245results about How to "Fast split" patented technology

The invention provides an unsupervised domain-adaptive brain tumor semantic segmentation method based on deep adversarial learning. The method comprises the steps of deep coding-decoding full-convolution network segmentation system model setup, domain discriminator network model setup, segmentation system pre-training and parameter optimization, adversarial training and target domain feature extractor parameter optimization and target domain MRI brain tumor automatic semantic segmentation. According to the method, high-level semantic features and low-level detailed features are utilized to jointly predict pixel tags by the adoption of a deep coding-decoding full-convolution network modeling segmentation system, a domain discriminator network is adopted to guide a segmentation model to learn domain-invariable features and a strong generalization segmentation function through adversarial learning, a data distribution difference between a source domain and a target domain is minimized indirectly, and a learned segmentation system has the same segmentation precision in the target domain as in the source domain. Therefore, the cross-domain generalization performance of the MRI brain tumor full-automatic semantic segmentation method is improved, and unsupervised cross-domain adaptive MRI brain tumor precise segmentation is realized.

The invention discloses a target identification and capture positioning method based on deep learning, and belongs to the field of machine vision. The method comprises the following steps that: firstly, utilizing a Kinect camera to collect the depth and the color image of a scene; then, using a Faster R-CNN (Regions with Convolutional Neural Network features) deep learning algorithm to identify ascene target; according to an identified category, selecting a captured target area as the input of a GrabCut image segmentation algorithm; through image segmentation, obtaining the outline of the target so as to obtain the specific position of the target as the input of a cascade neural network for carrying out optimal capture position detection; and finally, obtaining the capture position and the capture gesture of a mechanical arm. Through the method, the instantaneity, the accuracy and the intelligence of target identification and positioning can be improved.

The invention provides an MRI (Magnetic Resonance Imaging) brain tumor localization and intratumoral segmentation method based on a deep cascaded convolution network, which comprises the steps of building a deep cascaded convolution network segmentation model; performing model training and parameter optimization; and carrying out fast localization and intratumoral segmentation on a multi-modal MRIbrain tumor. According to the MRI brain tumor localization and intratumoral segmentation method provided by the invention based on the deep cascaded convolution network, a deep cascaded hybrid neuralnetwork formed by a full convolution neural network and a classified convolution neural network is constructed, the segmentation process is divided into a complete tumor region localization phase andan intratumoral sub-region localization phase, and hierarchical MRI brain tumor fast and accurate localization and intratumoral sub-region segmentation are realized. Firstly, the complete tumor region is localized from an MRI image by adopting a full convolution network method, and then the complete tumor is further divided into an edema region, a non-enhanced tumor region, an enhanced tumor region and a necrosis region by adopting an image classification method, and accurate localization for the multi-modal MRI brain tumor and fast and accurate segmentation for the intratumoral sub-regions are realized.

The invention discloses a segmentation method for an image with deep image information, which is high in segmentation accuracy, and capable of still achieving a good segmentation effect under the condition of a very similar front background. The segmentation method comprises the steps of: (1) obtaining the image with deep image information via Kinect; (2) performing probability modelling on the colour information of the front background and the deep image information; (3) performing parameter estimation on the model by EM (Expectation-Maximization) algorithm; and (4) performing segmentation after the first image segmentation on the image by adopting an image segmentation algorithm, wherein an energy function is formula shown in the abstract, and according to the energy function, the smallest segmentation is evaluated by a maximum flow algorithm, so as to obtain the final segmentation object.

The invention discloses an image segmentation method for a dynamic contrast-enhanced mammary gland MRI sequence, pertaining to the field of magnetic resonance image processing techniques, which is characterized by comprising the following steps: a three-dimensional magnetic resonance image sequence of the section of the mammary gland is put into a computer; the image is divided into two parts including a mammary gland-air interface and a mammary gland-chest interface; a breast-air boundary is obtained by a splitting transaction in which a dynamic threshold controls the regional growth; an initial profile of the mammary gland and the chest is obtained in the same way, the complex profile of the breast and the chest is obtained with a method of controlling a level set; a three-dimensional magnetic resonance image sequence of a point-in-time is obtained by split jointing the segmentation results and taken as an initial position of the next group three-dimensional image segmentation. The image segmentation method of the invention increases the segmentation speed, solves the problem that a level set algorithm can not easily determine the initial profile and the velocity function and realizes an automatic segmentation of the complex dynamic contrast-enhanced mammary-gland magnetic resonance image with plenty of data.

