33 results about "Image Quantification" patented technology

The invention relates to the technical field of image recognition, in particular to an automatic classification system for predicting cardiovascular and cerebrovascular events, which comprises the following steps of: segmenting carotid plaques and tracking the carotid plaques frame by frame to obtain preliminary plaque segmentation results; fusing and predicting to obtain a fusion segmentation result; extracting quantitative features of the carotid plaque; and performing prediction according to the fusion segmentation result and the quantitative features, and outputting a classification probability result corresponding to the cardiovascular and cerebrovascular event. According to the method, the problem of poor single-modal recognition effect is effectively solved, the accuracy of cardiovascular and cerebrovascular event prediction is remarkably improved through technical means such as multi-modal data fusion and feature guide attention, and powerful support is provided for clinical diagnosis and risk assessment. A carotid artery plaque part in common ultrasonic data is tracked and enhanced frame by frame through a plaque tracking module in advance, so that a subsequent network can capture plaque related semantic features more easily. And image quantitative features are extracted and input into a classification module, so that more accurate classification prediction is realized.

An adaptable deep learning method is provided that delivers sound hepatic lesion identification in NETs, while significantly reducing human effort for data annotation and improving model generalizability for PET image quantification. A region-guided GAN (RGGAN) model conducts image-to-image translation between list-mode simulated PET images and real-world clinical data, while preserving semantic content of interest, e.g., lesions. The RG-GAN model is integrated with a lesion detection model into an end-to-end, unified framework for joint-task learning, such that the two models can benefit from each other. The RG-GAN translates the list-mode simulated data into real world-style images, which appear to be drawn from the real clinical PET image dataset, and feeds the translated images into the lesion detection model for training. In order to deal with the limited diversity of list mode-simulated PET image data, a specific data augmentation module is incorporated into the unified framework to improve model training.

The invention belongs to the technical field of postoperative care guidance, and discloses an oral implant postoperative care guidance system based on edge calculation. The system comprises an original image acquisition and preprocessing module, a real-time image analysis and quantification module, a data fusion feature construction module, a personalized reply dynamic modeling module, an intelligent risk assessment and decision module and a self-adaptive nursing guidance generation module. The method comprises the steps of obtaining an image quantitative index data set, obtaining a multi-modal time sequence feature vector, obtaining a personalized recovery prediction report, carrying out risk assessment based on the personalized recovery prediction report, obtaining a risk assessment decision report, and generating a personalized nursing guidance report based on the risk assessment decision report. The method has the remarkable advantages of being high in nursing evaluation authenticity, large in risk active prediction effect and good in personalized nursing guidance effect.

This invention provides a clinical trial matching system and method for tumor patients based on a large language model and OCR technology, applicable to the field of medical data processing. This application parses clinical data and trial information, processes unstructured text, and generates structured clinical feature data through contextual association analysis; extracts key data and performs double verification and correction to generate supplementary structured data information; enhances the structured clinical feature data and supplementary information based on a multimodal processing pipeline module, extracts image quantification indicators, analyzes immunohistochemical results, and generates a comprehensive matching score; compares patient features with inclusion and exclusion conditions item by item through a rule engine and semantic similarity calculation to generate preliminary matching results; corrects misjudgments of marginal cases through context-aware multi-round reasoning, and adjusts the ranking based on clinical trial priority weights to generate an optimized clinical trial matching list; and generates a clinical trial matching report based on the above data.

This invention discloses a method and system for quantitatively assessing the degree of fibrosis in kidney pathological sections, belonging to the field of medical image processing technology. The method includes: performing color normalization and adaptive contrast enhancement preprocessing on Masson stained section images; classifying pixel-level tissue components using a multi-component semantic segmentation network containing a channel-space dual-path attention module; calculating the fibrosis area ratio and outputting a CI score based on the Banff grading standard; generating a fibrosis spatial distribution heatmap; and calculating the annual fibrosis progression rate through longitudinal follow-up comparison.

