109 results about "Scatter correction" patented technology

The invention discloses a high-sensitivity carbendazim detection method based on terahertz metamaterial resonance enhancement, and provides a terahertz metamaterial sensor with a cross-shaped composite double-peak structure, a terahertz spectrum technology is utilized to collect transmission spectrums, the difference of different concentrations among terahertz spectrums is explored, and the detection accuracy is improved. The analysis finds that the transmission peak amplitude of the terahertz transmission spectrum of the structure is in a decreasing trend along with the increase of the concentration of a carbendazim solution, data is preprocessed through multiple scatter correction (MSC), feature extraction is performed by adopting an iterative information variable reservation (IRIV) method, and a quantitative detection model of the carbendazim terahertz transmission spectrum is established based on an LS-SVM (least squares support vector machine). The invention provides a novel approach for quantitative detection of carbendazim, and experimental results show that the RP of the model reaches 0.9825, the RMSEP is 0.2001, and the detection limit LOD is 0.672 mu g / ml. The invention provides a new method for rapid, lossless and high-sensitivity detection of carbendazim in food.

The invention relates to the field of water quality detection, in particular to a method, system and equipment for detecting solution impurities through a water quality detector and a medium. The method comprises the following steps: acquiring spectral data of a to-be-detected ion solution; a correction observation window is constructed on the spectral data according to the local concentration gradient and used for extracting local nonlinear features, and the local nonlinear features comprise absorbance gradient change features and fitting residual error anomaly features; establishing a mapping relation between the spectral data and a water quality detection result according to the local nonlinear characteristics in the correction observation window, and generating a correction coefficient through a scatter correction algorithm; correcting the spectral data according to the mapping relation and the correction coefficient; and identifying impurity components in the ion solution based on the corrected spectral data. The stable mapping relation between the absorbance and the impurity concentration can still be maintained in a strong ion interference environment, so that the measurement result can keep reliable precision even in a high-concentration ion intensity environment.

The invention relates to the technical field of image focus detection, in particular to a fiber endoscope image focus detection method and system, and the method comprises the following steps: providing a data quality guide interface; capturing narrow-band light images of the endoscope away from a first preset position and a second preset position of the intestinal wall in the axial movement process, calculating brightness gain values of a blue light channel and a green light channel, calculating an asymmetric scattering correction coefficient, and updating indication of color information calibration integrity; tracking the pixel moving speed of the interferent in the view in the propulsion operation process, calculating the relative depth of the interferent, and updating the indication of the validity of the depth data of the interference layer; identifying the image area which is not shielded by the interferent, and updating the indication of the information coverage degree of the background area; triggering an image synthesis operation; performing color compensation on the image information of the image area which is stored in the background canvas and is not shielded by the interferent; reconstructing a clear image; and performing focus detection on the clear image. The method improves the accuracy of focus detection.

The invention discloses a method for detecting the nitrogen content of organ tissues of different varieties of oilseed rapes based on visible near-infrared spectroscopy, which comprises the following steps: firstly, collecting oilseed rape leaf, shell, stalk and root system samples under the conditions of multiple varieties and multiple nitrogen fertilizer levels, and acquiring spectral data within the range of 430-2500nm by using a visible near-infrared spectroscopy; and a Kjeldahl method is synchronously adopted to measure the real nitrogen content as a reference value. Preprocessing the spectral data, including de-noising, standard normal variable transformation, multivariate scatter correction and derivative processing, so as to weaken the influence of scattering and baseline drift; characteristic wavelengths related to the nitrogen content are screened through stepwise regression, variable projection importance, competitive self-adaptive reweighted sampling, a continuous projection algorithm and other methods, and a random forest model, a support vector machine model, a partial least squares discriminant analysis model, a partial least squares regression model, a support vector regression model, an XGBoost model and other models are combined. And respectively constructing a classification identification model and a regression prediction model.