The invention relates to a MRI brain tumor image segmentation method based on an optimized U-net network model. The method comprises the following steps: 101, preprocessing the acquired multimodal MRIbrain tumor image data; 102, inputting the preprocessed multimodal MRI brain tumor image data into the trained U-Net network model; 103, acquiring multi-modality MRI brain tumor image segmentation data output by a U-NET network model, wherein the multimodal MRI brain tumor image segmentation data output from the U-NET network model can preserve the image edge information to generate a complete segmented image feature map. The image segmentation method provided by the invention can not only preserve the image edge information and generate a complete characteristic map, but also improve the accuracy of image segmentation.

The invention discloses a retina eyeground image segmentation method based on a depth full convolutional neural network. The retina eyeground image segmentation method includes the following steps of:selecting a training set and a test set, extracting retina eyeground images to obtain optic disk positioning area images, and performing blood vessel removal operation on the optic disk positioning area images; constructing the depth full convolutional neural network, taking the optic disk positioning area images as the input of the depth full convolutional neural network, and performing the training of an optic disk segmentation model on the training set based on trained weight parameters as initial values to fine tune model parameters, and performing fine tuning on parameters of an optic cup segmentation model based on trained optic disk segmentation model parameters; and performing optic cup and optic disk segmentation on the test set by utilizing a trained optic cup segmentation model, performing ellipse fitting on final segmentation results, calculating a vertical cup-disk ratio according to optic cup and optic disk segmentation boundaries, and taking a cup-disk ratio result as important basis for a glaucoma auxiliary diagnosis. The retina eyeground image segmentation method achieves optic disk and optic cup automatic segmentation of the retina eyeground images, has high precision and fast speed.

The invention provides a cloud atlas segmentation method based on a FCN and a CNN, and belongs to the field of image segmentation of computer vision. The method is characterized in that firstly, a near neighborhood of each pixel point in a cloud atlas is correspondingly clustered through an ultra-pixel, the cloud atlas is input to fully convolutional networks FCN32s and FCN8s with different step lengths, and pre-segmentation results of the cloud atlas are realized; in a FCN32s result map, a black area is a part of a non-cloud area in the cloud atlas, and in a FCN8s result map, a white area is a part of a cloud area in the cloud atlas; and remaining uncertain areas are grey areas which need to be determined through a deep convolutional neural network (CNN), key pixels in an ultra-pixel area need to be selected to represent characterizes of the ultra-pixel area, and whether the characteristics of the pixels are cloud or non-cloud is determined through the CNN network. According to the method, the precision is equivalent to that of MR-CNN and SP-CNN, and the speed is increased by 880 times compared with MR-CNN and increased by 1.657 times compared with SP-CNN.

The invention discloses a building segmentation and contour line extraction method of ground laser point cloud data. The building segmentation and contour line extraction method comprises the steps of vertically projecting a point cloud, generating a two-dimensional grayscale image, segmenting a building point cloud by adopting an Otsu algorithm, carrying out principal component analysis, conducting best neighborhood calculation, extracting contour lines and the like. The building segmentation and contour line extraction method realizes the segmentation of the building point cloud from the original point clout quickly and accurately, and completes the extraction of the building contour line point cloud in a full-automatic manner.

A level set polarization SAR image segmentation method based on polarization characteristic decomposition, belonging to the radar remote sensing technology or the image processing technology. In the invention, a polarization characteristic vector v which is composed of three polarization characteristics: H, alpha and A is obtained by the polarization characteristic decomposition of each pixel point of the original polarization SAR image; the polarization characteristic vectors v of all the pixel points are combined into a polarization characteristic matrix omega so as to convert the segmentation problem of the polarization SAR image from data space to polarization characteristic vector space; and the condition that the characteristic vector definition is suitable for energy functional of the polarization SAR image segmentation is utilized and a level set method is adopted to realize the numerical value solution of partial differential equation, thus realizing the polarization SAR image segmentation. The method provided by the invention takes full use of the polarization information of the polarization SAR image; therefore, the image edge obtained by segmentation is relatively complete so that the local characteristic is maintained better, the robustness for noise is stronger, the stability of the arithmetic is higher and the segmentation result is accurate; and the invention reduces the complexity of data and can effectively improve the image segmentation speed.