The invention relates to the technical field of synthetic aperture radar image processing, and discloses an SAR image quantization and enhancement method based on histogram statistics, and the method comprises the steps: obtaining a minimum value and a maximum value of an SAR amplitude diagram; setting parameters required for SAR image quantization and enhancement, performing normalization processing on the SAR amplitude diagram according to a histogram statistical order to obtain a first normalized amplitude diagram, and extracting sample points from the first normalized amplitude diagram; determining an amplitude quantization upper limit according to the histogram result and the image brightness adjustment factor; according to the amplitude quantization upper limit, performing amplitude limiting processing on the first normalized amplitude diagram, and performing second normalization to obtain a second normalized amplitude diagram; performing contrast enhancement on the second normalized amplitude diagram to obtain an enhanced amplitude diagram; the SAR image enhancement method and the SAR image enhancement device solve the technical problems that according to an existing enhancement method, noise is prone to being amplified, brightness distortion is caused, or contrast ratio improvement is insufficient, and the overall processing efficiency is low.

The invention relates to the technical field of immunohistochemical staining image quantitative analysis, in particular to an immunohistochemical staining image quantitative analysis method and system, and the method comprises the following steps: S1, obtaining a digital image of an immunohistochemical staining section; and S2, carrying out color deconvolution processing on the digital image, and decomposing the digital image into an exclusive channel image corresponding to at least one target coloring agent and a background channel image. According to the method, color deconvolution is realized through a non-negative matrix factorization algorithm, target coloring agent signals and background and other dyeing signals in the composite dyeing image are accurately split, and the problem that dyeing components of an existing system are not thoroughly separated is solved; through combination of a Mask R-CNN deep learning network and multi-mode adaptive parameters, positive and negative cells in different dyeing modes can be accurately identified, and multiple scenes such as nuclear dyeing, membrane dyeing and slurry dyeing can be adapted.

This invention discloses a low-power chip, its data processing method, and a storage medium. The method includes: evaluating whether the bias will overflow after quantization based on a first quantization factor of a block image, a second quantization factor of the weight matrix of the block image, and the bias corresponding to the block image; if it is determined that the bias will overflow after quantization, then at least one of the first quantization factor and the second quantization factor is corrected; quantizing the block image, the weight matrix, and the bias using the corrected quantization factor and the remaining uncorrected quantization factors; performing fixed-point operations on the quantized block image, the quantized weight matrix, and the quantized bias using a fixed-point arithmetic unit for the corresponding network layer to obtain the fixed-point operation result; and performing inverse quantization on the fixed-point operation result to obtain the block features of the block image.

A method for quantifying liquid leakage from a barrier coating includes: a) providing a system for acquiring and analyzing images of a wet substrate; b) loading the wet substrate into a holder; c) illuminating the wet substrate using an illumination system; d) acquiring at least one image of the wet substrate using an imaging system; e) using an analysis unit to transform at least one image of the wet substrate to provide at least one transformed image, and quantifying liquid leakage from the barrier coating based on the at least one transformed image; and f) providing an output including a value that identifies the amount or percentage of liquid on the wet substrate, and / or a generated image showing the amount or percentage of liquid on the wet substrate.

The invention provides a knee osteoarthritis treatment decision-making tree system and device, and relates to the technical field of knee osteoarthritis treatment decision-making trees, and the system comprises a data receiving module, a data processing module and a result generation module. The data receiving module is used for receiving natural language text data and iconography image data of the patient and respectively preprocessing the natural language text data and the iconography image data; the data processing module comprises a text feature unit, an image feature unit and a decision unit; the decision-making unit is used for fusing the text features and the image quantization features to obtain comprehensive features; based on the comprehensive features, through a decision tree model, a treatment scheme for knee osteoarthritis is output; and the result generation module is used for sending out the treatment scheme. According to the scheme, information such as medical records of patients can be automatically analyzed, personalized treatment schemes are given, interference of human factors is reduced, real-time and objective decision support is provided for doctors, and therefore the diagnosis and treatment efficiency and effect are improved.