Aiming at the problems of low efficiency, strong subjectivity, insufficient modeling ability, complex hyperspectral data noise, weak waxiness spectrum difference and the like of a traditional rice grain waxiness discrimination method, the invention provides a hyperspectral rice grain waxiness discrimination method based on multi-branch collaborative modeling. The method comprises the following steps: S1, acquiring glutinous and non-glutinous rice grain images by using a hyperspectral imaging system to obtain spectral data; s2, preprocessing spectral data through a combined method of SG smoothing, an asymmetric weighted penalty least square method and multivariate scatter correction to reduce noise and interference; s3, constructing a multi-branch modeling architecture which comprises a CNN local feature extraction module, an SRU spectrum sequence modeling module and a HorNet global high-order modeling module, and outputting a discrimination result through a classifier after multi-branch features are subjected to fusion and pooling attention weighting; and S4, evaluating the performance. According to the method, high-precision lossless discrimination of the waxiness is realized, and a technical support is provided for germplasm screening and quality evaluation in rice breeding.

A method for training an algorithm for machine learning for a correction of recordings obtained by an imaging apparatus. A number of output images are recorded. Moreover a smaller number of first, high-quality scattered radiation images are simulated from the output images. A corresponding number of second, low-quality scattered radiation images is further simulated, wherein the simulation is undertaken with a number of photons reduced by at least an order of magnitude. The algorithm is trained with the second, low-quality scattered radiation images as input data and the first, high-quality scattered radiation images as output data.

The invention discloses an accurate X-ray radiation dose measurement system based on artificial intelligence, which comprises the following steps: acquiring CT image data of a patient and pulse irradiation parameters of radiotherapy equipment through terminal equipment, and calculating a patient specific anatomical structure dynamic influence factor set, a pulse waveform time domain characteristic factor and a tissue interface scattering correction factor; all the parameters are substituted into a dose measurement formula to obtain an accurate X-ray radiation dose value; through a deep fusion image recognition technology (such as a high-resolution anatomical structure, tissue density gradient and heterogeneous region distribution provided by CT / MRI), a secondary particle scattering effect of a junction region of a tumor and a normal tissue can be accurately represented; and dynamic anatomical information (such as respiratory movement and organ displacement) in the image is combined to correct the time characteristic (such as dynamic change under an instantaneous high dose rate) of the pulse radiation field in real time, so that the dose calculation accuracy of a fast algorithm in a complex scene is remarkably improved.

The invention discloses an image processing system and method suitable for a turbid water area, and belongs to the technical field of image processing. The system comprises an underwater camera component for collecting original image data of a turbid water area in real time and an embedded image processing unit. The embedded image processing unit comprises a preprocessing module used for carrying out noise suppression and image calibration processing on original image data, and a physical parameter estimation module used for estimating environment background light intensity of a current water area, a water body attenuation coefficient and a distance map of an object in a scene and calculating initial transmissivity. The scattering correction module is used for removing back scattering and preliminarily compensating a color channel based on a physical imaging model in combination with physical parameters, and the deep learning enhancement module is used for improving image local contrast, adjusting brightness and finely correcting colors by adopting a lightweight deep convolutional neural network. According to the image processing system and method suitable for the turbid water area, the image definition and color of the turbid water area can be improved, the real-time performance is high, and the system and method are suitable for different water area environments.

The invention relates to a 1D-CNN (1D-Convolutional Neural Network)-based dual-band fused hyperspectral flaxseed incomplete grain identification method, which is characterized in that sample data of moldy grains, damaged grains, thermal damaged grains, germinated grains and normal grains are synchronously acquired through a visible-near infrared (400-1000nm) and short-wave infrared (1000-2000nm) dual-band hyperspectral imaging system; after black and white board correction and region-of-interest extraction, seamless splicing of dual-band spectral data is realized by adopting a linear interpolation technology, then data quality is optimized by preprocessing methods such as multivariate scatter correction and standard normal transformation, and finally, deep features are extracted by utilizing a 1D-CNN deep learning model with a three-layer convolution structure. According to the technology, the limitation of traditional single-band detection is broken through, the identification accuracy of the flaxseed imperfect grains is improved to 97.50%, and compared with a single-band detection method, the identification accuracy is remarkably improved by 3-5%.

