187 results about "Level set method" patented technology

The invention provides distributed level cluster method and system of geographic information service, wherein the cluster system comprises a plurality of load equalizers, a plurality of secondary load equalizers and a plurality of service nodes. Each load equalizer is provided with at least one stage equalizer secondary node; the equalizer secondary node comprises at least one secondary load equalizer; and the secondary load equalizer of the equalizer secondary node is an equalizer middle secondary node or an equalizer leaf node belonging to a root load equalizer. When the secondary load equalizer is the equalizer middle secondary node, the secondary load equalizer has a next stage equalizer secondary node; when the secondary load equalizer is the equalizer leaf node, the secondary load equalizer has a leaf service node secondary node, and the leaf service node secondary node comprises at least one service node. The invention can reduce the storage burden of the cluster system, solve the problem of low reliability of the cluster system, which is caused by the storage burden, realize cluster redundancy and increase the efficiency and the reliability of the whole cluster system and does not need to increase additional costs.

A dynamic thresholding level set method combines two optimization processes, i.e., a level set segmentation and an optimal threshold calculation in a local histogram, into one process that involves a structure called a “propagating shell.” The propagating shell is a mobile 3-dimensional shell structure with a thickness that encompasses the boundary of an object, the boundary between two objects or the boundary between an object and a background. Because the local optimal threshold tends to shift to a value of a small region in a histogram, the shift can drive the propagating shell to an object boundary by pushing or pulling the propagating shell. The segmentation process is an optimizing process to find a balanced histogram with minimal threshold shift. When the histogram in the propagating shell is balanced, the optimal threshold becomes stable, and the propagating shell reaches a convergence location, i.e., an object boundary. This method can be applied to computer-aided organ and tumor volumetrics.

The invention belongs to the application field of computer analysis technology of medical image, and relates to a pulmonary nodule three-dimensional segmentation and feature extraction method and a system thereof based on chest CT. By adopting a level set method based on a three-dimensional space, pulmonary nodule is divided within a three-dimensional interest area, and the feature of the nodule can be extracted by combining the characteristics of a bounding curved surface and the interest area of the pulmonary nodule. The invention comprises a lung area segmentation module, a three-dimensional interest area extracting module, a pulmonary nodule three-dimensional segmentation module, a nodule feature extraction and quantification module and an output module. The invention fully utilizes the three-dimensional space information of HRCT image, thus reducing the interference of normal tissues around the nodule for segmentation and feature extraction, and improving the accuracy of feature extraction.

A method for graphically simulating stable but non-dissipative water in real-time includes steps for modeling multiphase materials with grid of nodes, suppressing numerical dissipation for getting rid of loss of mass of material, and suppressing numerical diffusion for reducing dampening of the fluid motion of materials in liquid phase. The step of modeling multiphase materials includes steps of describing liquid and gas with a set of nonlinear partial differential equations, representing the liquid-gas interface as an implicit surface, and determining properties of the materials, from the information about the liquid-gas interface, including the surface curvature and the surface tension. The set of nonlinear partial differential equations includes multiphase incompressible Navier-Stokes equations. The step of representing the liquid-gas interface includes a level set method.

The invention discloses a three-dimensional medical image segmentation method. The method comprises the following steps: firstly performing three-dimensional superposition reconstruction on sequential two-dimensional medical image, and then performing Gaussian filter smoothing in a three-dimensional space; carrying out three-dimensional space contraction computation on the three-dimensional medical image, and calculating and recording the contraction orders and the energy characteristics of every three-dimensional pixel point; performing three-dimensional space extension on the three-dimensional medical image in a plurality of areas by a level set method; and finally extracting and segmenting the three-dimensional pixel points in the three-dimensional image according to information of interested areas, and respectively generating a plurality of three-dimensional images of the interested areas. The method has positive effects of strong timeliness, high computing efficiency, high segmentation accuracy, and clear and reliable image local features, and can achieve simultaneous segmentation of a plurality of areas.