The invention relates to the technical field of medical health, in particular to an intelligent evaluation and intervention system for muscle quality of a patient with advanced solid tumors. The multi-dimensional dynamic evaluation unit is used for forming the extracted somatic cell mass, the score of the anorexia-cachexia syndrome, the standardized skeletal muscle index and the average muscle radiation attenuation value into multi-dimensional feature data, and the multi-dimensional dynamic evaluation unit is used for evaluating the quality of the somatic cells on the basis of the multi-dimensional feature data and fusing tumor treatment scheme data. Generating a multi-dimensional muscle health index and a malignant fluid risk probability through a decision tree model, and dividing patients into different risk levels; and the intervention strategy generation unit is used for outputting a comprehensive intervention scheme based on the multi-dimensional muscle health index and the malignant fluid risk probability in combination with the real-time tolerance data and the risk level of the patient. According to the system, the L3 level skeletal muscle index and the muscle radiation attenuation value can be extracted more accurately and stably, manual annotation and manual variation are reduced, and the reliability of image quantification is improved.

The invention discloses a quantitative analysis method for a jointed rock mass blasting cracking mode, which comprises the following steps: establishing a jointed rock mass blasting stress wave propagation theoretical model, deducing a propagation equation of stress waves in a jointed rock mass, and determining stress wave reflection, transmission and superposition rules; a jointed rock mass blasting numerical model is constructed based on ANSYS / LS-DYNA software, material parameters and state equations of rocks, joints, explosives and air are determined, an RHT damage model is adopted for the rocks and the joints, an MATHIGHEXPLOSIONBURN model is adopted for the explosives and combined with an EOSJWL state equation, and an EOSLINEARPOLYNOMIAL state equation is adopted for the air. According to the method, theoretical derivation, numerical simulation and image quantitative analysis are combined, and quantitative analysis of the blasting cracking mode of the jointed rock mass is achieved. The engineering cost can be effectively reduced, and the blasting effect and the construction safety are improved.

The application discloses a method for multi-modal large model inference acceleration, and belongs to the technical field of artificial intelligence, aiming to solve the technical problems of long time consumption in the pre-filling stage caused by large image quantization loss and too many image coding tokens during the inference of a multi-modal large model. The method first extracts the overall features of an image through a mask auto-encoding network, then acquires the candidate regions of the image by using a detection network and completes the alignment and extraction of the regional features, and obtains the image sequence feature vector after the overall features and the local features are fused; meanwhile, the input text is segmented and embedded to obtain the text word vector, and the two types of vectors are spliced and fused according to the sequence dimension to input a large language decoding model to complete decoding. The application can greatly reduce the image token calculation amount, significantly reduce the time consumption in the inference pre-filling stage under the premise of ensuring the inference effect, and improve the overall inference efficiency of the multi-modal large model.

The invention relates to a lubricating oil emulsification detection system and detection method based on microscopic video analysis. The system comprises an image acquisition unit used for realizing microscopic video stream acquisition by adopting a camera or a camera; the calculation processing unit is used for executing an image processing and analysis algorithm; the result storage unit is used for storing images with labels, quantized data and report files; the detection method comprises the following steps: S1, collecting a microscopic video stream, initializing an analyzer and a storage environment, and establishing a corresponding directory; s2, reading video frames according to a set interval and carrying out basic preprocessing; s3, passing the read video frame through a core detection assembly line: highlight interference elimination-candidate emulsified block mass extraction-background modeling verification-morphological optimization processing; s4, performing quantitative analysis on the detected candidate area; and S5, generating a final report. The method is sensitive in detection precision and high in efficiency, realizes quantification and standardization, and shows remarkable advantages in the aspects of economy, reliability and environmental safety.

The invention discloses a method for observing the shape and number of synapses in a dentate gyrus region based on image analysis, and relates to the technical field of neurobiology. The method comprises the following steps: S1, tissue treatment: after an experimental animal is anesthetized, pouring a buffer solution and a stationary solution into the heart in stages, separating hippocampus tissue, trimming and fixing, pre-cooling, slicing and preserving at low temperature; s2, dyeing and marking: performing targeted marking on the dentate gyrus area of the sea horse by adopting DiI particles through a fine tungsten steel through needle, and preserving at low temperature after incubation; s3, fixing and mounting: fixing for the second time, and mounting with an anti-fluorescence quenching agent after DAPI nuclear dyeing; s4, confocal imaging is carried out, exclusive parameter layer scanning is set, and qualified dendritic spine images are screened; and image quantitative analysis can be further carried out, and accurate quantification is realized according to classification standards. The method is standard in operation, greatly improves sample quality and experimental result credibility, optimizes experimental efficiency and controllability and reduces technical threshold by means of targeted marking and standardized processes, and is suitable for relevant basis and application research of the hippocampal synapses.

