200 results about "3d segmentation" patented technology

The invention is suitable for the technical field of intelligent diagnosis, and relates to a multi-mode breast volume ultrasound focus grading method, medium and terminal, and the method comprises the steps: carrying out the full-coverage scanning of a target region through automatic volume ultrasound equipment, obtaining automatic volume ultrasound data, and carrying out the preprocessing; carrying out three-dimensional segmentation and point cloud feature extraction on the preprocessed data; processing the dynamic gray ultrasonic time sequence through a visual Transform model so as to extract dynamic gray features; the ultrasonic elastography image is processed through a 2D convolutional neural network, and static elastic features are extracted; fusing the point cloud features, the dynamic gray features and the static elastic features through a weighting algorithm or a multi-modal neural network structure; and processing the fused features through a neural network layer, inputting the processed features into a classification layer, and carrying out breast focus BI-RADS classification. The method is simple in process and convenient to operate, and the accuracy of breast focus grading is effectively improved.

The invention belongs to the technical field of three-dimensional scene understanding, and particularly relates to a training method and device for a three-dimensional open vocabulary semantic segmentation model. The training method comprises the steps of obtaining multi-view RGB-D images of a target area, performing multi-stage reasoning on each image through a visual language model, generating a target vocabulary list, prompting a two-dimensional segmentation model to establish a pixel-level text label, performing depth mapping on the images to generate a first point cloud, and generating a second point cloud; mapping the text tag to the first point cloud to generate a point-by-point text tag; pre-training a neural network model with a sparse encoder-decoder structure by taking the point-by-point text label as a supervision signal, and generating a three-dimensional segmentation model on the first point cloud; and for the second point cloud of the complete scene of the target area, matching point feature embedding and text embedding with the highest similarity in the shared vision-language feature space, generating a credible point-text tag pair, and finely adjusting the three-dimensional segmentation model based on the credible point-text tag pair.

The invention discloses an automatic identification method, device and equipment for mandibular wisdom teeth, a medium and a product, and relates to the field of image processing.The method comprises the steps that an oral maxillofacial cone beam computed tomography image is obtained; segmenting and positioning the mandibular wisdom tooth region-of-interest in the oral and maxillofacial cone beam computed tomography image by adopting a pre-trained first deep learning model to obtain a mandibular wisdom tooth segmentation mask image, and cutting a mandibular wisdom tooth region-of-interest image from the oral and maxillofacial cone beam computed tomography image; adopting a pre-trained second deep learning model to perform three-dimensional segmentation on the mandibular wisdom tooth region-of-interest image to obtain a three-dimensional segmentation mask image; and according to the mandibular wisdom tooth region-of-interest image and the three-dimensional segmentation mask image, determining a mandibular wisdom tooth recognition result by adopting a pre-trained third deep learning model. The identification accuracy and the identification efficiency of the wisdom teeth of the lower jaw are improved.

The invention discloses a mandibular neural tube automatic segmentation method based on multi-view guidance, and relates to the technical field of oral medical treatment. Comprising the following steps: collecting original oral cavity CBCT image data, and performing preprocessing; sending the preprocessed image data into a mandible segmentation network, and segmenting a mandible region containing a mandible neural tube; a contrast enhancement technology is adopted for the segmented mandible area; projecting the processed mandible image along three orthogonal tangent planes of a coronal plane, a sagittal plane and a cross section; performing key point detection on the mandibular neural tubes on the three projection planes by using the trained key point detection network to obtain shape information of the mandibular neural tubes; and the features are combined with texture direction features to be input into a three-dimensional CBCT mandibular neural tube segmentation network, and accurate 3D segmentation is carried out. Two-dimensional missing space information is avoided, and mandibular neural tube segmentation is more complete and accurate.

