122 results about "Manual segmentation" patented technology

Segmenting the prostate boundary is essential in determining the dose plan needed for a successful bracytherapy procedure—an effective and commonly used treatment for prostate cancer. However, manual segmentation is time consuming and can introduce inter and intra-operator variability. This present invention describes an algorithm for segmenting the prostate from two dimensional ultrasound (2D US) images, which can be full-automatic, with some assumptions of image acquisition. Segmentation begins with the user assuming the center of the prostate to be at the center of the image for the fully-automatic version. The image is then filtered to identify prostate edge candidates. The next step removes most of the false edges and keeps as many true edges as possible. Then, domain knowledge is used to remove any prostate boundary candidates that are probably false edge pixels. The image is then scanned along radial lines and only the first-detected boundary candidates are kept the final step includes the removal of some remaining false edge pixels by fitting a polynomial to the image points and removing the point with the maximum distance from the fit. The resulting candidate edges form an initial model that is then deformed using the Discrete Dynamic Contour (DDC) model to obtain a closed contour of the prostate boundary.

The invention discloses a nasopharyngeal-carcinoma (NPC) lesion automatic-segmentation method and nasopharyngeal-carcinoma lesion automatic-segmentation systems based on deep learning. The method comprises: carrying out registration on a PET (Positron Emission Tomography) image and a CT (Computed Tomography) image of nasopharyngeal carcinoma to obtain a PET image and a CT image after registration;and inputting the PET image and the CT image after registration into a convolutional neural network to carry out feature representation and scores map reconstruction to obtain a nasopharyngeal-carcinoma lesion segmentation result graph. The method carries out registration on the PET image and the CT image of the nasopharyngeal carcinoma, obtains a nasopharyngeal-carcinoma lesion by automatic segmentation through the convolutional neural network, and is more objective and accurate as compared with manual segmentation manners of doctors; and the convolutional neural network in deep learning isadopted, consistency is better, feature learning ability is higher, the problems of dimension disasters, easy falling into a local optimum and the like are solved, lesion segmentation can be carried out on multi-modal images of the PET-CT images, and an application range is wider. The method can be widely applied to the field of medical image processing.

The invention relates to the medical apparatus field, aims to propose an improved full convolutional neural network to realize machine segmentation of a brain tumor nuclear magnetic resonance image, avoids low efficient and instability existing in manual segmentation, provides the rapid and reliable brain tumor segmentation result and provides accurate bases for diagnosis, treatment and operationof brain tumors. The method comprises steps that 1), an image is selected; 2), a full convolutional neural network FCN model is constructed; 3), the segmentation result is tested, when the FCN model is trained, the trained model is utilized to predict the tumor position and the boundary size of any brain tumor image, corresponding evaluation indexes are utilized to evaluate the segmentation result, and the FCN model can be better ameliorated. The method is advantaged in that the method is mainly applied to NMR image processing occasions.

The invention discloses an SVM (support vector machine)-based medical image blood vessel recognition method. The method includes the following steps that: since an SVM classifier is obtained after cross validation and optimization are performed on an SVM model trained by samples automatically selected by an FCM by using an SVM model which is trained by samples obtained through the manual segmentation of experts, segmentation results are more accurate; the SVM is adopted to segment a blood vessel, namely, pixels are divided into a foreground (blood vessels) and a background (other parts), and the blood vessel part is extracted out; morphological processing and thresholding are performed, and therefore, a blood vessel network can be enhanced, the branches and intersections of the blood vessels are reserved; and an obtained image is converted into a binary image, so that blood vessel distribution can be reflected more directly. According to the method of the invention, the FCM, the SVM and the morphological image processing are combined together, so that a recognition effect is better.

The utility model relates to a segmentation method of active web page, and is characterized in that the method first receives the content streams of web page and then builds a DOM tree, then makes the nodes of the DOM tree to feature codings, next compares the corresponding nodes of the DOM trees to build common blocks and customization blocks. The utility model can understand and identify the common parts (common blocks) sharing among multi-pages and the special parts of different variation rules (customization blocks) according to the dynamic characteristic and the structural characteristic of the web pages, and dynamically divide the web pages without human interference. The utility model provides a solution with a good expandability, lowers the labour costs for manual segmentation, and can be widely used in technology field of active web page.