Machine learning based systems and methods for improving attenuation and scatter correction and for estimating organ volume in medical images are disclosed. In some embodiments, a co-modality image, a non-attenuated corrected nuclear image, and an x-ray image are received. A first machine learning process is applied to the co-modality image to generate location data identifying feature locations. Further, a second machine learning process is applied to the non-attenuated corrected nuclear image, the x-ray image, and the location data to generate a segmentation mask. In addition, at least a third machine learning process is applied to the segmentation mask, the co-modality image, the non-attenuated corrected nuclear image, and the x-ray image to generate a plurality of synthetic images. Moreover, at least a fourth machine learning process is applied to the plurality of synthetic images to generate a final synthetic image.

The invention relates to a cross scatter correction method and device, computer equipment, a computer readable storage medium and a computer program product, relates to the technical field of medical images, and can improve the accuracy of cross scatter correction. The method is applied to a double-source or multi-source CT imaging system, the double-source or multi-source CT imaging system comprises at least two detectors, each detector comprises a plurality of channels, and the method comprises the following steps: acquiring a signal intensity hopping relationship corresponding to the double-source or multi-source CT imaging system; the signal intensity hopping relationship is determined according to the detection signal intensity of each channel of the plurality of detectors, and the detection signal intensity is obtained by scanning different motifs and / or the same motif at different positions by the detectors; obtaining scattering estimation information obtained by scanning the to-be-measured object by the dual-source or multi-source CT imaging system; and correcting the scattering estimation information according to the signal intensity hopping relationship.

The invention relates to the technical field of cone-beam computed tomography, and discloses a scattering correction method and system for cone-beam computed tomography. The method comprises the following steps: acquiring original projection data and main scattering data acquired by a first flat panel detector; wherein the first flat panel detector is arranged in the emission direction of the radiation source; acquiring secondary scattering data acquired by a second flat panel detector; wherein the second flat panel detector is arranged in the scattering direction of the radiation source; determining target projection data based on the original projection data, the main scattering data and the auxiliary scattering data; the target projection data is used for cone beam computed tomography imaging. According to the method, scattering correction can be completed on the premise of not depending on special hardware, not additionally increasing radiation dose and not losing imaging view, and the imaging precision can be effectively improved.

A method of performing scatter correction on an X-ray image is disclosed. The method comprises obtaining an input image for processing based on a source X-ray image of a sample acquired using an X-ray detector, and determining a model of the sample based on the input image. The model is then evaluated by computing, based on the model, simulated X-ray image data and evaluating the simulated image data against the input image to determine whether a convergence criterion is fulfilled. An updated model of the sample is generated if the convergence criterion is not fulfilled. The model evaluating step is repeated based on one or more successive updated models until the convergence criterion is fulfilled in a final iteration, and scatter correction is then performed on the source X-ray image using simulated image data computed during the final iteration.

The invention provides an infrared spectrum-based material component analysis and defect detection evaluation method, which is characterized by comprising the following specific steps of: setting a scanning range by virtue of an instrument, and collecting infrared spectrum data in a sample; a self-adaptive iteration reweighted penalty least square method is sequentially adopted to carry out baseline correction, wavelet transform noise suppression and multivariate scatter correction standardization to process spectral data, a multi-dimensional characteristic parameter system is combined with a machine learning model, accurate identification of defect types and degrees is achieved, and the limitation that a traditional method can only carry out qualitative judgment is broken through; and through a component-defect linkage evaluation model, quantitative judgment of the overall performance of the material is realized, and the comprehensive requirement of an industrial scene for quality control is met.