The invention discloses an image segmentation method based on super pixel clustering. More specifically the image segmentation method includes the steps of: 1, segmenting images by using a Simple Linear Iterative Clustering (SLIC) algorithm, and generating super pixels; 2, improving a construction mode of a similarity matrix about the super pixels, and fusing a color characteristic and a texture characteristic through non-symmetry of the similarity matrix; 3, clustering the super pixels through an Affinity Propagation (AP) clustering algorithm based on the similarity matrix; and 4, reaching the purpose of image segmentation by adding spatial information of the super pixels and dividing a disconnected region into different types of super pixel groups by means of breadth-first traversal. The image segmentation method based on the super pixel clustering has good segmentation effect and a fast convergence speed. Target objects can be effectively segmented without arrangement of target quantity.

The invention provides a rapid 3D blood vessel boundary segmenting method and system. The system comprises a preprocessing module, a first coordinate transformation module which transforms an image into an image under a polar coordinate system, an image artifact identification module which determines an artifact in the image and generates an artifact mask image, a vessel cavity center locating module which determines a blood vessel cavity center in the original image, a second coordinate transformation module which transforms the determined image of the blood vessel cavity center into a new image under the polar coordinate system, and removes an artifact from the new image according to the artifact mask image, a vessel cavity boundary extracting module which generates an energy consumption image according to the artifact removed image and determines a blood vessel boundary of the image under the polar coordinate system, and an inverse coordinate transformation module which transforms the blood vessel boundary of the image under the polar coordinate system into a blood vessel boundary curve under the coordinate system of the original image. Thus, artifacts of guide wires and forking of blood vessels in the image can be identified and eliminated effectively, and thus, rapid accurate 3D blood vessel boundary segmentation is realized.

The invention discloses a fast color image segmentation method based on combination of clustering analysis and an image pyramid. The method comprises the following steps: 1) constructing a Gauss and Laplace image pyramid according to a source image, and obtaining a low-resolution image and an edge image; 2) transforming the low-resolution image to an HSV (hue, saturation and value) space, obtaining the clustering number and an initial clustering center through histogram analysis, and performing clustering segmentation on the low-resolution image in the HSV space by use of a clustering algorithm; 3) performing up-sampling on the segmentation result obtained in the step 2), projecting to the original resolution, performing spatial filtering and removing the excessively small area to obtain the original resolution area segmentation result; and 4) integrating the edge image obtained in the step 1) and the area segmentation result obtained in the step 3) to obtain the final segmentation result. The method disclosed by the invention guarantees the segmentation quality while increasing the color image segmentation speed, and is suitable for a digital image processing system with high requirements on real-time property.

The invention provides an improved Euclidean clustering-based scattered workpiece point cloud segmentation method and relates to the field of point cloud segmentation. According to the method, a corresponding scene segmentation scheme is proposed in view of inherent disorder and randomness of scattered workpiece point clouds. The method comprises the specific steps of preprocessing the point clouds: removing background points by using an RANSAC method, and removing outliers by using an iterative radius filtering method. A parameter selection basis is provided for online segmentation by adopting an information registration method for offline template point clouds, thereby increasing the online segmentation speed; a thought of removing edge points firstly, then performing cluster segmentation and finally supplementing the edge points is proposed, so that the phenomenon of insufficient segmentation or over-segmentation in a clustering process is avoided; during the cluster segmentation, an adaptive neighborhood search radius-based clustering method is proposed, so that the segmentation speed is greatly increased; and surface features of workpieces are reserved in edge point supplementation, so that subsequent attitude locating accuracy can be improved.

The invention discloses an automatic segmentation method based on graph cut for color images and gray level images, mainly solving the problems of the existing graph cut technology that interaction and modeling are required in graph cut and the segmentation result is required to be modified manually. The method comprises the following steps: dividing an image into an inner area and an outer area; establishing the data item of the energy function according to the similarity of pixels in different areas, wherein mean shift, YCbCr color space conversion and block partition are adopted in calculation of the similarity; establishing the smoothing item of the energy function according to the marginal information and spatial location of the image; adopting graph cut to perform optimization to the energy function, thus realizing one-step cutting to the image; and using the segmentation result as the new inner and outer areas, performing iterative execution of the above operations, and stopping iterative execution when iterative conditions are satisfied. The method has the advantages of automation, good effect and less iterations and can be used in the computer vision fields such as image processing, image editing, image classification, image identification and the like.