The invention relates to a level-set retinal vessel image segmentation method with fusion shape prior, comprising: (1) utilizing morphological operators and Gaussian convolution to enhance the retinal vessel image; (2) adopting the anisotropy characteristic of Hessian matrix and improving The vascular response function of the retinal vascular image is roughly segmented and used as shape constraints and initialization information; (3) using the shape prior and retinal image data information to construct a model that includes local area energy fitting items, shape constraint items, and level set function regularity maintenance A level set model for retinal vessel segmentation with length penalty term, weighted area constraint term. The segmentation result of the present invention has high accuracy, can replace manual segmentation, can play an important auxiliary role in the diagnosis and treatment of clinically relevant ophthalmic diseases, and has strong clinical application value.

The invention designs a lung parenchyma CT image segmentation method based on a deep convolutional neural network. The method comprises the steps that 1, using The VGG-16 network improved by cavity convolution to extract the convolution characteristics of lung parenchyma in lung CT images;; 2, constructing a multi-scale feature supercolumn descriptor based on multi-layer extraction; And 3, learning a nonlinear predictor for classifying pixels. Results show that the method can realize accurate lung parenchyma image segmentation, compared with the prior art, the method has the advantages that the segmentation accuracy and speed are improved, and the problems of low manual segmentation efficiency, low accuracy, excessive consumption of manpower resources and the like of the lung parenchyma image are successfully solved.

The invention belongs to the technical field of image segmentation, and specifically relates to a 3D full-automatic glioma segmentation method combining a Volume of Interest and a GrowCut algorithm. The method first expands a Bounding box algorithm to 3D, utilizes the algorithm to extract a Volume of Interest (VOI) containing glioma, then utilizes a reflection symmetry algorithm to estimate the VOI and overcomes the defects of Bounding box when the glioma is detected to cross over a median sagittal plane, and finally marks pixel points in an image based on the accurate VOI, and thus a semi-automatic 2D GrowCut algorithm is optimized to a full-automatic 3D segmentation method. While accurately segmenting the glioma, the method is more rapid in theory and reality compared with the 2D segmentation algorithm of the same principle, and is better in convenience and feasibility compared with a manual segmentation method. As an image segmentation method, the 3D full-automatic glioma segmentation method provided by the invention can serve as a powerful auxiliary tool for clinical diagnosis of glioma.

The invention discloses a server thermal fault monitoring and diagnosing method based on infrared images. The server thermal fault monitoring and diagnosing method comprises acquiring infrared images and visible-light images of a server in different thermal fault states; carrying out tilt correction processing on the images in each thermal fault state by means of standardization method based on image registration, and determining the region-of-interest including the server in each infrared image by means of a manual segmentation method; converting the image of the determined region-of-interest into a corresponding gray scale image, and extracting an image entropy feature from the gray scale image; carrying out dimension reduction processing on the obtained global entropy and the local entropy row-column mean value feature by means of a PCA method; wherein the feature main component obtained after dimension reduction is used for training a support vector machine classifier; acquiring the infrared image and the visible-light image of the server to be detected, and diagnosing the thermal fault state type of the server by means of the trained SVM model. According to the invention, the management efficiency of management personnel in the data center is greatly improved, and engineering debugging and system maintenance are facilitated.

The invention discloses a high-resolution remote sensing image house extraction method based on morphological house indexes. The morphological house indexes are constructed on the basis of the morphological algorithm according to the characteristics of high brightness, isotropy and similar rectangle degree of a house on a high-resolution remote sensing image, and a remote sensing image house is automatically extracted according to the morphological house indexes. On this basis, morphological shadow indexes are derived from the morphological house indexes according to similar spatial characteristics and opposite optical characteristics of a shadow and the house, house extraction is restrained through the morphological shadow indexes, and consequently the accuracy degree of house extraction is further optimized. According to the high-resolution remote sensing image house extraction method based on the morphological house indexes, manual segmentation and artificial training are not needed, full-automatic extraction of the remote sensing image house can be achieved; the morphological shadow indexes are added for restraint, the accuracy degree and the similar rectangle degree of house extraction can be obviously improved.

The invention discloses an abnormal sound monitoring method based on large-scale farm mammals, belongs to the field of sound recognition, and particularly relates to an unsupervised sound recognitionmethod. The method mainly comprises the following parts: 1. spectrogram analysis: analyzing collected voice frequencies to determine feasibility of a voice recognition scheme; 2. voice frequency noisereduction: performing noise reduction processing on the voice frequencies to improve accuracy of voice recognition; 3. unsupervised voice frequency segmentation: simplifying a voice frequency processing process without manual segmentation to obtain voice frequency segments containing required sound events; 4. voice frequency feature extraction: adopting a Mel frequency cepstrum coefficient as a feature extraction technology; and 5. unsupervised classification: adopting an unsupervised classification method as a K-means algorithm. According to the method provided by the invention, unsupervisedsound recognition of large-scale farm animals is realized by adopting an unsupervised voice frequency segmentation technology and a K-means classification method, combining a spectrum and time spectrum analysis technology, a voice frequency noise reduction technology and a Mel frequency cepstrum coefficient feature extraction technology.