The present application relates to the field of medical imaging technology, and in particular to a multi-energy CT spectral imaging method and apparatus, wherein the method comprises: obtaining scanning object parameters of a target object; constructing a target optimization criterion, and establishing a high- and low-energy spectral projection model based on the target optimization criterion combined with the scanning object parameters to obtain optimal spatiotemporal filter parameters; using the optimal spatiotemporal filter parameters to produce corresponding spatiotemporal filters to collect high- and low-energy projection data, and using the low-frequency properties of scattering to perform scatter correction on the high- and low-energy projection data to obtain descattered projection data, and performing base material decomposition in the projection domain or performing descattered image reconstruction to perform base material decomposition in the image domain to complete the multi-energy CT spectral imaging task. The present application can select appropriate spatiotemporal filter parameters based on a specific material decomposition task and apply them to the production of spatiotemporal filters, effectively improving the performance of multi-energy CT spectral imaging.

The invention provides an image correction method and a double-projection imaging system, and relates to the technical field of images, in particular to the technical field of image correction. According to the method, a first projection image and a second projection image of a target object under a target projection angle can be acquired, scattering correction is performed on the first projection image, and a corrected image under the target projection angle is determined based on the first projection image after scattering correction and the second projection image of the target object under the target projection angle. Wherein the target projection angle is a projection angle within a target projection angle range, the first projection image comprises a shadow area formed after shielding of the first BSA, and the target projection angle range is a projection angle range where the first projection group and the second projection group are overlapped. According to the method, the phenomenon of shielding artifacts of the image in the reconstruction process can be effectively reduced.

SOLUTION: An X-ray imaging apparatus and associated method are provided that receive measured projection data from a wide aperture scan of a wide body-axis direction and a narrow aperture scan of a narrow body-axis direction within the wide body-axis direction and determine an estimated scatter in the wide body-axis direction using an optimized scatter estimation technique. The optimized scattering evaluation technique is based on the difference between the measured scattering in the narrow body-axis direction and the evaluated scattering in the narrow body-axis direction. The kernel-based scatter evaluation / scatter correction approach can be fitted to minimize scatter differences in a narrow body-axis direction. Thereafter, the fitted (optimized) kernel-based scatter evaluation / correction is applied to the wide body-axis direction region. Optimization may occur in the projection data domain or in the reconstruction domain. An iterative process is also utilized.SELECTED DRAWING: None

Devices, methods, and calibration methods for estimating the concentration of an analyte are provided. The device for estimating the concentration of an analyte includes: an optical sensor configured to emit light toward an object and receive light reflected from the object; and a processor configured to: obtain a ratio of an absorption coefficient to a scattering coefficient based on the received light, obtain a first absorption spectrum of the object based on the obtained ratio, obtain a second absorption spectrum by eliminating a scattering correction spectrum from the first absorption spectrum, the scattering correction spectrum corresponding to a non-linear change of the scattering coefficient according to the wavelength of the emitted light, and estimate the concentration of the analyte based on the second absorption spectrum.

The invention relates to the technical field of cosmetic component detection and analysis, and discloses a method for rapidly recognizing arbutin in cosmetics based on surface enhanced Raman spectroscopy, AgNPs sol is prepared by an ascorbic acid oxidation-reduction method, and an SERS sensing interface is constructed through aspartic acid electro-polymerization modification; for different dosage forms of cosmetics, a to-be-detected solution is obtained through methanol extraction and centrifugal filtration purification, the to-be-detected solution and AgNPs sol are mixed and adsorbed, then a Raman spectrum is collected, pretreatment is conducted through combination of multivariate scatter correction and standard orthogonal transformation, characteristic wavelengths are screened through a genetic algorithm-combined interval partial least square method, and detection is achieved through combination of qualitative and quantitative models. Through cooperation of sensing interface specific modification and a data processing technology, the arbutin signal recognition capability is enhanced, matrix interference is eliminated, rapid and accurate detection of arbutin in cosmetics is realized, and the effects of high sensitivity, high specificity and simple and convenient operation are achieved.