The invention provides a three-dimensional lung vessel image segmentation method based on a geometric deformation model. The method comprises the following steps: (1) determining vessel segmentation computing regions according to the physiological structure characteristics of a human body, wherein region selection completely covers targets to be segmented and the shape characteristics of the regions are stable, thereby avoiding computing a global region and improving segmentation speed; (2) computing the mean value of the vessel regions and positioning internal and external homogeneous regions of the targets; (3) computing vessel edge energy and evolving a curved surface along second derivatives in an image gradient direction so that the curved surface is accurately converged to a target edge; (4) correspondingly establishing a three-dimensional vessel segmentation curved surface evolution model and effectively combining the mean value and edge energy of the internal and external regions of the lung vessels; and (5) adopting optimized level set evolution for obtaining solution according to the established deformation model and impliedly solving a curved surface motion according to the level set function curved surface evolution. A large quantity of lung CT image experiments proof that the method provided by the invention has the advantages of rapid and accurate lung vessel segmentation and strong robustness.

The invention discloses a satellite image segmentation method based on residual network and U-Net segmentation network. The satellite image segmentation method comprises the following steps: constructing a residual network ResNet 34; constructing U-Net segmentation network; constructing a training sample set; training the residual network ResNet 34; training the U-Net segmentation network; inputting the satellite image to be segmented into the residual network ResNet 34 for binary classification, and judging that a ship target is included; using the U-Net segmentation network to perform binarysegmentation on the positive samples in the classification result; for the negative samples in the classification result, directly outputting a single-value mask graph. According to the method, the satellite image is subjected to binary classification by using the residual network ResNet 34, and the U-Net segmentation network is used to only segment the positive samples in the classification result, and an SE-ResNet module is embedded in the U-Net segmentation for extracting finer segmentation masks and the satellite image segmentation method is high in real-time performance and segmentationprecision.

The invention relates to an image segmentation method and system. The method comprises the following steps: initially positioning medical images to obtain a positioning area; preprocessing the positioning area to obtain a target area, wherein the target area contains a nodular area and a background area, and the nodular area consists of a solid area and a surrounding area which surrounds the solid area; processing the target area in combination with a Gaussian mixture model to obtain a probability graph of the target area; processing the probability graph according to a morphological model to determine the solid area and the surrounding area of a nodule; and fusing the solid area and the surrounding area to obtain an image segmentation result. According to the method and system provided by the invention, different types of nodules can be accurately segmented, and the follow-up diagnostic analysis can be effectively improved.

The invention provides a lung segmentation method, comprising following steps: step S1, inputting an original image, carrying out preprocessing to the original image; step S2, carrying out coarse segmentation to a lung region, wherein the step S2 comprises: step S201, setting a threshold value, using the threshold value to carrying out segmentation, carrying out binarization processing to the preprocessed image, extracting the lung region and partial background similar to the gray value of the lung region; step S202, carrying out background backfill starting from the edges at four corners of the image; step S203, finding out a layer of lung with maximum area in the z direction from the top of the head downwards; step S204, carrying out forward and backward layer-by-layer region growth in the z direction by the maximum layer of lung, judging layer by layer, preventing adhesion with the background; step S3, carrying out fine segmentation to the lung region so as to extract and remove trachea and separate left and right lungs. According to the settings, the segmentation speed is increased; and the segmentation effect is improved.

The invention provides a polarized SAR (synthetic aperture radar) image segmentation method with space adaptivity, mainly solving the problem that a segmentation result can not reflect image detail information caused by insufficient space complexity adaptivity by using the conventional segmentation technique. The polarized SAR image segmentation method comprises the following steps of: firstly, combining H / alpha-ML Wishart cluster and quadtree disassemble to obtain initial segmentation areas with unequal sizes and capability of adapting scene complexity, and regulating the sizes and the shapes of the initial segmentation areas by utilizing complex Wishart distribution and an MRF (markov random field) to obtain a final segmentation result. The method fully and effectively utilizes polarization information of a polarization SAR image with the excellent space adaptivity, the segmentation result can be used for well reserving detail information of a polarization SAR image, the segmentation speed is relatively quick and the segmentation result is relatively exact.