The invention relates to an aortic valve ultrasonic image segmentation method based on probability distribution and continuous maximum flow. The method comprises the following steps: 1, acquiring medical ultrasonic image data of a human body aortic valve short axis, and extracting a five-frame prior image at equal intervals; 2, segmenting the five-frame prior image; 3, constructing a two-dimensional gray-distance histogram; 4, calculating to obtain a comprehensive probability estimation function through the two-dimensional gray-distance histogram; 5, respectively calculating a respective independent probability estimation function; 6, respectively calculating the pixel gray values which can respectively represent the foreground and background for the five-frame prior image; 7, solving an independent probability estimation map for the current image to be segmented; 8, respectively measuring the similarity for the foreground area and the manual segmentation result of the five-frame prior image; and 9, obtaining the segmentation result. Compared with the prior art, the aortic valve ultrasonic image segmentation method is stable, reliable, convenient to implement and suitable for actual clinical application.

The invention discloses an automatic segmentation method for a rectal cancer CT image based on a U-Transformer. The method comprises the following steps: 1, preprocessing experimental data; 2, constructing a U-Transformer network model; 3, training the U-Transformer network model; 4, adopting the trained U-Transformer network model to carry out segmentation of the rectal cancer in the CT image, and evaluating the segmentation effect. According to the invention, the segmentation of the CT image of the rectal cancer tumor is realized, and the defects of low efficiency and instability of manual segmentation are avoided, so that an accurate basis is provided for diagnosis, treatment and operation guidance of related diseases. Compared with other U-shaped network structures, the method can learn global features and has a larger visual perception range, so that high-precision medical segmentation is realized.

The invention is suitable for the technical field of image processing, and particularly relates to an ultrasound image segmentation method and system. The method includes the following steps that coarse registration is conducted on a statistical shape model and collected three-dimensional ultrasound data of a specified organ to obtain initialized coordinate transformation parameters; according to the initialized coordinate transformation parameters, an image segmentation algorithm based on particle filter is used for conducting iteration segmentation on the three-dimensional ultrasound data, wherein the statistical shape model is a combination of an average value obtained by training manual segmentation results of a plurality of high-definition three-dimensional data and a group of feature vectors of a representation change mode. Therefore, the problem that more artificial participation is needed in manual segmentation and semi-automatic segmentation is solved. Compared with an existing full-automatic segmentation method, the ultrasound image segmentation method and system solve the problems of low image resolution and segmentation accuracy under the image fuzzy condition.

The invention provides a full-automatic CT image kidney segmentation method. Multi-template-based image matching algorithm is used and a multi-resolution two-step algorithm strategy is adopted. A region of interest of the kidney in a low-resolution CT image is firstly positioned, and then kidney tissues in a region of interest in a high-resolution CT image are precisely segmented. The experimental result shows that in view of renal arteriography and urography CT images, accurate segmentation results can be provided, the workload of manual segmentation by a doctor can be greatly reduced, and the efficiency and effects of diagnosis and treatment can be effectively improved.

The invention relates to medical instrument, applies a computer image processing technology to segmentation of a brain tumor magnetic resonance image, avoids the defects of low efficiency and instability existing in manual segmentation, provides a rapid and reliable brain tumor segmentation result by use of a computer algorithm and provides an accurate basis for diagnosis, treatment and surgical guidance of a brain tumor. According to the technical scheme, a brain tumor segmentation method in combination with a convolutional neural network and fuzzy inference comprises the following steps of (1) selecting an image; (2) constructing a CNN (Convolutional Neural Network) model; (3) carrying out nonlinear mapping; and (4) establishing a fuzzy inference system. The brain tumor segmentation method in combination with the convolutional neural network and fuzzy inference is mainly applied to processing of medical images.

The invention discloses a server and a Chinese character segmentation method and device and belongs to the technical field of search engines. The Chinese character segmentation method includes: receiving a word segmentation instruction; acquiring a first Chinese character set; acquiring retrieving information corresponding to each character in the first character set according to preset corresponding relations; acquiring multiple combined characters and retrieving probabilities according to the first character set and the retrieving information corresponding to each character in the first character set; performing path combination according to the characters included in the multiple combined characters; acquiring the retrieving probability of each path; determining the path with the highest probability; performing segmentation on keywords according to the combined characters included in the path with the highest probability. Manual segmentation is omitted, independence on tools of dictionaries and the like is not needed, convenience in operation is achieved, data sources are dynamically updated, wrong segmentation modes can be rapidly corrected, high distinguishing degree is achieved for new characters, and accuracy in segmentation is improved.