The invention relates to the technical field of medical image processing, in particular to a lung tumor CT image 3D segmentation method and system based on multi-modal image fusion. The method comprises the following steps: acquiring a lung tumor CT image; determining a first texture feature based on the lung tumor CT image; identifying a lung tumor boundary by using the first texture feature; detecting a nodule protrusion area in the boundary of the lung tumor; obtaining tissue infiltration data from the nodule protrusion area; determining a second texture feature according to the tissue infiltration data; determining a tumor heterogeneity feature according to the first texture feature and the second texture feature; evaluating the potential malignancy degree by utilizing tumor heterogeneity characteristics; and dividing a tumor risk area of the lung tumor CT image based on the potential malignancy degree. According to the invention, accurate heterogeneity identification and risk region division of the lung tumor CT image are realized based on a medical image processing technology, and the accuracy of lung tumor 3D segmentation is improved.

The invention discloses an object three-dimensional reconstruction method, device and system based on deep learning. The reconstruction method comprises the following steps: acquiring a multi-view color image of an object through a controllable image acquisition device; reconstructing a sparse three-dimensional point cloud by using a motion recovery structure method and obtaining a camera pose; initializing parameters of the three-dimensional Gaussian sputtering model based on the sparse point cloud and performing training optimization; a target object semantic segmentation data set is constructed, and a low-rank adaptive technology is adopted to finely segment all models; generating prompts through an open vocabulary detection model at each view angle, obtaining an accurate segmentation mask, and optimizing a three-dimensional segmentation weight by adopting a joint loss function fusing color consistency loss and edge perception loss; and finally outputting the color three-dimensional point cloud of the target object. According to the method, the original image is segmented, so that the influence of the quality of the rendered image is avoided; the segmentation precision of the model in a specific scene is improved through field adaptive fine tuning; and the accuracy of the segmentation boundary is ensured by adopting a double-loss joint optimization mechanism.

The invention relates to the technical field of concrete structure damage detection, in particular to a post-fire RC structure burst damage three-dimensional segmentation method, which comprises the following steps: firstly, determining the type and three-dimensional characteristics of post-fire RC structure member surface concrete burst damage, and constructing a corresponding three-dimensional point cloud data set for network training and verification; and then based on a KP-FCNN network structure, a KPConv layer is improved and optimized, so that the detection and segmentation precision is improved, the model size is reduced, the reasoning time is remarkably shortened, the optimal segmentation precision of 82.3% is achieved under the working conditions of different damage degrees, automatic damage segmentation of the burst damage of the concrete structure after the fire disaster is realized, and the automatic damage segmentation of the burst damage of the concrete structure after the fire disaster is realized. And technical support is provided for subsequent damage three-dimensional quantification and deployment to an unmanned aerial vehicle system.

The invention discloses a 3D segmentation method and system based on bidirectional fusion, and the method comprises the steps: obtaining the image information and camera information of a fixed scene; then, the image information and the camera information pass through a three-dimensional image segmentation model, and a preliminary segmentation result of a three-dimensional space is obtained; and finally, based on the preliminary segmentation result, carrying out bidirectional fusion on two adjacent frames of point cloud images to obtain an optimized segmentation result, then obtaining a geometric information-based segmentation result of the scene, and carrying out bidirectional fusion on the point cloud image based on the geometric information segmentation result and the point cloud image based on the optimized segmentation result to obtain a final segmentation result. According to the method, the complexity of the outdoor scene and the difference of the sparse degrees of the point clouds are fully considered, the problem that segmentation masks of front and back frames of a long-distance object in an outdoor environment are inconsistent is solved, the segmentation consistency of an outdoor target is enhanced, the applicability to a large-range and complex scene is improved, and the method is suitable for large-scale and complex scenes. And the feasibility of the SAM3D-based three-dimensional segmentation method in practical engineering application is improved.

