Intelligent navigation system for human body bone positioning rapid position identification and detection method

Abstract

The invention discloses an intelligent navigation system and a detection method for human body bone positioning rapid positioning identification, and relates to the technical field of human body bone positioning, and the key points of the technical scheme are that image data of a bone structure is acquired by using an image acquisition module; carrying out denoising and contrast enhancement processing on the acquired image; performing feature extraction and skeleton structure recognition on the image by using a deep learning recognition module; according to an identification result, determining positions of key nodes of the skeleton by using an image registration technology; displaying the skeleton structure and the key node information through a user interaction interface; and the detection results are classified and stored in a database for subsequent analysis and query. According to the invention, the features of the human skeleton structure are automatically identified and classified by using the deep convolutional neural network, the accuracy of skeleton identification is significantly improved, the image registration technology is adopted in the skeleton positioning module, the positions of key nodes of the skeleton can be accurately determined, the adaptability is high, and diversified operation requirements can be met.

Description

Technical Field

[0001] The present invention relates to the technical field of human bone positioning, and more specifically, to an intelligent navigation system and a detection method for rapid positioning and identification of human bones. Background Art

[0002] In orthopedic surgery, accurate positioning of human bones is crucial. Surface positioning is required before surgery, and other vertebrae not involved need to be firmly fixed during surgery to avoid displacement during surgery. However, existing fixation devices have poor adjustment performance and low adaptability, making it difficult to meet diverse surgical needs.

[0003] With the rapid development of medical imaging technology and artificial intelligence technology, computed tomography (CT) has been widely used in clinical diagnosis, which has improved the level of medical care. The use of artificial intelligence technology to process and analyze medical imaging data can provide a powerful auxiliary role in modern medical diagnosis, especially in the measurement of human bone density. It can accurately locate the position of human bones and automatically obtain images of human bones, which is of great significance to medical research such as bone density analysis.

[0004] In the field of computer vision, human skeleton key point detection is an important task, which enables computers to perceive the positions of key points of human skeletons, providing a basis for multiple practical scenarios such as action recognition, action anomaly detection, and intelligent monitoring. By constructing and training a fully convolutional deep neural network, using a monocular depth map as input, outputting a probability confidence map of human skeleton key points, and calculating the coordinates of the skeleton key points, the positioning effect of the skeleton key points is improved.

[0005] In binocular vision surgical navigation, three-dimensional reconstruction needs to be achieved through the alignment of rigid bones with external binocular vision marking devices. The traditional approach is to use steel nails to fix the binocular vision marking devices to the bones, but this will increase the pain and risks of the patient; therefore, the present invention aims to provide an intelligent navigation system and detection method for rapid positioning and identification of human bones, which can reduce errors caused by inaccurate operation, improve positioning accuracy, have high adaptability, and meet diverse surgical needs. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent navigation system and detection method for rapid positioning and identification of human bones. The present invention uses a deep convolutional neural network to automatically identify and classify the characteristics of the human bone structure, which significantly improves the accuracy of bone identification. At the same time, the image registration technology is used in the bone positioning module to accurately determine the position of key bone nodes. It has high adaptability and can meet diverse surgical needs.

[0007] The above technical objectives of the present invention are achieved through the following technical solutions: an intelligent navigation system and detection method for rapid positioning and identification of human bones, including an image acquisition module, an image preprocessing module, a deep learning recognition module, a bone positioning module, a user interaction module and a data storage module;

[0008] The image acquisition module is used to capture image data of the human body surface;

[0009] The image preprocessing module is used to perform denoising and enhancement processing on the collected images to improve the accuracy of image recognition;

[0010] The deep learning recognition module is used to extract features and recognize bone structures of the preprocessed images using a deep convolutional neural network;

[0011] The skeleton positioning module uses image registration technology to accurately locate key skeleton nodes based on the recognition results;

[0012] The user interaction module is used to provide operation guidance and skeleton information display to the user;

[0013] The data storage module is used to classify, store and manage the detection data.

[0014] The present invention is further configured as follows: the deep learning recognition module includes a training learning unit, a classification recognition unit and an image processing unit;

[0015] The training and learning unit uses at least one deep convolutional neural network to train and learn the characteristics of different human bone structures;

[0016] After feature learning, the classification and recognition unit is used to classify and recognize the learned features;

[0017] The image processing unit is used to process and recognize human skeleton images in a variety of different postures and angles.