A Bayesian formulation for coupled surface evolutions in level set methods and application to the segmentation of the prostate and the bladder in CT images are disclosed. A Bayesian framework imposing a shape constraint on the prostate is also disclosed, while coupling its shape extraction with that of the bladder. Constraining the segmentation process improves the extraction of both organs' shapes.

The invention provides a blood vessel extraction method. The method comprises steps of reading angiography data; adopting a region growing method on the data to obtain a first result; adopting a level set method to obtain a second result; performing morphological combination according to the first result and the second result; and performing blood vessel extraction of the angiography data. According to the invention, advantages of the region growing method based on the threshold and the gradient and the level set method based on the radius and the gradient are effectively combined, and region growing of bones and blood vessels are flexibly utilized to assist the blood vessel extraction, so that the complete extraction of blood vessels can be achieved.

The invention discloses a level set based method for edge detection of SAR images, which mainly overcomes the defect that the existing methods for edge detection of SAR images can not well suppress the impacts of speckle noises. The method comprises the following steps: (1) inputting the SAR images and carrying out median filtering and brightness transform in sequence to obtain the enhanced images; (2) manually selecting initialized seed points on the enhanced images and beginning area growth; (3) carrying out morphological close operation and removal operation on the area growth result in sequence to obtain the edge profile of the smoothened destination area; (4) obtaining a level set evolved initialized zero level set according to the edge profile of the destination area, which is obtained through area growth; and (5) carrying out level set evolution until the preset iterative evolution frequency is met to obtain the final edge profile of the destination area. The method can effectively overcome the impacts of speckle noises of the SAR images on edge detection, is accurate in edge detection and can be used for edge detection of the SAR images of airport runways and roads.

The invention relates to a Mura defect level judgment method and device based on deep learning. The method comprises the following steps that: setting a Mura defect level tag, and enabling a Mura defect image sample to correspond to the Mura defect level tag one by one according to a preset level; then, taking the Mura defect image sample as an input neuron to enter a network, carrying out training after feature extraction is carried out, and outputting a training result after the training result passes through a classifier, and obtaining a Mura defect image corresponding to the Mura defect level tag; and finally, outputting the Mura defect image test sample through feature extraction after the Mura defect image test sample passes through the classifier, and comparing with the Mura defectimage sample features with a Mura defect level tag to obtain the Mura defect level corresponding to classification. By use of the method, the Mura defect of a panel can be subjected to accurate and detailed judgment and output, the limitation of a traditional algorithm can be successfully evaded, and meanwhile, human cost can be greatly lowered.

The invention relates to a hierarchical FPGA layout and wiring method based on a multi-level method and empowerment hypergraph. A mathematical model of a logical netlist in a hierarchical FPGA layout and wiring process is constructed by using empowerment hypergraph; circuit logic units are represented as nodes in the empowerment hypergraph; wiring among the circuit units is expressed as overrides in the empowerment hypergraph. The multi-level method is adopted as a hierarchical FPGA layout and wiring method to effectively meet the higher requirements of VLSI on the operating efficiency and processing capacity. The method can gradually reduce the scale of problems and the time complexity of methods by bottom up cluster stage and also can process details of problems level by level, increasing solution accuracy of methods by means of top-down projection optimization so as to be naturally combined with hierarchical features of researched objects. By means of the hierarchical FPGA layout and wiring method based on the multi-level method and empowerment hypergraph, the time complexity of FPGA physical design can be effectively reduced and the design capability and performance of FPGA are increased.

The invention discloses an active contour tracing method based on superpixel. The method comprises the following steps of carrying out the superpixel segmentation on a training image to obtain a training sample pool of a target and a background, adopting a measuring learning method to obtain a distance measuring projection matrix according to a training sample, establishing a judgment-type appearance model, carrying out the superpiexel segmentation on each frame of test image of a sequence image, obtaining a confidence diagram corresponding to the test image according to the established judgment-type appearance model, obtaining a speed field of the test image, substituting the speed field into an evolution equation of a level set method, and obtaining a contour trace result of the test image. Compared with the prior art, the contour evolution efficiency of each frame of test image can be improved, and the tracing accuracy and tracing efficiency of the sequence image can be improved.