The invention relates to the technical field of region segmentation, in particular to a three-dimensional segmentation method for a hematoma region in a craniocerebral MRI scanning image, and the method comprises the steps: obtaining a target 3D image corresponding to a to-be-detected patient through craniocerebral MRI scanning; determining a T1 gradual change expression factor and a T2 gradual change expression factor of each voxel point in each preset direction according to the T1 and T2 weighted gray level change conditions of each voxel point in each preset direction; screening out a T1 main gradual change direction and a T2 main gradual change direction corresponding to each voxel point; determining a fuzzy possible factor corresponding to each voxel point and an overall main gradual change direction, and making a gradual change vertical plane to which each voxel point belongs; determining a red and swollen possible factor corresponding to each voxel point; and segmenting a target hematoma area from the target 3D image according to the position corresponding to the voxel point, the gray value and the red and swollen possible factor. According to the invention, the segmentation of the hematoma region is realized, and the accuracy of the segmentation of the hematoma region is improved.

A head mounted display (HMD) device is provided. The HMD includes a display, a gaze sensor configured to detect a gaze direction of a user of the HMD device, a camera, a position sensor configured to detect a position of the HMD device, communication circuitry, memory storing one or more computer programs, and one or more processors communicatively coupled to the display, the gaze sensor, the camera, the position sensor, the communication circuitry, and the memory, wherein the one or more computer programs include computer-executable instructions that, when executed by the one or more processors individually or collectively, cause the HMD device to, based on the position of the HMD device, acquire three-dimensional (3D) segmentation information for a space corresponding to the position of the HMD device, acquire gaze point-based interest information corresponding to a profile of the user, the gaze point-based interest information including interest information generated based on gaze points of a plurality of users, display, on the display, a first visual effect for a plurality of regions in the space based on the 3D segmentation information and the interest information, and display, on the display, a second visual effect for a plurality of objects in a first region among the plurality of regions based on the 3D segmentation information and the interest information when the position of the HMD device corresponds to the first region.

A computer device performs feature extraction on two-dimensional medical images included in a three-dimensional medical image, to obtain image features corresponding to the two-dimensional medical images. The three-dimensional medical image are obtained by continuously scanning a target tissue structure. The computer device determines offsets of the two-dimensional medical images in a target direction based on the image features. The computer device performs feature alignment on the image features based on the offsets, to obtain aligned image features. The computer device performs three-dimensional segmentation on the three-dimensional medical image based on the aligned image features, to obtain three-dimensional layer distribution of the target tissue structure in the three-dimensional medical image.

The invention discloses a lightweight 3D heart MRI segmentation algorithm based on 3D efficient partial attention and three-dimensional sparse MLP, and the algorithm obtains an MRI pixel block with a detailed target region feature through a 3D efficient deformable attention module EDA, and fits the edge distribution of a segmented region with lower calculation complexity through reference point translation. Efficient attention calculation ensures feature information extraction and greatly reduces resource consumption; the method comprises the following steps: constructing a cross-shaped receptive field at extremely low cost through information in transverse, longitudinal and depth directions of a three-dimensional sparse MLP module (triSMLP) mixed voxel block, and outputting a fine characteristic spectrum with a global receptive field through three-time superposition; the feature maps output by the EDA and the triSMLP recover down-sampling lost detail features at an encoder through a partial convolution feature fusion decoder. Compared with most of the leading edge 3D segmentation algorithms at present, the method has the advantage that a better segmentation effect is obtained with extremely small computing resources.

The invention discloses a multi-axis RWKV-UNet + + multi-mode MRI (Magnetic Resonance Imaging) brain tumor segmentation method, and belongs to the technical field of medical image processing. According to the invention, multi-modal MRI three-dimensional body data is input and preprocessed, and fusion features are output through a modal fusion module; the fusion features are input into an encoder containing multi-axis RWKV sequence modeling, and long-range dependence is extracted; after the output of the encoder is processed by the bottleneck layer, the global Token aggregator converges the global context and reinjects the global context; the enhanced features are input into a UNet + + nested topology decoder, the jump features are fused with the up-sampling features after being subjected to jump RWKV semantic alignment, and finally a three-dimensional segmentation probability graph is generated through mapping. The method is mainly used for accurate three-dimensional segmentation of the multi-mode MRI brain tumor, and provides support for clinical brain tumor diagnosis and treatment.