The invention relates to the technical field of image processing, and discloses an end-to-end image segmentation processing method and system. The end-to-end image segmentation processing method includes the steps: acquiring an image for testing end-to-end image segmentation processing, and taking the image as a sample image for training; establishing an end-to-end image segmentation network, and using the sample image to perform training learning; and according to an obtained image segmentation network model, segmenting a target image so as to realize segmentation of the target image and outputting the segmentation result of the whole target image for one time. The end-to-end image segmentation processing method has the advantages of being relatively high in accuracy of image segmentation, having no demand for segmenting small images, saving the storage resource of a computer, and reducing the time of preparing images, and can output the segmentation result of the whole image for one time, thus improving the segmentation speed and simplifying the steps of image segmentation.

The invention provides a method for automatically segmenting consistent damage areas for buddha figure Thangkas. The method includes the steps of conducting vertical projection for a head light area of a Thangka image, obtaining an image symmetric axis by using a one-dimensional function symmetry detection method, obtaining an initial segmentation result by employing a partitioning segmentation method based on the symmetric axis, obtaining the image covered by the damage areas, extracting texture characteristics by using Gabor transformation, constructing a multi-scale and multi-characteristic set in combination with Lab space color characteristics, employing KNN classification to obtain a secondary segmentation result, further refining the damage areas by using morphological operation, and removing small damage areas to finally obtain a template of consistent damage areas. Through the method, various large-scale linear and block falling areas in the buddha figure Thangkas can be automatically segmented, the segmentation speed is fast, the efficiency and accuracy are high, and rapid automatic segmentation of the damage areas of buddha figure Thangkas can be realized.

The invention discloses a retinal fundus image segmentation method based on a deep full convolutional neural network, and the method comprises the steps: selecting a training set and a test set, carrying out the extraction of a retinal fundus image, obtaining an optic disc positioning area image, and carrying out the blood vessel removal operation; Constructing a deep full convolutional neural network, taking the image of the optic disc positioning area as the input of the deep full convolutional neural network, performing optic disc segmentation model training on the training set based on thetrained weight parameter as an initial value to finely adjust model parameters, and performing parameter fine adjustment on the optic disc segmentation model on the basis; Using the trained visual cup segmentation model to perform visual cup and visual disc segmentation on the test set, performing ellipse fitting on a final segmentation result, and calculating a vertical cup-visual disc ratio according to a segmentation boundary of the visual cup and the visual disc. Automatic segmentation of the optic disc and the optic cup of the retinal fundus image is realized, the precision is high, andthe speed is high.

The invention discloses a full-automatic image segmentation method. The method comprises the following steps of: inputting a to-be-segmented MRI (Magnetic Resonance Imaging) image, and converting the image into a gray level image; obtaining pixel feature of each bone-gristle edge in the gray level image, wherein the pixel feature comprises global pixel feature and local feature; respectively forming a binary-class SVM (Support Vector Machine) model corresponding to various bone-gristles; and respectively performing image segmentation for the various bone-gristles in the gray level image through the binary-class SVM model according to the pixel feature. In the method provided by the invention, by obtaining the pixel feature of the bone-gristle edge in the gray level image, and forming corresponding binary-class SVM model for different bone-gristles, the thighbone gristle, the tibia gristle and the patella gristle are independently segmented through the binary-class SVM model according to the obtained pixel feature, pre-segmentation for the bone is not needed, less pixel features are used and the segmentation speed is fast.

The invention discloses a navel orange image segmenting method based on adaptive step size harmony search algorithm, and aims at solving the shortages of slow segmenting and low segmenting precision of the traditional harmony search algorithm applied to navel orange image segmenting. The method is characterized in that search factors are generated according to the chaotic motion rules during tone adjustment in a naval orange image division process adopting the harmony search algorithm; then the difference between the optimal individual and random individual is utilized to adaptively determine the search step size, so that the search performance of the algorithm can be improved; in addition, the information of the optimal individual and the individual in the adjacent area is utilized to generate a reverse individual in the adjacent area, and the reverse individual in the adjacent area is included into the selection operation, so as to speed up the convergence of the algorithm; compared with the similar methods, the method has the advantages that the naval orange can be quickly segmented, and the segmenting precision can be improved.