The present invention relates to a method for estimating a measure of a penumbra size of a penumbra in biological tissue wherein first and second images obtained respectively by perfusion weighted imaging (PWI) and diffusion weighted imaging (DWI) are analyzed, and wherein the analysis of the first image includes application of a level set method, and the analysis of the second image includes application of a grayscale morphological operation. In a further embodiment of the invention, a connected component labeling algorithm maybe applied on any one of the first and second image. The invention further relates to a system, a computer program product and use of a corresponding method.

The invention relates to the technical field of image processing and discloses a method for classifying and identifying visible components of microscopic excrement examination images based on machine vision. Firstly, a level set method is applied to conduct image segmentation on microscopic excrement examination images, then primary classification based on morphological characteristics and verifying identification based on an HOG + VSM are sequentially performed according to shapes and outlines of the visible components obtained through segmentation, and finally high-precision visible component classification and identification results are rapidly obtained for timely consulting of patients. In addition, the method further has the advantages of being high image segmentation precision, high in processing speed, good in user experience and the like and is convenient to apply actually and popularize.

The invention discloses a narrowband constraint-based local segmentation method for a geometric activity contour model and belongs to the field of image processing. The method comprises the followingsteps of constructing a narrow-band range based on particle swarm threshold segmentation and morphology expansion; constructing an energy function of fusing a global energy item and a local energy item by a self-adaptive coefficient; and solving the energy function by adopting the level set method. According to the invention, the local segmentation calculation region is optimized, and the self-adaptation coefficient of the global energy term and the local energy term is achieved. Therefore, the efficiency and the accuracy of the local segmentation of a grayscale uneven image are improved. Theproblems that the narrow-band control is unstable and the curve evolution precision is insufficient during the local segmentation of the grayscale uneven image are solved. The problem that the energyfunction model fitted by the global energy item and the local energy item cannot be rapidly and accurately segmented is also solved.

The invention discloses a workpiece detecting and sorting device and method based on a visual robot. The device comprises a transmission device, a robot and a computer. The transmission device collects a workpiece image, analysis is carried out through a computer, and the robot grabs a damaged workpiece. Compared with the prior art, the workpiece detecting and sorting device and method based on the visual robot is characterized in that Haar-like features are adopted to establish a weak classifier based on gray level changes, an AdaBoost algorithm is used for forming a strong classifier with high accuracy, cascading of the strong classifier is then carried out, a region of interest (ROI) containing a target object is extracted in the image, and the better detection accuracy and the real-time effect are obtained; a defect image is obtained in the target object, and a level set method is used for obtaining a defect ROI to form a training sample; and a convolutional neural network (CNN) isintroduced as a training model, and the accuracy of defect classification is improved.

The invention discloses a remote-sensing image road network extract method based on an improved C-V mode, comprising the following steps: (1) pre-processing initial remote-sensing images; (2) selecting seed points and segmenting a first road network subimage; (3) using the level set method of the improved C-V model to extract a road network area; (4) using the method of morphologic detailing to extract the center line of the road network; (5) segmenting the next road network subimage according to the subimage position criterion rule to automatically obtain the road network initial curve according to threshold segmentation and the morphologic method. The method of the invention can be employed to effectively extract the road network from the remote-sensing image, upgrade urban road network in real time and effectively assist decision-makers in formulating traffic planning.