[0018] The present invention is further configured as follows: the bone positioning module includes an image registration unit, a three-dimensional reconstruction unit and a dynamic tracking unit;

[0019] The image registration unit is used to determine the positions of key bone nodes;

[0020] The three-dimensional reconstruction unit is used to perform three-dimensional reconstruction on the identified bone structure to provide more accurate spatial positioning;

[0021] The dynamic tracking module is used to track and update the positions of key skeleton nodes in real time under dynamic conditions.

[0022] The present invention is further configured as follows: the user interaction module includes an operation guidance unit, a display unit, a query unit and a feedback unit;

[0023] The operation guidance unit is used to provide intuitive operation guidance and allow the user to select different detection and identification modules;

[0024] The display unit uses images, diagrams and text descriptions to display the skeletal structure and key nodes;

[0025] The query unit is used for users to query specific skeleton information;

[0026] The feedback unit is used to provide feedback information to optimize system performance.

[0027] The present invention is further configured as follows: the data storage module includes an analysis and processing unit, a storage unit and a backup and recovery unit;

[0028] The analysis and processing unit is used to perform real-time analysis and processing on the detection data;

[0029] The storage unit is used to store the detection data in a database;

[0030] The backup and recovery unit is used to provide data backup and recovery functions to ensure the security and integrity of data.

[0031] The present invention also provides a detection method for a human body bone fast positioning recognition intelligent navigation system, comprising the following steps:

[0032] S1. Scan the human body surface using an image acquisition module to obtain image data of the bone structure;

[0033] S2, performing denoising and contrast enhancement processing on the collected image to improve the image quality;

[0034] S3, use the deep learning recognition module to extract features from the image and identify the bone structure through the trained model;

[0035] S4. Based on the recognition results, the positions of the key bone nodes are determined using image registration technology;

[0036] S5. Display the skeleton structure and key node information through the user interaction interface, and allow users to query and interact;

[0037] S6. The detection results are classified and stored in a database for subsequent analysis and query.

[0038] In summary, the present invention has the following beneficial effects:

[0039] 1. The present invention uses deep learning technology, especially deep convolutional neural network (CNN), to enable the system to automatically identify and classify the characteristics of human bone structure, significantly improving the accuracy of bone recognition. At the same time, the image registration technology is used in the bone positioning module to accurately determine the position of key bone nodes, providing accurate data support for subsequent medical diagnosis, teaching or virtual reality applications;

[0040] 2. The present invention has a powerful real-time processing capability for real-time processing of image data, and can quickly complete the entire process from image acquisition to bone recognition, meeting the demand for rapid recognition. At the same time, the system also adopts an optimized algorithm to reduce the computational complexity and improve the data processing speed, so that the system can complete the processing of a large amount of data in a short time;

[0041] 3. The user interaction interface of the present invention is intuitively designed and easy to operate, so that users can quickly understand and use the system, improving the user experience. At the same time, the system allows users to interact with the bone information and query specific bone information, which enhances the interactivity and educational value of the system;

[0042] 4. The system of the present invention can be used in the medical field for auxiliary diagnosis, surgical planning and rehabilitation training to improve the quality and efficiency of medical services. It can also be used as an auxiliary tool for anatomical teaching in the educational field to provide a real human skeleton model to help people understand the human skeleton structure more intuitively.

[0043] 5. The system of the present invention can store the detection results in a database, which is convenient for subsequent analysis and query, and provides valuable data resources for research and development. At the same time, the system also provides data backup and recovery functions to ensure the security and integrity of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a module schematic diagram of an intelligent navigation system for rapid bone positioning and recognition in an embodiment of the present invention;

[0045] Figure 2 It is a schematic diagram of the flow structure of an intelligent navigation system for rapid bone positioning and recognition in an embodiment of the present invention;

[0046] Figure 3 is a schematic diagram of a deep learning recognition module in an embodiment of the present invention;

[0047] Figure 4 is a schematic diagram of a bone positioning module in an embodiment of the present invention;

[0048] Figure 5 is a schematic diagram of a user interaction module in an embodiment of the present invention;

[0049] Figure 6is a schematic diagram of a data storage module in an embodiment of the present invention;

[0050] Figure 7 The present invention is a flowchart of a detection method for a human body bone fast positioning recognition intelligent navigation system according to an embodiment of the present invention.