The application relates to the technical field of image processing, and discloses a PET image enhancement method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a PET image to be reconstructed and measurement data, wherein the measurement data refers to sinogram data formed after signal data collected by a PET scanner is subjected to attenuation and scattering correction; and finally, based on the PET image, the sinogram data, a trained convolutional neural network and an EM fidelity algorithm, the PET image is reconstructed to obtain a target PET image. According to the application, a convolutional neural network for denoising is first trained based on a diffusion model theory on a high-quality (a high-quality PET image of a conventional dose) image, and then based on the trained model, high-quality image reconstruction is performed under the guidance of the PET image to be reconstructed and an EM fidelity algorithm.

The invention discloses a peanut kernel producing area identification method based on a near infrared spectrum. The method comprises the following steps: (1) collecting near infrared diffuse reflection spectrum data of peanut kernel samples from different producing areas by using a portable near infrared spectrometer; (2) carrying out preprocessing on the spectral data by utilizing a multiple scatter correction (MSC) method and a Savitzky-Golay smoothing filter; (3) extracting identification information of the spectral data by using a fuzzy identification analysis method; and (4) classifying by using a K neighbor classifier. According to the method, linear identification information of the near infrared spectrum value domain space and the null space of the peanut kernels can be extracted, the K-nearest neighbor classifier is adopted for classification, and the peanut kernel producing area can be rapidly and accurately identified. The peanut kernel producing area identification method based on the near infrared spectrum can be used for accurately identifying the peanut kernel producing area.

The invention discloses a CBCT scattering correction method based on dual-domain interaction and multi-scale wavelet transform, and the method comprises the steps: inputting original scattering-containing CBCT projection data into a preliminary projection correction network, and obtaining preliminary de-scattering projection data; performing three-dimensional reconstruction on the two types of projection data to obtain an original scattering-containing image and a preliminary correction image; fusing the features through an image domain feature fusion network to generate a fusion correction image; performing forward projection on the fusion correction image to obtain reference projection data, and calculating a difference value between the reference projection data and the original projection data to obtain differential projection data; performing multi-scale two-dimensional discrete wavelet transform on the differential projection data, and optimizing coefficients; performing inverse transformation to obtain a refined scattering diagram; and subtracting the refined scattering image from the original projection data, and reconstructing to obtain a final non-scattering CBCT image. According to the method, depth interaction between the projection domain and the image domain is realized, signals are accurately separated in scattering estimation, and the image quality, the detail retention capability and the robustness of the algorithm are effectively improved.

The invention discloses a system for multispectral transmission image breast lesion segmentation, and belongs to the technical field of medical image processing and optical imaging. The system comprises an image preprocessing subsystem and a semi-supervised segmentation network subsystem. The preprocessing subsystem adopts modulation and demodulation, frame accumulation and a scattering correction technology based on spatial pyramid matching, so that the signal-to-noise ratio and the contrast ratio of an image are remarkably improved; the segmentation network subsystem proposes a multi-head consistency semi-supervised network model, integrates a multi-head decomposition module and a cross supervision module, and effectively utilizes unlabeled data to carry out uncertainty modeling and consistency constraint. Under the condition that only 20% of labeled data is used, the segmentation Dice coefficient on the self-built mammary gland phantom data set reaches 91.47% and is obviously superior to that of an existing method, and a complete solution from image enhancement to intelligent segmentation is provided for early diagnosis of the breast cancer.

In order to reduce a computing effort for correcting initial images from an imaging apparatus, a scatter model is used to simulate scattered-radiation images, and corresponding initial images are selected from the original initial images. These are used to train a neural network. The neural network may be used to calculate scattered-radiation single images that may be used to correct each individual initial image into a corrected initial image.