The invention discloses an automatic threshold value image segmentation method based on an entropy value and facing to transmission line part identification, comprising the following steps: converting an input transmission line colour image into a gray level image, and establishing a gray level histogram and an entropy value histogram aiming at the gray level image; determining a proper gray level stretching scheme according to the entropy value histogram, and stretching the gray level of the gray level image; repeating method for establishing the gray level histogram and the entropy value histogram in the last step, and reestablishing the entropy value histogram of the gray level image the gray level of which is stretched; finding an inflection point of entropy value saltation on an entropy value curve when the entropy value histogram appears to be a monotone increasing curve; evaluating the inflection point by a maximum distance method, wherein a gray level value corresponding to the inflection point is the optimal threshold value of the image threshold value segmentation; and changing the threshold value of the gray level image which is stretched by the optimal threshold value so as to complete the image segmentation. The invention has easy algorithm realization, low operation cost and high operation speed and can meet the need of the real-time preprocessing of a high-resolution image of the automatic line walking of transmission line taking the sky as a main background.

The invention discloses an improved Mask R-CNN image instance segmentation method for identifying defects of power equipment. The method comprises the steps of constructing a convolutional neural network; reading the power equipment defect pre-processed picture, inputting the power equipment defect pre-processed picture into a convolutional neural network, and performing feature extraction on thepower equipment defect pre-processed picture by the convolutional neural network to obtain a feature-containing region; refining the region containing the characteristics through an RPN network; and in the refined region, carrying out RoIAgign operation through a bilinear difference method to process the refined region, generating a feature map of a fixed size for each region of interest, and obtaining power equipment defect instance segmentation through the category, the coordinate information and the mask information. By improving the Mask R-CNN image instance segmentation method, the basiceffect of Mask R-CNN segmentation is reserved, the bilinear interpolation speed in the RoIAgign process is increased, meanwhile, the mapping process fully and uniformly utilizes power equipment defects to preprocess all pixels of an image, and the segmentation effect is more obvious.

The invention discloses a migratory active contour model based stomach CT (computerized tomography) sequence image segmentation method, which mainly overcomes the shortcomings that the CT sequence image segmentation speed is slow, and an edge leakage phenomenon is easy to occur in the prior art. The method is implemented through the steps of firstly, manually drawing a line so as to make the initial contour of a target area to be segmented of a first image, and carrying out segmentation by using an area-edge combined active contour model so as to obtain a target contour of the current image; then, repeatedly migrating the target contour of the image subjected to segmentation to the adjacent next image, and taking the target contour of the image as the initial contour of the adjacent next image, and then carrying out segmentation by using a GLCV model until images in the whole sequence are completely segmented. Compared with the traditional active contour model, the method disclosed by the invention has the advantages of rapid speed and good effect and the like, and can be applied to the segmentation of stomach CT sequence images; and by using the method, possibly occurring target areas of gastric lymph nodes can be segmented better.

The invention relates to the technical field of pelvis and pelvis artery blood vessel tree segmentation, in particular to a deep learning-based pelvis and pelvis artery blood vessel multi-level segmentation method, which is used for solving the problem that an abdomen pelvis and a pelvis artery blood vessel tree cannot be automatically, efficiently and accurately segmented in a multi-resolution CTimage in the prior art. The method comprises the following steps: step 1, preparing and labeling data; 2, preprocessing the data; 3, constructing a first-level segmentation model of the 3D convolutional neural network based on multi-level segmentation; 4, constructing a second-stage segmentation model; 5, training a first-stage segmentation model and a second-stage segmentation model by using thecalibrated data and the synthesized loss function; and 6, performing abdomen information segmentation on the input three-dimensional CT image by using the first-stage segmentation model and the second-stage segmentation model trained in the step 5. According to the method, the abdominal pelvis and the pelvis vascular tree can be automatically, efficiently and accurately segmented in the multi-resolution CT image.

The invention discloses a multi-threshold image segmentation method based on a cooperative quantum particle swarm algorithm. The multi-threshold image segmentation method provided by the invention comprises the following steps of: (1) depending on an optimal segmentation threshold, establishing and initializing a first generation of partial swarm; (2) depending on an adaptability function of the multi-threshold segmentation, calculating an adaptability value of each particle, and calculating an individual optimal position of each particle as well as an overall optimal position of all the particles; (3) updating a position vector of each of the particles by a cooperative quantum-behaved particle swarm iteration formula, as well as the individual optimal position of each particle and the overall optimal position of all the particles; and (4) repeating the steps (2) and (3) until satisfying iteration times of the particle swarm iteration formula. According to the image segmentation method, the multi-threshold resolving speed of a target function based on the maximum between-class variance, and the segmentation efficiency are improved.