The invention discloses a brain tumor MRI image segmentation method based on a soft clustering KAN network, and belongs to the technical field of medical information processing, and the method comprises the steps: carrying out the voxel alignment and intensity normalization of a multi-modal three-dimensional brain tumor MRI image, and constructing a unified input sample; layer-by-layer downsampling and multi-scale feature extraction are realized through a KAN residual encoder formed by alternately cascading double-layer KAN attention subnets and three-dimensional maximum pooling layers; three-dimensional position coding is added to the highest-layer features, and bottleneck features containing global structure priori are obtained through soft K-means clustering bottleneck layer modeling; fusing the jump connection feature and the up-sampling feature by using an attention gating mechanism to complete decoding; and through soft clustering regularization and a multi-scale depth supervision constraint optimization model, finally through fusion of a main segmentation branch and a refined branch, outputting a multi-subarea three-dimensional segmentation result of the whole tumor, the tumor core and the enhanced tumor.

The invention discloses a CBCT (Cone Beam Computed Tomography) image distance determination method and device for maxillary sinus and maxillary posterior teeth, a medium and a product, and relates to the field of image processing, the method comprises the following steps: acquiring CBCT images of maxillary sinus and maxillary posterior teeth; according to the CBCT images of the maxillary sinus and the maxillary posterior teeth, performing three-dimensional segmentation of the maxillary sinus and the maxillary posterior teeth by adopting a structure segmentation model; the structure segmentation model is constructed based on a deep learning network; according to a three-dimensional segmentation result, generating a surface mesh model based on a marching cube algorithm; determining point cloud data based on a point cloud generation algorithm according to the surface mesh model; according to the point cloud data, the nearest distance between the maxillary sinus and the maxillary posterior teeth and the corresponding nearest apical point of the maxillary posterior teeth are determined; and determining the position relationship between the maxillary sinus and the maxillary posterior teeth according to the nearest distance, and classifying the position relationship. The distance between the maxillary sinus and the maxillary posterior tooth can be efficiently and accurately measured, and the position relation can be judged.

The invention discloses a three-dimensional scene segmentation method based on 3D Gaussian Splitting, the core idea is to make full use of the relation between different views in the step-by-step segmentation process, which relates to two complementary aspects: 1, the target is to enhance the algorithm for aggregating a 2D segmentation mask into 3D segmentation, especially to improve the precision and integrity in a scene with a small number of lenses; this function of a small number of lenses is crucial to providing cues for new views because the cues depend on segmentation predictions from preceding views. Second, by retaining the reliable cues used in the previous view, the new view can avoid cueing inconsistent targets. On the basis, a simple and effective lifting algorithm based on Gaussian center perspective is provided, the completeness of three-dimensional segmentation is guaranteed, boundary distinguishing is enhanced, and accurate three-dimensional segmentation is achieved. Besides, a prompt propagation strategy is introduced, and the reliable prompt from the adjacent view is effectively utilized by restoring the 2D prompt into the scene, so that accurate segmentation is realized.

The invention discloses a three-dimensional digital core aperture feature intelligent three-dimensional segmentation method and related equipment, and the method comprises the steps: firstly obtaining an aperture feature contour and region in a two-dimensional image slice in a mode of learning a network model, achieving high speed and high efficiency, and then carrying out the segmentation of the aperture feature contour and region in a two-dimensional image slice through a mode of mapping and information merging. According to the invention, the method achieves the three-dimensional segmentation and extraction of the hole seam features of the whole three-dimensional digital rock core, achieves the automatic segmentation through the deep learning technology, is high in efficiency, is high in precision, is easy to popularize, can effectively solve the technical problem of the conventional three-dimensional image processing, and meets the demands of the related fields for the high-quality three-dimensional feature extraction. Compared with an artificial three-dimensional segmentation method, the method has a wide application prospect. The method not only can be used as a powerful auxiliary means for conventional digital core processing work, but also provides an efficient three-dimensional image analysis tool for microcosmic feature research in related research fields, and has important application value.

