40results about How to "Achieve precise segmentation" patented technology

The invention discloses a principal vector analysis based broken bone section segmentation and broken bone model registration method. The method comprises the following steps of S1: performing axis extraction of a broken bone model by adopting a principal vector analysis algorithm; S2: extracting a grid vertex set of a broken bone section according to normal vector mutation of a triangular patch on a broken bone three-dimensional grid model and an included angle between the triangular patch and the broken bone axis; S3: performing spatial rough registration on the broken bone three-dimensional grid model in combination with methods for alignment of the broken bone axis and alignment of a principal vector of the grid vertex set of the broken bone section; S4: performing multi-time iterative computation for grid vertex sets of two broken bone sections by utilizing an iterative closest point algorithm to realize spatial fine registration of the broken bone three-dimensional grid model; and S5: according to a complete skeleton model obtained by broken bone three-dimensional grid model registration, performing fitting of a fracture steel plate model. According to the method, the grid vertex set of the broken bone section can be accurately segmented, rough consistency of the sections is realized in rough registration, the precision of fine registration is improved, and the success rate of fine registration is increased.

The invention discloses an auroral oval segmenting method based on a brightness self-adaptive level set, which mainly solves the defects of the existing auroral oval segmenting method that the segmentation precision is low, the robustness is poor and the application range is small. The auroral oval segmenting method comprises the following steps of (1) adopting a morphology component analysis method to preprocess an ultraviolet aurora image; (2) establishing a morphology saliency map to be used as shape characteristics of the auroral oval; (3) utilizing the marginal curve of the morphology saliency map to initialize a level set function; (4) calculating the brightness self-adaptive level set evolution speed and a stop function; (5) updating the level set function according to the brightness self-adaptive level set evolution equation; and (6) extracting a zero level set curve after being updated and utilizing the zero level set curve as the auroral oval margin to be outputted. Due to adopting the auroral oval segmenting method, the phenomenon of the traditional segmenting method such as result deviation and margin leakage can be avoided, advantages such as high segmentation precision and strong robustness can be achieved, and the method is applicable to the segmentation of different ultraviolet auroral images.

The invention relates to an eye fundus image vessel segmentation method based on a local enhancement active contour module. The method comprises: according to a feature vector of a Hessian matrix, vessel enhancement in an eye fundus image is carried out; curvature distribution statistics is carried out on the enhanced image to obtain an eyeball edge and get rid of the eyeball edge; and with a local enhancement active contour module, enhanced image segmentation is carried out by combining global energy information, thereby extracting an eye fundus vessel. According to the invention, on the basis of gray level distribution characteristics of the vessel in the medical eye fundus image, a local energy function is established and a global energy functional item is combined, so that the curve evolution process becomes stable; the speed is accelerated; and the vessel in the eye fundus image can be extracted precisely and effectively.

The invention discloses an image segmentation method of a local energy functional and a non-convex regular term combined with local entropy, comprising the following steps: (1) reading an image I (x,y); (2) initializing the parameters of the local energy functional and the non-convex regular term based on the local entropy in the image segmentation model; (3) calculating the local entropy hx of image I (x, y); (4) segmenting the image I (x, y) on the basis of the initialization parameters in the step (2), segmenting the image I (x, y) using a local energy functional based on the local entropyand an image segmentation model of a non-convex regular term, and updating the level set function phi in the segmentation process; 5) carry out evolution on that level set function phi according to the equation, jud whether the level set function phi converges or not, if so, stopping the evolution of the level set function phi, and outputting a segmented image; Otherwise, the process returns to step (4) to continue. The invention can efficiently and accurately segment gray-level non-uniform images.

The invention relates to a method for segmenting an inhomogeneous medical image. The method for segmenting the inhomogeneous medical image comprises the following steps that firstly, foreground seed points and background seed points on the image to be segmented are selected; secondly, the probability that each grey level belongs to the foreground or the background of the image to be segmented is evaluated according to grey level information of a selected seed point set, the grey levels are mapped on all pixel points of the image, and therefore a corresponding probability density distribution graph is obtained; thirdly, the selected foreground seed points and the selected background seed points are used as growing seed points respectively, one probability threshold on the corresponding probability density distribution graph is used as a growing condition, a region growing algorithm is executed, and therefore a foreground seed point group and a background seed point group which have grown automatically are obtained; finally, the obtained seed point groups which have grown automatically are used as seed points of a random walk algorithm, the random walk algorithm is executed, and a final segmentation result is obtained. By the adoption of the method for segmenting the inhomogeneous medical image, the sensitivity to the number and the position of initial seed points can be reduced, and the segmentation precision of the inhomogeneous medical image is obviously improved.