The invention relates to the technical field of brain tumor medical image segmentation, in particular to a brain tumor medical image segmentation method based on deep learning, which comprises the steps of training a segmentation model, receiving the to-be-segmented brain tumor medical image data information, performing the segmentation processing on the received to-be-segmented brain tumor medical image data information, and outputting a segmentation result. The brain tumor medical image segmentation system based on deep learning comprises a segmentation model training module, a brain tumor medical image receiving module, a brain tumor medical image segmentation processing module and a segmentation result output module. According to the medical brain tumor image segmentation method basedon deep learning, deep learning training is carried out on the segmentation model, the segmentation model trained through deep learning carries out segmentation processing on the received medical brain tumor image data information to be segmented, the segmentation result is accurate, and the problem that a traditional manual segmentation method is time-consuming and labor-consuming is solved.

The invention discloses a human brain MRI hippocampus detection and segmentation method based on deep learning. The method includes the steps of firstly, conducting data pretreatment on human brain MRI data and labels both of which are obtained by means of various channels and creating a model; secondly, determining a final model and determining super parameters of the final model; thirdly, training and estimating the model; finally, predicating the model, comparing a model predication result with a manual segmentation result, observing the effect, and conducting analysis to obtain a final predicated image. According to the method, historical manual segmentation result image information is fully utilized, not only can detection and segmentation be automatically and efficiently carried out,but also convenience is provided for solving the problems of shortage of doctors in the image department, a poor primary medical capability, a great disparity in the proportion of doctors and patients and the like. When the model is subjected to fitting, L2 regular terms are added for the first time, and regular term super parameters are also added, correspondingly the variance of the model is reduced, and the effect is obviously improved. Through several experiments, the network depth is increased to five layers, and the effect of the model is also improved.

The invention discloses a medical image automatic segmentation method based on deep learning, and the method specifically comprises the steps: (1) obtaining a manual segmentation result of an original medical image of a patient and a target region, and constructing a training data set related to a segmentation task; (2) designing a deep U-shaped convolutional neural network model fusing a residual module and an attention mechanism; (3) constructing a mixed loss function by using Dice and binary cross entropy; (4) performing network training by using the training data set; and (5) segmenting a target area in the test image by using the trained network. The invention relates to an end-to-end medical image full-automatic segmentation method, which can effectively solve the problems of fuzzy segmentation boundary, low small target detection rate, unstable training process, difficulty in convergence and the like in the current deep learning image segmentation field.

The invention relates to a medical image non-rigid registration algorithm performance evaluation method based on segmentation. The method concretely comprises the steps that (1) a fixed image is selected, template image registration is performed through a pending evaluation algorithm to obtain a target image, and the manual segmentation image of the target image is segmented into N parts composed of omega1, omega2,...omegan respectively; (2) values of Jacobian determinants of all body pixels of the post-registration image are calculated; (3) the coordinates of the body pixels in all segmented regions are extracted by utilizing a split image IS of the template image I; (4) the coordinates of the body pixels of the corresponding segmented regions of the target image are extracted, the values of the Jacobian determinants of all the body pixels in all the regions are figured out, one is subtracted from each value, and then the values are added and then averaged to obtain regional Jacobian determinant standard values; (5) square averaging is performed on all the regional Jacobian determinant standard values, so that evaluation parameters JSD are obtained, and the performance of the pending evaluation algorithm is judged. Compared with the prior art, the method has the advantage that the image tissue volume changes before and after the registration can be directly reflected.

The invention relates to a segmentation method of fabric printing cycle pattern primitives based on adaptive template matching, comprising the following steps: 1. the adaptive template selection: automatically determining an adaptive size template image in an image containing the printing cycle pattern; 2. template matching: template pattern in the step 1 is the most template, template matching iscarried out with the original image, and regions similar to the template pattern in the original image are found; 3, determining the size of the cycle: determining the size of the pattern cycle according to the positional relationship between the regions similar to the template pattern found in the step 2; 4. cyclic pattern primitive segmentation: according to the cyclic size determined in step (3), the original image is traversed and a complete pattern primitive conforming to human visual perception is segmented. The invention can automatically segment a complete pattern primitive from an image with a circulating pattern, avoids the shortcomings of slow manual segmentation speed, low efficiency and poor segmentation precision, and accelerates the flow of printing pattern development anddesign.