The invention relates to a three-dimensional object segmentation method and system based on a neural radiation field, and the method comprises the steps: training a basic neural radiation field model through a multi-view original RGB image, and obtaining a pre-trained neural radiation field model; fine tuning is performed on the pre-trained neural radiation field model according to a two-dimensional segmentation mask corresponding to the original RGB image, a three-dimensional segmentation neural radiation field model is obtained, and the two-dimensional segmentation mask comprises a first pixel value representing a foreground object and a second pixel value representing a background; segmenting an image to be segmented through the three-dimensional segmentation neural radiation field model to generate segmented three-dimensional point clouds; and clustering the three-dimensional point cloud to obtain a three-dimensional segmentation result of the to-be-segmented image. According to the method, high-precision three-dimensional segmentation is realized through a two-stage fine tuning strategy, a neural radiation field model architecture does not need to be changed, a three-dimensional segmentation process is simplified, and segmentation precision and counting accuracy are improved.

The invention provides a multi-modal tissue segmentation method and a surgical navigation system, and the method comprises the steps: inputting a multi-modal three-dimensional medical image into a corresponding deep learning network model for segmentation, and obtaining a corresponding three-dimensional segmentation result; registering the three-dimensional medical image of each modal to obtain a registration relation; and according to the registration relationship, combining the preset weight of the three-dimensional medical image of each modal for each type of tissue, and carrying out weighted fusion on the three-dimensional segmentation results of each modal. According to the method, the segmentation model is trained for the medical image of each modal, and each deep learning model has higher segmentation capability for the medical image of the corresponding modal and higher segmentation precision; different types of medical images respectively design respective preset weights (modal weights) for each type of tissues, and the segmentation results are fused according to the modal weights in the fusion process, so that the tissue structure features are reflected to a greater extent, and the segmentation precision is improved.

The invention discloses a radiotherapy path planning method based on surface profile monitoring, particularly relates to the field of medical image data processing, and is used for solving the problems of low reusability and low intelligent degree of existing medical radiotherapy path planning. The method comprises the following steps: constructing a distribution prediction model of radiotherapy incidence points by collecting marked target region characteristic parameters and incidence point distribution in a historical case, completing three-dimensional segmentation of a target region and an organ structure in a CT image of a current patient, and establishing a unified voxel coordinate system; constructing an incidence point array covering the surface of the target region according to a prediction result, generating a candidate radiotherapy path set, and performing risk organ recognition on each path; further combining the spatial distance between the path trajectory and the risk organ contour surface to calculate the comprehensive risk score of the candidate path; and finally, performing dose simulation on the candidate paths according to a scoring sequence, outputting a radiotherapy path planning result meeting dose and safety requirements, and improving the precision and clinical applicability of path selection.

The invention provides a fetal heart region image segmentation method and system, electronic equipment and a storage medium, and the method comprises the steps: segmenting a current frame image of fetal heart body data through employing an inference network and a Canny edge detection algorithm; the fetal heart segmentation area of the current frame image is determined according to the obtained first fetal heart segmentation area and the second fetal heart segmentation area, and the inference network carries out inference based on the two-dimensional features and the three-dimensional features of the current frame image to obtain the first fetal heart segmentation area. The invention aims to combine two-dimensional information and three-dimensional information of an image so as to effectively solve the problems of non-fitting edges and poor reasoning effect of some frames in 3D segmentation, and a segmentation result of a Canny edge detection algorithm is used as a reference to help to judge and correct segmentation output generated by a reasoning network so as to improve the efficiency of 3D segmentation. By combining the advantages of the multi-source features and the Canny edge detection algorithm, the precision of complex heart structure segmentation is improved, and a more reliable segmentation result can be obtained.