The invention discloses a left-atrial-appendage CT image segmentation method, and relates to medical image processing. Firstly, left-atrial-appendage CT images are denoised, prior knowledge is utilized to construct missing boundary information of a left atrial appendage, and a region growing algorithm is used to obtain a single initial segmentation image to use the same as a single Atlas label graph; then information relationships of the single label graph and an adjacent left-atrial-appendage CT sequence are utilized for improved Atlas segmentation to obtain an initial segmentation sequence;contour information in the initial segmentation sequence is used as initial contours of a level set algorithm, and approximate Euclidean distance information of left-atrial-appendage CT sequence graphs is obtained to be used as feature input; and finally, a precise segmentation result sequence is obtained through a fast level set algorithm to achieve the purpose of left-atrial-appendage CT image segmentation.

The invention discloses an occlusion tissue stripping method for a three-dimensional ultrasonic image, which comprises the steps of performing segmentation processing on a three-dimensional ultrasonicimage from a sagittal plane direction to obtain a plurality of slices; Carrying out contour key point identification on the plurality of standard slices by adopting a convolutional neural network toobtain key points of the standard slices; Fitting a cross section curve where each key point is located according to the same key point in each standard slice, and determining the key point at the corresponding position in the non-standard slice according to each cross section curve; Connecting the key points of the slices to obtain contour boundaries; And cutting the corresponding slices according to all the contour boundaries to obtain a plurality of cut slices, and synthesizing the cut slices to obtain a target three-dimensional ultrasonic image. The key points of the non-standard slices can be determined through the standard slices, so that the non-standard slices are cut. The invention further provides a shielded tissue stripping system, an ultrasonic detection device and a computer readable storage medium which have the above beneficial effects.

The invention discloses an image processing method and device and a storage medium. The method comprises the steps of extracting a target area image in an original pathological image; performing imagesegmentation on the target area image to obtain a focus area image, the focus area image comprising a focus area of the target area image; and marking a focus boundary of the target area image in thetarget area image or the original pathological image according to the focus area image. According to the image processing method, after the target area image is extracted from the original pathological image, accurate focus boundary detection can be conducted on the target area image, and therefore the image processing method used for achieving area-level accurate focus boundary detection is provided.

The invention discloses a device for splitting albedo and exocarp of tangerine peel, which belongs to the technical field of fruit processing apparatus. The device consists of an upper tangerine peel conveyor belt (1), a lower tangerine peel conveyor belt (2), a roll (3), a power unit (4) for the conveyor belts, a cutting tool (5), a cutting tool positioning device (6) and a power transmission unit (7) of the cutting tool. When in use, the tangerine peel with a radian structure is firstly pressed to be even by the device, then the exact position of the cutting tool is adjusted and fixed based on the thickness proportion between the albedo and the exocarp of the tangerine peel to obtain the best splitting effect, which avoids the problem of unevenly splitting the tangerine peel with different thicknesses by the common plane cutting tools. The device lays a foundation for splitting the tangerine peel effectively for respectively extracting and manufacturing pectin and tangerine essential oil with low cost and high quality. The device can be popularized and applied in fruit processing enterprises.

The invention discloses a semi-supervised industrial image defect segmentation method based on an adversarial generative network. A neural network is trained by using a small number of marked negativesamples with defects and a large number of positive samples without defects so as to obtain a segmentation network capable of automatically identifying the defects. In the construction process of theneural network, a segmentation network based on D-LinkNet and a reconstruction network based on U-net are respectively used, and feature spaces of a negative sample and a positive sample are separated in a cross training mode, so that the segmentation network can correctly segment defects in the negative sample. According to the method, dependence on industrial defect sample images can be greatlyreduced, and errors of a segmentation model during defect segmentation can be greatly reduced.

The invention discloses a Chinese ancient book character recognition method, a Chinese ancient book character segmentation, a layout reconstruction method, a medium and equipment, and the Chinese ancient book character recognition method comprises the steps: firstly obtaining a Chinese ancient book document image marked with a character bounding box and a character category, and taking the image as an original training sample; acquiring an annotation file of the original training sample; randomly selecting a plurality of original training samples, and processing the original training samples to obtain new training samples: processing the original training samples and the new training samples in an online random cutting mode to obtain a training sample set; training a character level detection classification model through training samples in the training sample set; and inputting a Chinese ancient book document image of which characters are to be recognized into the character level detection classification model to obtain a prediction bounding box and a prediction category of each character of the Chinese ancient book document image. According to the method, common characters can be recognized, some uncommon special characters in the ancient books can be recognized very accurately, and the problems of misjudgment, omission and the like existing in ancient book document recognition in the prior art are solved.

The embodiment of the invention provides an optic nerve segmentation method and device in a magnetic resonance image, and the method comprises the steps: carrying out the image registration of a target magnetic resonance image, obtaining the spatial probability distribution information of the target magnetic resonance image, wherein the spatial probability distribution information comprises shapeinformation and position information of a visual pathway; predicting the target magnetic resonance image and the spatial probability distribution information based on a trained visual pathway segmentation model to obtain a visual pathway segmentation image of the target magnetic resonance image; wherein the trained visual pathway segmentation model is obtained by training a sample magnetic resonance image and sample space probability distribution information. According to the embodiment of the invention, the spatial probability distribution information of the visual pathway in the magnetic resonance image is acquired, and the visual pathway in the magnetic resonance image is segmented according to the shape information and the position information of the spatial probability distribution information, so that the problem that the boundary of the visual pathway is fuzzy is effectively overcome, and accurate segmentation of the visual pathway is realized.