The application belongs to the field of display module residual image quantitative test, and relates to a display module residual image quantitative test device, which comprises a lower jig, an upper jig b provided above the lower jig, and an upper jig a fixedly connected to one side of the upper jig b; a shielding assembly provided between the lower jig and the upper jig b, the shielding assembly comprising a through hole b, a partition grid a and an optical test probe; and a switching assembly provided on the top of the lower jig. Through the arrangement of the shielding assembly, the upper jig a or the upper jig b can cover the entire display module main screen area, and only the through hole a or the through hole b position is reserved for brightness measurement, external light interference is effectively shielded, and it is ensured that the measurement data reflects the true brightness value of the display module main body itself. Through the arrangement of the switching assembly, the switching of the upper jig a and the upper jig b can facilitate the test of the black-white junction center position and improve the detection comprehensiveness.

The application discloses a kind of oral prosthetic body abutment relationship image quantitative evaluation systems, belong to oral prosthetic body image processing technical field, including image acquisition and pretreatment module, feature atlas construction module, topological analysis calculation module and quantitative evaluation mapping module.Image acquisition and pretreatment module obtains the three-dimensional scanning image of prosthetic body in situ state, obtains two-dimensional development image by gray mapping and two-dimensional development, separates out highlight contact zone by multi-scale morphological opening and closing reconstruction operation;Feature atlas construction module constructs morphological feature atlas according to the morphological gradient distribution characteristics of highlight contact zone under different scales;Topological analysis calculation module tracks the continuous boundary of abutment contact zone in morphological feature atlas, and calculates the length, width and area quantitative value of contact zone;Quantitative evaluation mapping module maps quantitative value to prosthetic three-dimensional space frequency domain, generates quantitative index for evaluating the closeness of abutment relationship.

A method for quantifying surface defects on a coated substrate includes: a) providing a system for acquiring and analyzing images; b) loading a coated substrate, wherein the coated substrate includes a coating formed on the surface of the substrate, onto a holder; c) illuminating the coated substrate using an illumination system, wherein illuminating the coated substrate includes illuminating the coated substrate at an incident angle of 15° to 85° with respect to a plane parallel to the surface of the coated substrate to generate shadows of any defects on the surface of the coated substrate; d) acquiring one or more images of the coated substrate using an imaging system; e) using an analysis unit to transform one or more images of the coated substrate and provide one or more transformed images, and quantifying surface defects on the coated substrate based on one or more transformed images; and f) providing an output, wherein the output includes a value that identifies the amount or percentage of defects on the coated substrate, and / or a generated image showing the amount or percentage of surface defects on the coated substrate.

The invention discloses a method for evaluating the recovery benefit of a giant panda national park greenway on a refined patient. The method comprises the following steps: acquiring an image of the giant panda national park greenway; preprocessing the giant panda national park greenway image; selecting a sperm fraction patient subject; obtaining the recovery benefit of the perceptual classification patient subject on the greenway image of the giant panda national park by adopting a perceptual recovery scale; screening environmental factors in the giant panda national park greenway image; quantifying environmental factors in the giant panda national park greenway image; and constructing a correlation prediction model between the environment characteristic factors and the scores of the perception recovery scale. According to the method, environment feature factors are quantified through images, greenway environment features are systematically analyzed, the association prediction model is constructed, dynamic association between environment elements and rehabilitation indexes of the precisely-classified patient is revealed, and a new data-driven normal form is provided for scientific design of the rehabilitation-oriented greenway.

The invention discloses a method for cooperatively distinguishing pores and bubbles in ultra-stable foam based on FC and sphericity analysis, and belongs to the field of solid-liquid separation of flotation clean coal, and the method comprises the following steps: obtaining a three-dimensional grayscale image of the foam through Micro-CT, performing three-dimensional reconstruction according to a filtered back projection algorithm, and extracting a gap structure; determining a representative sub-volume based on the change in porosity to ensure statistical reliability; adopting a Feret Caliper method to calculate geometrical characteristic parameters of the gap and performing morphological classification; determining a distinguishing threshold TH by combining the relationship between the sphericity and the bubble volume fraction; and automatically identifying and distinguishing the bubbles and the pores according to the threshold value. And finally verifying by using an optical microscope result. According to the method, non-destructive recognition and quantitative analysis of the internal structure of the ultra-stable foam are achieved, high precision and repeatability are achieved, and a reliable image quantification means is provided for optimization of a coal dehydration process.