The invention discloses a pulmonary nodule growth prediction method based on the combination of LSTM and GAN, and relates to the technical field of image analysis and processing, and the method comprises the following steps: collecting multi-time-sequence chest CT data, and carrying out the marking and auditing to form a data set; preprocessing the marked CT data to obtain a standardized pulmonary nodule ROI (Region of Interest); segmenting a pulmonary nodule region by using the three-dimensional image segmentation network, and generating a three-dimensional segmentation mask; inputting the segmentation masks of the multiple time nodes into an LSTM network, and extracting a time sequence feature vector representing nodule dynamic evolution; based on the time sequence feature vector and the random noise, the generator synthesizes the predicted pulmonary nodule image at the future moment, and the discriminator jointly optimizes model parameters through multiple loss functions; and inputting the three-dimensional segmentation masks at the current moment and the historical moment, and generating a predicted pulmonary nodule image at the future moment. The method can effectively improve the situation that the prior art is insufficient in utilization of time sequence information and lacks high-quality generation and inference ability.

Unsupervised volumetric 3D animation (UVA) of non-rigid deformable objects without annotation learns the 3D structure and dynamics of the object only from a single view red / green / blue (RGB) video and decomposes the single view RGB video into semantically meaningful portions that can be tracked and animated. Using a 3D automatic decoder framework, the UVA model learns 3D geometry and partial decomposition of underlying objects from still or video images in a fully unsupervised manner via a microperspective n-point (PnP) algorithm pairing with a keypoint estimator. This allows the UVA model to perform 3D segmentation, 3D keypoint estimation, novel view synthesis, and animation. The UVA model may obtain animatable 3D objects from a single or several images. The UVA method also characterizes a space in which all objects are represented in their normative, animated ready form. Applications include creating a shot from an image or video of a social media application.

The invention discloses a cerebral hemorrhage heterogeneity quantitative evaluation system based on cyclic cross-attention clustering, and relates to the field of cerebral hemorrhage evaluation, and the system comprises an image segmentation module which is used for carrying out the three-dimensional segmentation of a brain plain-scan CT image through an nnU-NetV2 model, and outputting the binary masks of a hematoma region and an edema region; the feature extraction module is used for extracting high-dimensional pixel features from the mask region and generating an initial feature embedding matrix; the cyclic cross-attention clustering module is used for dynamically updating a clustering center through a multi-scale cyclic EM cross-attention mechanism and executing clustering analysis on the feature embedding matrix; the heterogeneity scoring module is used for calculating information entropy and generating hematoma and edema heterogeneity scores; and the prognosis prediction module is used for predicting the hematoma expansion risk and the neural function outcome through a machine learning model. According to the scheme, objective quantification of cerebral hemorrhage heterogeneity can be realized, and the prediction accuracy of the hematoma expansion risk and the neural function outcome is improved.

Provided are an augmented reality device for providing an augmented reality service for controlling an object in a real world space, and an operating method for the same. The method may include recognizing a first plane comprising a wall and a second plane comprising a floor from a spatial image based on a photograph of the real world space; generating a three-dimensional (3D) model of the real world space by extending the wall and extending the floor along the respective planes and performing 3D in-painting on an area of the extended wall and the extended floor that is hidden by an obstructive object; segmenting an object selected by a user input from the spatial image based on two-dimensional (2D) segmentation; and segmenting the object on the spatial image from the real world space based on a 3D model or 3D position information of the object using 3D segmentation.