[0051] In the figure: 1. Image acquisition module; 2. Image preprocessing module; 3. Deep learning recognition module; 4. Skeleton positioning module; 5. User interaction module; 6. Data storage module; 301. Training and learning unit; 302. Classification and recognition unit; 303. Image processing unit; 401. Image registration unit; 402. Three-dimensional reconstruction unit; 403. Dynamic tracking unit; 501. Operation guidance unit; 502. Display unit; 503. Query unit; 504. Feedback unit; 601. Analysis and processing unit; 602. Storage unit; 603. Backup and recovery unit. DETAILED DESCRIPTION

[0052] The following is combined with Figure 1-7 The present invention is described in further detail.

[0053] Embodiment 1: An intelligent navigation system for rapid positioning and identification of human bones, comprising an image acquisition module 1, an image preprocessing module 2, a deep learning recognition module 3, a bone positioning module 4, a user interaction module 5 and a data storage module 6.

[0054] In this embodiment, the image acquisition module 1 uses a high-definition camera to perform a full-body scan of the human body to obtain image data of the human body surface. The image preprocessing module 2 performs denoising and contrast enhancement on the acquired images to improve the image quality. The deep learning recognition module 3 uses a pre-trained deep convolutional neural network model to extract features and recognize bone structures from the images.

[0055] The concept in the process of deep learning recognition module 3 extracting features from images and identifying bone structures, the deep learning recognition module 3 also specifically includes a training learning unit 301, a classification recognition unit 302 and an image processing unit 303. Among them, the training learning unit 301 uses at least one deep convolutional neural network to train and learn the features of different human bone structures, the classification recognition unit 302 classifies and recognizes the learned features after feature learning, and the image processing unit 303 further processes and recognizes human bone images in a variety of different postures and angles.

[0056] In this embodiment, the skeleton positioning module 4 uses image registration technology to compare the identified skeleton structure with the standard human skeleton model to determine the position of the key skeleton nodes; in the process of the skeleton positioning module 4 determining the position of the key skeleton nodes, the skeleton positioning module 4 specifically includes an image registration unit 401, a three-dimensional reconstruction unit 402 and a dynamic tracking unit 403, the image registration unit 401 determines the position of the key skeleton nodes; the three-dimensional reconstruction unit 402 performs three-dimensional reconstruction on the identified skeleton structure, so that more accurate spatial positioning can be provided, and the dynamic tracking module tracks and updates the position of the key skeleton nodes in real time under dynamic conditions.

[0057] The user interaction module 5 in this embodiment is used to provide operation guidance and skeleton information display for the user; in the process of the user interaction module 5 providing operation guidance and skeleton information display, specifically, the user interaction module 5 also includes an operation guidance unit 501, a display unit 502, a query unit 503 and a feedback unit 504. The operation guidance unit 501 helps to provide intuitive operation guidance, so that the user can select different detection and recognition modules. The display unit 502 in this embodiment mainly uses images, charts and text descriptions to display the skeleton structure and key nodes. The query unit 503 can help the user query specific skeleton information. The feedback unit 504 continuously collects feedback information during use for continuous iteration of system optimization performance.

[0058] The data storage module 6 in the intelligent navigation system of this embodiment includes classified storage and management of detection data. In the process of classified storage and management of detection data, the data storage module 6 includes an analysis and processing unit 601, a storage unit 602 and a backup and recovery unit 603; wherein the analysis and processing unit 601 performs real-time analysis and processing according to the acquired detection data, the storage unit 602 stores the analyzed and processed detection data in a database, the backup and recovery unit 603 multiples the detection data and provides data backup and recovery functions for the user. The user can obtain the detection data that was lost by mistake according to the backup and recovery unit 603, thereby further ensuring the security and integrity of the data.

[0059] Embodiment 2: A detection method for a human body bone fast positioning recognition intelligent navigation system, comprising the following steps:

[0060] S1. Scan the human body surface using an image acquisition module to obtain image data of the bone structure;

[0061] S2, performing denoising and contrast enhancement processing on the collected image to improve the image quality;

[0062] S3, use the deep learning recognition module to extract features from the image and identify the bone structure through the trained model;

[0063] S4. Based on the recognition results, the positions of the key bone nodes are determined using image registration technology;

[0064] S5. Display the skeleton structure and key node information through the user interaction interface, and allow users to query and interact;

[0065] S6. The detection results are classified and stored in a database for subsequent analysis and query.