Systems, methods, and devices for generating a corrected image are provided. A first robotic arm may be configured to orient a source at a first pose and a second robotic arm may be configured to orient a detector at a plurality of second poses. An image dataset may be received from the detector at each of the plurality of second poses to yield a plurality of image datasets. The plurality of datasets may comprise an initial image having a scatter effect. The plurality of image datasets may be saved. A scatter correction may be determined and configured to correct the scatter effect. The correction may be applied to the initial image to correct the scatter effect.

Systems, methods, and devices for generating a corrected image are provided. A first robotic arm may be configured to orient a source at a first pose and a second robotic arm may be configured to orient a detector at a plurality of second poses. An image dataset may be received from the detector at each of the plurality of second poses to yield a plurality of image datasets. The plurality of datasets may comprise an initial image having a scatter effect. The plurality of image datasets may be saved. A scatter correction may be determined and configured to correct the scatter effect. The correction may be applied to the initial image to correct the scatter effect.

The invention relates to a cross scatter correction method and device and computer equipment, relates to the technical field of medical images, and can improve the accuracy of a cross scatter correction result. The method is applied to a double-source or multi-source CT imaging system, the double-source or multi-source CT imaging system comprises at least two detectors, and the method comprises the steps that respective detection signal curves of the detectors are obtained, and the detection signal curves are determined based on detection signals collected by the detectors for a scanned object; for each detector, determining a first convolution region corresponding to a detection signal curve according to boundary position features of the detection signal curve of the detector, and determining scattering estimation information corresponding to the detector according to a convolution result of the first convolution region and a preset convolution kernel; and determining a cross scattering correction result of each detector according to the scattering estimation information corresponding to each detector.

The present invention provides an oil-in-water spectral analysis and data transmission system based on AI technology, which relates to the field of oil quality detection technology. The system includes a spectral detection module for emitting multi-wavelength light to a water sample and collecting optical signals; an AI processing module connected to the spectral detection module for performing spectral analysis based on the optical signals, and using an association algorithm group including a light absorption-scattering coupling model, a self-attention feature enhancement model, and a multimodal classification prediction model to output oil concentration and distribution information; a data recording module connected to the AI ​​processing module for storing detection data; an auxiliary function module connected to the data recording module for transmitting detection data, issuing voice prompts, and powering the system; light scattering interference is eliminated through a scattering correction unit and a coupling model, and the weight of oil characteristic wavelengths is automatically enhanced through a self-attention mechanism to improve the accuracy of mixed oil identification. At the same time, wireless charging, real-time cloud transmission, voice alarm functions, and oil-in-water monitoring are combined.

The present invention is titled "Scatter Correction for Computed Tomography Imaging". The present invention provides a system and method for scatter correction of X-ray images. A partially scatter-free image acquired using a pore plate can be used to correct the scatter image of an object. The plate is positioned between the object and the radiation detector and includes grid-like pores. The original X-rays pass through these pores and the scattered X-rays can be blocked by the pore plate. The pore plate can be moved to different positions, thereby allowing the acquisition of partially scatter-free images at each position of the pore plate. A completely scatter-free image can be generated by combining the partially scatter-free images. These scatter images and scatter-free images can also be used to train a scatter correction model.

The application provides a scattering correction method and device, a digital device and a computer readable storage medium. The method comprises: obtaining a down-sampling response line based on detection data; obtaining an estimated target function corresponding to each down-sampling response line by using a matrix estimation method based on a maximum likelihood-expectation maximization iterative algorithm; solving the estimated target function to obtain a number of scattering coincidence events corresponding to each down-sampling response line; performing up-sampling processing on the down-sampling response line to obtain an up-sampling response line; and calculating the number of scattering coincidence events corresponding to each up-sampling response line. When the embodiment of the application performs scattering correction, it does not depend on activity images and attenuation images, and the method does not require a hyperparameter, so that the hyperparameter no longer needs to be set in a determined range, and the method is simple and accurate.