The invention discloses a tooth CBCT image three-dimensional segmentation method and system, and relates to the technical field of image segmentation, and the method comprises the following steps: data collection and processing, image preprocessing, image registration, feature extraction and fusion, three-dimensional segmentation model construction and training, and segmentation effect evaluation. According to the method, dynamic MRI examination and generative adversarial network are introduced to generate virtual data, multi-task learning is combined, multiple modes and multi-time-point data are comprehensively utilized, analysis of soft tissue peripheral information is indirectly enhanced, and soft tissue lesion diagnosis is assisted; the dynamic change of teeth and surrounding tissues can be better captured through the attention to dynamic features in multi-time-point data acquisition, dynamic MRI image preprocessing, feature extraction and fusion and the synergistic effect of model training and evaluation links, and the limitation of CBCT as a static imaging technology in the aspect of evaluating dynamic related disease diagnosis and treatment effects is overcome.

Systems and methods for segmenting 3D images are provided. In an embodiment, the system includes a neural network having a low-resolution module trained to infer a complete low-resolution segmentation from an input low-resolution 3D image and to generate corresponding low-resolution feature maps; and a high-resolution module trained to infer a complete high-resolution segmentation from an input high-resolution 3D image and the feature maps from the low-resolution module. Methods for training the neural network and measuring a volume of an object using the 3D segmentations are also described.

The invention is suitable for the technical field of CT image processing, and provides a diagnosis and segmentation method for a positive lymph node in a CT image based on multi-task learning, and the method comprises the steps: carrying out the preprocessing of a CT image sequence, enhancing the data, constructing a multi-task deep learning model, extracting features through an encoder, and executing a classification task through a classification module. And segmenting the positive lymph node by using a segmentation module in combination with the classification result. According to the method, the classification accuracy (up to 91.1%) and the segmented DICE coefficient (up to 0.602) are both excellent, the method is superior to a single 2D segmentation model, and compared with a 3D segmentation model, the method keeps similar segmentation performance and is shorter in reasoning time. According to the method, classification and segmentation tasks for the positive lymph nodes in the CT image can be completed at the same time, the workload of doctors is relieved, the diagnosis and treatment efficiency of the doctors is improved, and the blank that positive lymph nodes are judged and sketched at the same time in the head and neck CT image of a nasopharyngeal carcinoma patient is filled.

Systems and methods are provided for determining fabrics of a geological sample using three-dimensional segmentation. An example method can include receiving three-dimensional (3D) image of a geological sample, adjusting an initial size of the 3D image of the geological sample, and partitioning the resized 3D image of the geological sample into cubes. The example method can include, for each cube, generating orthogonal planes based on a center of mass of each cube and extracting, for the orthogonal planes associated with each cube, one or more features to represent texture of the geological sample. The example method can further include grouping the cubes into one or more clusters based on the one or more features and constructing a volume of the resized 3D image of the geological sample based on the one or more clusters for a texture analysis of the geological sample.

The invention discloses a depth information three-dimensional morphology-based adhered peanut two-dimensional grayscale image segmentation method, and relates to the technical field of image adhesion segmentation, and the method comprises the following steps: building a line structured light imaging system based on a triangulation principle, collecting the point clouds of a peanut in operation line by line through the system, obtaining a three-dimensional morphology through splicing, and carrying out the segmentation of the three-dimensional morphology of the peanut. The method comprises the following steps: calculating a point cloud normal vector and curvature by combining a local surface fitting algorithm, carrying out three-dimensional segmentation by taking the normal vector and curvature as features and utilizing a region growing segmentation algorithm, then extracting a two-dimensional contour of a single peanut model by adopting a rolling ball method, establishing a search template, and implementing multi-scale feature matching on an adhered image to realize region positioning. And finally, optimizing the segmentation region by using an opening operation. According to the method, the problem that the adhesion image is difficult to segment due to the characteristics of non-convexity of the peanut pod appearance, complex surface texture and the like is effectively solved, the information of peanut height, spherical degree and the like is increased, and the accuracy and rationality of later quality detection are facilitated.