The invention discloses a WSI image quantitative evaluation system based on cross-scale feature collaboration and multi-dimensional quality factor fusion, belongs to the technical field of digital pathological image analysis, and aims at solving the problems that an existing evaluation method is single in dimension and insufficient in robustness and efficiency. The system obtains a WSI image through scanning, randomly samples a tissue area under a basic amplification factor, screens out an unclear area by adopting a definition detection method, and feeds back a re-scanning instruction. And for the clear area, synchronously extracting image slices with different objective lens magnifications under the same hierarchy of the pyramid structure, and constructing a multi-magnification slice data set. And carrying out cell identification and counting based on high-magnification slices, carrying out bubble area detection and staining defect identification based on low-magnification slices, and respectively generating cell number, slice integrity and staining quality sub-item scores. And finally, performing weighted fusion on each item score to obtain a slice comprehensive quality score, and realizing objective quantitative evaluation on the WSI image quality.

The application discloses a CT image quantitative analysis system based on machine learning and relates to the technical field of image processing. The application comprises an information acquisition module for acquiring CT image data and analysis requirements; and an information processing module for performing specification uniform processing on the CT image data based on the CT image data through a specification processing method to obtain to-be-analyzed image data. The application can cross-verify and optimize multiple sub-information obtained from the to-be-analyzed image data through preset verification quantity and a subsequent cross-verification module, so as to improve the accuracy of characteristic region name and quantity information reflecting the to-be-analyzed image data, and further improve the accuracy of subsequent model processing. A single characteristic recognition model can analyze only a single characteristic region, so that the efficiency of characteristic recognition analysis can be improved, and multiple different recognition analysis requirements can be processed, multi-task processing is realized, and the efficiency of information processing is improved, so as to meet the increasing demand for precise diagnosis in clinical practice.

The invention discloses a UBM image quantitative analysis method and system for silicone oil emulsification evaluation, belongs to the field of medical image processing, and aims to objectively quantify emulsification-related acoustic signs in a silicone oil filling postoperative UBM image. The method comprises the following steps: communicating with UBM equipment, monitoring gain and probe perpendicularity, and outputting a control / prompt instruction for standardized acquisition; desensitizing the image and masking a non-scanning area; extracting the characteristics of floating particle density, artifact intensity, high-echo particle aggregation region area proportion, tissue infiltration signal-to-noise ratio, corneal endothelial post-deposition density and the like; and fusing the center and each orientation feature, introducing a space weight matrix to calculate a weighted acoustic distribution feature value ADCV, and outputting the ADCV and a structured quantization result for clinical evaluation and follow-up comparison.

The present application relates to the technical field of image recognition, in particular to a traditional Chinese medicinal material impurity rapid screening and identification method and system based on visual fusion, which comprises the following steps: acquiring a projection image, a bright field microscopic image, an oblique illumination microscopic image, a pre-expansion thin layer image and a post-expansion ultraviolet image, quantifying projection geometry and determining sheet state and dry state, calculating contour residual error, combining microscopic lines and oblique illumination offset to generate foreign fiber features, correcting spot migration rate and splicing a multi-source feature matrix, and inputting the random forest to output impurity screening results. In the present application, through the synergistic constraint of projection morphology, microscopic texture and thin layer migration, the dry and wet sheet state difference, edge shadow, fiber continuity and spot drag expansion deviation are all included in the same discrimination link, the distinguishing ability of medicinal material body and mixed materials such as sand, gravel, fiber and foreign varieties is improved, the misjudgment caused by similar appearance and batch difference is reduced, and the stability of impurity positioning, morphology identification and category judgment is improved.

