obtained, external expansion diffusion is performed from the main blood vessels through the breadth first-search algorithm based on SAT so that all the blood vessels can be obtained, a complete automatic blood vessel classification method is achieved, manual intervention is not needed, and classification precision is high.

The invention relates to a non-fluorescent eye fundus image based automatic segmentation method for retinal blood vessels. The method comprises following steps: step one, an image is preprocessed, blood vessel characteristics are enhanced, and background noise is weakened; step two, morphological characteristics of the retinal image are extracted with a multi-scale linear operator, and a refined blood vessel framework is segmented with two-dimensional Gabor wavelet transform; step three, after the refined blood vessel framework is segmented, the image is subjected to path morphology filtering, the maximum path length is combined with geometrical characteristics of a blood vessel region, and an adjacent map is constructed; step four, a binaryzation process is performed with regional connectivity analysis and a hysteresis threshold technology for auxiliary segmentation of microvessels, and the segmentation is finished. Compared with the prior art, the method has the advantages as follows: the blood vessel location accuracy is higher, the adhesion phenomena are reduced, and a retinal blood vessel network of the eye fundus image is effectively segmented.

The invention relates to a retinal blood vessel classification method and a retinal blood vessel classification device. The retinal blood vessel classification method combines a local binary algorithm with an independent component analysis technique for integrated use in the aspect of fundus blood vessel feature extraction, since the local binary algorithm and the independent component analysis technique performs structural description of blood vessel features from different angles, the method inherites the advantages of each single technology, supplements the blood vessels to certain extent, and filters the influence of more noise; and the retinal blood vessel classification method finally improves the precision of fundus blood vessel arteriovenous classification, reduces the proportion of blood vessels without classification marks, and provides a solid data basis for the follow-up diagnosis of diseases related to fundus blood vessels.

The invention discloses a retinal vessel segmentation method and system based on a retinal fundus image, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a to-be-detected retinal fundus image; constructing a basic module according to the retinal fundus image features; cascading N basic modules to serve as a final network model, the to-be-detected retinal fundus image serving as input of the whole network model, and obtaining a segmentation result of retinal vessels. The foreground characteristics of the previous basic module and the originalpicture are transmitted to the next basic module together, so that the rear basic module can inherit the learning experience of the front basic module, the training process is accelerated, and the problem of data imbalance is effectively solved; the to-be-detected retinal fundus image is used as the input of the overall model S-UNet, and the obtained segmentation result of the retinal blood vessel is more accurate.

The invention relates to the field of biomedical image processing, in particular to a choroidal angiography method based on optical coherence tomography imaging body scanning. The method can be widelyused for screening and diagnosis of medical ophthalmic diseases. The method includes the steps of S1, acquiring original data of OCT body scanning fundus; S2, extracting retinal blood vessel shadow data and choroidal blood vessel data from the original data; S3, using the retinal blood vessel shadow data for masking the choroidal blood vessel data to obtain choroidal angiography data. According to the technical scheme above, by extracting the retinal blood vessel shadow data to mask the choroidal blood vessel data, retinal blood vessel shadows in the choroidal blood vessel data are removed, so that the influence of the retinal blood vessel shadows on the contrast and continuity of choroidal blood vessel projection images is reduced, and choroidal vessels can be completely and clearly displayed.

The invention discloses a retinal vessel artery and vein distinguishing method. The method comprises: obtaining a blood vessel extraction image, a fundus image and optic disc center coordinates; extracting a main blood vessel according to the blood vessel extraction image, the fundus image and the optic disc center coordinate; the method comprises the following steps: obtaining a main blood vesselimage, intercepting a main blood vessel based on the main blood vessel image to obtain a plurality of single blood vessel sections, performing pipe diameter measurement on the plurality of single blood vessel sections to obtain the pipe diameter width of each single blood vessel section, and selecting and obtaining a plurality of arteriovenous blood vessel pairs according to the pipe diameter widths. The method is advantaged in that the test effect on the fundus retina color image of the actual scene is excellent, relatively good effects are realized for fundus images of different types, different brands and different levels of image quality, algorithm robustness and universality are relatively strong, and possibility is provided for retina vessel arteriovenous distinguishing and tube diameter measurement algorithm actual scene landing.