The invention provides a fetal heart measurement method and device, and the method comprises the steps of obtaining a to-be-measured fetal heart ultrasonic cardiac four-cavity cardiac tangent plane image, and obtaining a to-be-segmented image; inputting a to-be-segmented image into the trained segmentation model to obtain a segmentation result image; and automatically measuring the size of each segmentation region in the segmentation result image. According to the invention, accurate segmentation of each structure of the fetal heart in the to-be-segmented image is realized. The size of each segmentation area in the segmentation result image is measured, automatic measurement is realized, the problem of low accuracy of the measurement result due to the fact that the measurement process is tedious and the operation individual only depends on other nonstandards in the prior art is avoided, the accuracy of the measurement result can be ensured, therefore, an expert can carry out rapid andaccurate diagnosis according to an automatic measurement result, conditions are provided for the ability of the expert to benefit more patients and regions, and the prenatal detection rate of the fetal congenital heart disease is improved.

The invention belongs to the technical field of image segmentation, and provides an image segmentation method and device, and a server, and the method comprises the steps: inputting a to-be-segmentedimage into an image segmentation model, carrying out the feature extraction of the to-be-segmented image to generate a feature map, and calculating the spatial position relation between pixel points in the feature map to obtain spatial position information; fusing the feature map and the spatial position information to obtain a feature map containing the spatial position information; and segmenting the to-be-segmented image according to the feature map containing the spatial position information, and outputting a target image. According to the embodiment of the invention, the problem that theboundary between different to-be-segmented targets cannot be segmented accurately is solved.

The invention discloses an enterprise work task distribution system based on artificial intelligence, and relates to the field of enterprise task distribution, the technical scheme comprises a task system and a database, the task system internally comprises a central processing module, a task input module, a task identification system, a task matching system, a cloud storage module, a database and a supervision system, the external of the task system is in communication connection with a cloud storage module, the cloud storage module is in communication connection with a database, and a task input module inputs tasks inside an enterprise and transmits the input task information to a central processing module; task types can be identified through the task identification system, and accurate segmentation of the tasks can be realized through matching use of a task segmentation module, an employee ability evaluation module and an ability and task fitting module in the task matching system and comparison with previous tasks in a database.

The invention discloses a user grouping method. The method comprises the steps of determining one or more exclusive attributes of a category according to browsing behaviors of users purchasing commodities in the category in a first predetermined browsing time range; performing traversal combination on commodity attribute values of the exclusive attributes and commodity attribute values of other exclusive attributes to obtain user groups corresponding to all combinations; and allocating users with intentions of purchasing the commodities in the category in a second predetermined browsing time range to corresponding user groups. The exclusive attribute is a commodity attribute and has multiple commodity attribute values; the multiple commodity attribute values completely cover all the commodities in the category; and the commodity attribute values of the same commodity attributes selected by the same user group in a process of browsing the commodities in the category are most concentrated and have relatively low coincidence degree with other commodity attribute values of the commodity attributes. By adopting the method, the user groups can be distinguished according to real purchasedemands of the users.

The invention discloses a blood vessel segmentation method based on a geodesic distance map and a process function equation, and the method comprises the steps: enabling the front propagation based on the geodesic distance map to be expanded to the application of an asymmetric Finsler measurement condition, considering the anisotropy and asymmetry of a boundary, strengthening the description of a measurement function, and setting a freezing criterion of the front propagation; the situation of leakage in the anterior propagation process is effectively prevented, smooth anterior propagation is guaranteed, the target object can be found more accurately and quickly, and accurate segmentation of the blood vessel is achieved.

The invention provides a retinal vessel segmentation method based on an improved U-Net network. Image enhancement is performed on a color eye fundus image, so that the contrast ratio between a blood vessel and a background in the image is improved, and a training data set is amplified. A U-Net encoder-decoder structure is used as a basic segmentation framework, a dense convolution block and a CDBR layer structure are designed to replace a traditional convolution block, learning of multi-scale feature information is achieved, and the feature extraction capacity of the model is improved. Meanwhile, an attention mechanism is introduced at a jump connection part of the model, so that the model is enabled to allocate weights again, the importance degree of a feature channel is adjusted, noise is suppressed, the problem of blood vessel information loss in an up-sampling process at a decoder end is solved, and a GAB-D2BUNet network model is constructed based on the above technologies. According to the method, an internationally disclosed retina fundus blood vessel data set DRIVE is adopted for training, and finally the optimal segmentation model is reserved to verify the segmentation performance of the model. The retina fundus blood vessel segmentation method achieves the task of accurately segmenting the retina fundus blood vessel, and has better segmentation performance.