The invention discloses an intelligent periappendicular abscess operation opportunity prediction system which is suitable for an operation opportunity auxiliary judgment task in a multi-source data environment. The system comprises a clinical data acquisition module, a medical image analysis module and a fusion prediction engine, the medical image analysis module is based on a low-rank optimization-federated abscess segmentation model, realizes multi-node abscess area identification and quantification under the condition of not concentrating image data, and adopts an image fingerprint aggregation and heterogeneous structure adaptation strategy to improve segmentation accuracy and compatibility; constructing a kernel set driven LSTM model by fusing a prediction engine, integrating structured indexes and image quantization parameters, dynamically constructing a high-quality training subset, and outputting an operation urgency score and a recommendation time window through trend analysis; the system has the characteristics of strong privacy protection capability, high model efficiency and excellent trend prediction precision, and is suitable for opportunity prediction scenes of structural risk events.

This invention provides a method and electronic device for generating periodontitis reports based on dual-energy CBCT. The method includes: acquiring high- and low-energy dual-energy CBCT projection data, an intraoral 3D scanning model, and structured electronic medical record data of the patient; reconstructing high- and low-energy CBCT volumetric images; obtaining image quantification features and quantification indicators; aligning and fusing the image quantification features with clinical features in the intraoral 3D scanning model and electronic medical record data at the tooth position level to obtain staging results; calculating a consistency coefficient; when the consistency coefficient is lower than a preset threshold, triggering a conflict alarm and automatically adjusting the evidence weight in the final staging decision to obtain conflict detection results; and automatically generating a structured diagnostic report based on the quantification indicators, staging results, and conflict detection results. This aims to improve diagnostic accuracy in complex oral environments.

The application discloses a method and system for detecting self-compatibility of Brassica campestris L. and relates to the technical field of biological detection. The method comprises the following steps: collecting flower data of the Brassica campestris L.; quantitatively judging the flower data of the Brassica campestris L.; collecting structural feature data of the flower of the Brassica campestris L.; and quantitatively judging the structural feature data of the flower of the Brassica campestris L. The application obtains an image detection quantization value by acquiring an original image of the flower of the Brassica campestris L. and quantizing image quality, judges whether image quality optimization is to be performed according to the quantization value, acquires structural feature data of the flower of the Brassica campestris L. according to the result after optimization if the optimization is to be performed, and quantitatively judges whether structural feature optimization is to be performed based on the structural feature data of the flower of the Brassica campestris L., thereby improving the accuracy of self-compatibility detection, and solving the problem of low detection accuracy of self-compatibility of the Brassica campestris L. caused by differences in image quality and structural features of the flower of the Brassica campestris L. in the prior art.

The application discloses an image compression coding method without entropy coding, an image compression coding device without entropy coding, an electronic device and a storage medium. The image compression coding method without entropy coding comprises the following steps: performing sampling convolution on an original image to obtain image data after sampling convolution, performing quantization processing on the image data after sampling convolution to obtain image quantization data, and performing adaptive run-length coding on the image quantization data to obtain image compression data. The image compression coding method without entropy coding provided by the embodiment of the application can obtain image compression data through adaptive run-length coding, improve the compression ratio, improve the image data compression effect, and the compression amount obtained through the encoder can meet the needs of actual application, and the following condition existing in the related art is improved: the compression amount obtained through the encoder in the related art is not high enough in terms of compression ratio, which leads to the difficulty in meeting the needs of actual application in terms of compression amount, and the compression ratio needs to be further improved.

The invention discloses a decompressed WebP image quantization parameter estimation method and device and a terminal, and the method comprises the steps: obtaining a target image, classifying macro blocks of the target image, and obtaining a plurality of target macro block groups, the target image being a compressed and decompressed WebP image; performing frequency domain periodic feature driving on the multiple target macro block groups to obtain quantization step coarse estimation; and performing adaptive optimization and candidate quantization step size correction on the quantization step size coarse estimation to obtain a target optimal quantization step size. According to the method, the problems of mismatching of feature extraction dimensions and mismatching of coefficient distribution models when an existing quantitative estimation method for JPEG is directly applied to WebP images in the prior art are solved, and key technical support with high precision and high efficiency is provided for authenticity verification of digital images.

Systems, methods, and devices for monitoring intraocular pressure are provided. A plurality of images is acquired using a camera, in which the plurality of images is associated with a blink by an eye of a subject. A corneal response is quantified using the plurality of images. An eye pressure of the subject is quantified using the corneal response. A risk of glaucoma is quantified for the subject using the eye pressure of the subject.