Disclosed is a method for measuring the retinal blood vessel diameter and thickness of a vessel wall. The method is characterized by including: adopting an SD-OCT (spectral domain optical coherence tomography) scanning system for measurement and selecting all the retinal blood vessels in an area with the distance apart from the edge of an optical disc 0.5-1.0 times of the diameter of the optical disc as measuring objects through the SD-OCT system; allowing a scanning line to be perpendicular to the running direction of a positioning blood vessel to acquire a cross section figure of the retinal blood vessels; magnifying the scanned cross section figure of the blood vessels; moving a scale ruler to measure inner diameter and outer diameter of each blood vessel, wherein the thickness of the vessel wall=(the outer diameter-the inner diameter)/2; calculating the central retinal artery equivalent Wt; taking the central retinal artery equivalent Wt=(0.87Wa<2>+1.01Wb<2>-0.22WaWb-10.73)<1/2> as a diagnostic basis. The method has the advantages of being free of damage, non-invasive, free of mydriasis and high in measurement precision.

The invention relates to the technical field of image analysis, health service and data processing, and provides a method for analyzing the hypertension retinal vessel change characteristic data. Themethod includes: obtaining the fundus image of the patient; extracting and identifying the retinal vessel change characteristic data of a fundus image of a patient, wherein retinal vessel change characteristics comprise limitation retinal artery constriction; analyzing and comparing the retinal vessel change characteristic data of the patient in different periods; further obtaining the change condition of the fundus screening characteristic data of the patient; analyzing blood pressure control of the patient in a near period and related prevention and treatment effect conditions of hypertension. A powerful excitation mechanism is provided for enhancing the compliance of basic treatment intervened by the lifestyle of a patient, a user is greatly helped to detect and control the blood pressure consciously and regularly, and the method has important significance in treatment effect evaluation and chronic disease management of hypertension.

The invention discloses a retinal arteriovenous classification method based on topological structure estimation. The method comprises the steps of performing pixel-level segmentation on a retinal vessel image through a U-net deep learning network to obtain vessel segmentation result information; separately carrying out topology estimation on the blood vessel segmentation result information throughan advantage set theory, establishing a complete topology tree set, and obtaining topology information; carrying out arteriovenous classification on the myopia disc area blood vessels through the advantage set theory, and obtaining a myopia disc area blood vessel arteriovenous classification result; and processing the topological information and the arteriovenous classification result of the myopia disc region blood vessels according to a label propagation algorithm to obtain global arteriovenous classification. The invention further discloses a retinal arteriovenous classification device based on topological structure estimation. According to the invention, the topology of retinal vessels is continuously added into arteriovenous classification work, and the arteriovenous problem of vessels in a far optic disc area in fundus color photograph can be effectively solved through topology information guidance.

The invention relates to a retinal vessel segmentation method and device based on guided filtering. The retinal vessel segmentation method based on guided filtering comprises the following steps: preprocessing a color fundus image to enhance the contrast; performing phase consistency feature extraction and multi-scale line operator feature extraction on the preprocessed image; taking the response diagram of the multi-scale line operator as a guide image, and performing guide filtering on the phase consistency characteristic response diagram to obtain a filtered and output characteristic response diagram; using an FCM clustering algorithm to construct a membership matrix for clustering the output feature response map to obtain two clustering centers, and performing classification according to the membership of pixel sample points belonging to the two clustering centers to obtain a retinal blood vessel segmentation map; and performing denoising processing on the retinal blood vessel segmentation image to obtain a final retinal blood vessel segmentation result image. According to the retinal blood vessel segmentation method based on guided filtering, the detection capability of small blood vessels with low contrast is improved.