The invention relates to a demoscopy focus automatic segmentation method in a medical image processing technology. The method includes the following steps: detection and extraction of a focus area; preliminary segmentation of a focus area and a background area in an image and extraction of the shape, position and contour of the focus area; segmentation of a mobile contour model; and construction of an initial curve of the mobile contour model according to the contour of the focus area and establishment of a corresponding energy functional. According to a level set method and a curve evolution theory, a curve capable of meeting minimum of the energy functional is obtained through partial-differential solution and the focus area is segmented automatically from the image. The method performs automatic detection and segmentation on the focus area according to the difference of the demoscopy focus area and the surrounding area; the method is accurate and high in efficiency in focus segmentation, great in practicality, dispensed with manual interruption and capable of assisting dermatology doctors to perform more accurate focus analysis and diagnosis.

The invention discloses a multiscale level set image segmenting method based on kernel fuzzy clustering. The method includes step 1 adopting a divide marking method to calculate the gray average of small areas; 2 adopting the gray average to initialize a membership matrix, conducting kernel fuzzy clustering to obtain the initial contour of the interested area; 3 designing an edge constraint stop item of the multiscale level set; 4 conducting iteration evolution to segment an image. The method overcomes the shortcoming that over-segmenting is easy to cause for the divide based on edge information, and edge segmenting missing is easy to cause for the C-V model level set method based on area information for the images with unclear edges and poor contrast. The edge information and the area information are effectively blended by adopting the kernel fuzzy clustering method, the multiscale edge constraint stop item is added, the re-initialization is eliminated, the segmenting accuracy is improved, and the real-time performance of the algorithm is ensured.

The invention discloses a breast tumor partition method. The breast tumor partition method includes the steps of building a coupled framework of classifications and biased field correcting of breast tissue nuclear magnetic resonance images, enhancing the breast areas and the peripheral areas, and partitioning the breast tumor images in cooperation with the shape prior. By means of the breast tumor partition method, biased field information is fused into classification models, the biased field information and the classification models are combined to be the unified framework, and the classifications and a corrected biased field of the breast tumor nuclear magnetic resonance images are solved with the fast energy minimization method at the same time, and use information of each other in the model evolution process for finally achieving accurate solving of the classifications and the corrected biased field; the shapes of blood vessels and tumors are analyzed, differences of the blood vessels and the tumors in shape are caught, the shape prior is built through parameters such as the characteristic values and the characteristic vectors, and the level set driving force based on the shapes is built and combined with a level set method based on local information, so that a level set overcomes the interference of a tubular structure during evolution and only captures the breast tumor area.

The invention discloses a method for predicting traffic flow extracted by an improved C-V model-based remote sensing image road network, which comprises the following steps of: (1) preprocessing an original remote sensing image; (2) selecting a seed point and segmenting a first road network sub-image; (3) extracting a road network area by an improved C-V model-based level set method; (4) extracting a road network central line by a morphological thinning method; (5) segmenting a next road network sub-image by using a sub-image position decision rule and automatically acquiring a road network initial curve in the road network sub-images by a threshold segmentation and morphological method; (6) vectorizing a road network; and (7) predicting the traffic flow. By integrating technology such as remote sensing, geographic information system (GIS), image identification, traffic planning and the like, an urban road network can be more accurately, efficiently and cheaply updated in real time, the traffic flow prediction cost is lower, the traffic flow prediction accuracy is higher and the traffic flow prediction period is shorter, so that decision makers can be effectively assisted in making traffic planning decisions.

The invention discloses a local Gamma fitting-based active contour synthetic aperture radar (SAR) image segmentation method. The SAR image segmentation precision is low in the prior art. The method comprises the following steps of: representing the local structure of each pixel in an original SAR image in a certain neighborhood, regarding a maximum likelihood as a regional separation criterion, and defining a local fitting energy term; then integrating all pixel points in the defined region to acquire an overall optimized energy functional; and finally, describing and solving an energy minimization process by adopting a curve evolution theory and level set method-based geometrical active contour model so as to realize effective segmentation of the SAR image. The method has strong speckle noise inhibiting capability, can realize effective segmentation of the SAR image, and particularly has accurate segmentation effect on complex boundaries such as deep indentation and the like.