[0066] This embodiment is preferred. This embodiment is based on an intelligent navigation system for rapid positioning and identification of human bones in embodiment 1. Through deep learning technology and image processing technology, rapid and accurate positioning and identification of human skeletal structure are achieved. This method has broad application prospects, especially in the fields of medical diagnosis, anatomical teaching, virtual reality and human-computer interaction, and has important practical value and market potential.

[0067] This specific embodiment is merely an explanation of the present invention and is not a limitation of the present invention. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed. However, as long as they are within the scope of the claims of the present invention, they are protected by the patent law.

Claims

1. An intelligent navigation system for rapid identification of human bones, characterized by: It comprises an image acquisition module (1), an image preprocessing module (2), a deep learning recognition module (3), a bone positioning module (4), a user interaction module (5) and a data storage module (6); The image acquisition module (1) is used to capture image data of the human body surface; The image preprocessing module (2) is used to perform denoising and enhancement processing on the collected images to improve the accuracy of image recognition; The deep learning recognition module (3) is used to perform feature extraction and bone structure recognition on the preprocessed image using a deep convolutional neural network; The bone positioning module (4) uses image registration technology to accurately locate key bone nodes based on the recognition results; The user interaction module (5) is used to provide operation guidance and skeleton information display to the user; The data storage module (6) is used for classifying, storing and managing the detection data.

2. According to claim 1, an intelligent navigation system and detection method for rapid human bone positioning and identification is characterized by: The deep learning recognition module (3) comprises a training learning unit (301), a classification recognition unit (302) and an image processing unit (303); The training and learning unit (301) uses at least one deep convolutional neural network to train and learn the characteristics of different human bone structures; The classification and recognition unit (302) is used to classify and recognize the learned features after feature learning; The image processing unit (303) is used to process and recognize human skeleton images in a variety of different postures and angles.

3. The intelligent navigation system and detection method for rapid human bone positioning and identification according to claim 1 is characterized by: The bone positioning module (4) comprises an image registration unit (401), a three-dimensional reconstruction unit (402) and a dynamic tracking unit (403); The image registration unit (401) is used to determine the positions of key skeletal nodes; The three-dimensional reconstruction unit (402) is used to perform three-dimensional reconstruction on the identified bone structure to provide more accurate spatial positioning; The dynamic tracking module is used to track and update the positions of key skeleton nodes in real time under dynamic conditions.

4. The intelligent navigation system and detection method for rapid human bone positioning and identification according to claim 1 is characterized by: The user interaction module (5) comprises an operation guidance unit (501), a display unit (502), a query unit (503) and a feedback unit (504); The operation guidance unit (501) is used to provide intuitive operation guidance and allow the user to select different detection and identification modules; The display unit (502) uses images, diagrams and text descriptions to display the skeletal structure and key nodes; The query unit (503) is used for a user to query specific skeleton information; The feedback unit (504) is used to provide feedback information to optimize system performance.

5. The intelligent navigation system and detection method for rapid human bone positioning and identification according to claim 1 is characterized by: The data storage module (6) comprises an analysis and processing unit (601), a storage unit (602) and a backup and recovery unit (603); The analysis and processing unit (601) is used to analyze and process the detection data in real time; The storage unit (602) is used to store the detection data in a database; The backup and recovery unit (603) is used to provide data backup and recovery functions to ensure the security and integrity of data.

6. The detection method for a human body bone fast position identification intelligent navigation system according to claim 1, characterized in that: The following steps are involved: S1, scanning the human body surface to obtain image data of the bone structure; S2, performing denoising and contrast enhancement processing on the collected image to improve the image quality; S3, use the deep learning recognition module to extract features from the image and identify the bone structure through the trained model; S4. Based on the recognition results, the positions of the key bone nodes are determined using image registration technology; S5. Display the skeleton structure and key node information through the user interaction interface, and allow users to query and interact; S6. The detection results are classified and stored in a database for subsequent analysis and query.

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