Asthma lung image processing system based on lung tissue data

Abstract

The invention discloses an asthma lung image processing system based on lung tissue data, which relates to the technical field of medical image processing, and comprises a data acquisition module, a lung segmentation module, a feature extraction module, a visualization module, a lesion analysis module and a data storage module, the lesion analysis module is used for judging the condition of the patient according to the lung data, the lesion analysis module comprises a lung expansion unit, an airway evaluation unit and an asthma risk evaluation unit, and the lung function and the airway obstruction condition are quantitatively analyzed by evaluating the lung expansion value and the airway stenosis value of the patient and combining the actual breathing condition; the method is advantaged in that the asthma risk values are acquired, the risk threshold values are set, the asthma risk values are divided into different grades, doctors are helped to make personalized treatment schemes, subjective errors are reduced, lung health conditions of patients are accurately evaluated, and accuracy and quality of asthma treatment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing, and particularly to an asthma lung image processing system based on lung tissue data. Background Technique

[0002] As a common chronic respiratory disease, asthma is characterized by airway inflammation, airway hyperresponsiveness, airway smooth muscle spasm, etc. The main features are recurrent wheezing, shortness of breath, chest tightness or coughing in the airway, which often occur or worsen at night and in the early morning. The onset of asthma is related to genetic and environmental factors, etc. Among them, genetic factors mainly determine the predisposing constitution of patients. Common types of bronchial asthma include exercise-induced, drug-induced, occupational and allergic, etc. Asthma patients need to receive long-term treatment to control the development of the disease, which seriously affects the quality of life of patients. With the development of medical imaging, it has become increasingly important to use lung image data to assist in the early diagnosis of asthma.

[0003] Currently, the diagnosis and treatment of asthma mainly rely on doctors' clinical experience and patients' self-observation, lacking data support. There are certain subjectivity and errors, making it difficult to accurately evaluate each patient. Moreover, asthma patients show different clinical symptoms and risk levels at different disease stages, and the treatment methods may not be able to effectively meet the needs of each patient, resulting in uneven treatment quality. Summary of the Invention

[0004] The purpose of the present invention is to provide an asthma lung image processing system based on lung tissue data, which solves the problems raised in the above background technique.

[0005] To achieve the above purpose, the present invention provides an asthma lung image processing system based on lung tissue data, including a data acquisition module, a lung segmentation module, a feature extraction module, a visualization module, a lesion analysis module, and a data storage module; The data acquisition module is used to obtain the image data of patients from the hospital database; The lung segmentation module is used to segment the image data to obtain the lung region, and then the feature extraction module is used to extract features from the lung region to obtain lung data. The visualization module is used to visualize the lung data for doctors to understand; The lesion analysis module is used to judge the patient's condition according to the lung data. The lesion analysis module includes a lung expansion unit, an airway assessment unit, and an asthma risk assessment unit; The lung expansion unit obtains the lung expansion value F based on the change in the lung area under different breathing states. The airway assessment unit compares the cross-sectional area of the patient's airway with the standard value to obtain the airway stenosis value S, which is used to understand the airway obstruction situation of the patient. The asthma risk assessment unit is used to analyze the asthma situation of the patient by combining the airway stenosis value S and the lung expansion value F, obtain the asthma risk value C, set a first risk threshold Y1 and a second risk threshold Y2 for the asthma risk value C, and divide the level for the asthma risk value C according to the set thresholds.

[0006] Optionally, the process of obtaining the lung expansion value by the lung expansion unit is as follows: ; where F is the lung expansion value; A exp is the area of the lung region in the deep breathing state; A ori is the area of the lung region in the normal breathing state; P lung is the lung tissue density; P air is the air density; α is the lung density influence coefficient, and its value is 0.7; The lung expansion value L represents the lung expansion ability of the patient. When the lung expansion value L is greater than 1, it means that the expansion ability of the lung region is strong, that is, in the deep breathing state, the expansion area of the lung is significantly larger than that in the normal state, indicating good lung elasticity. The smaller the lung expansion value L is, the worse the expansion ability of the lung region is.

[0007] Optionally, the assessment process of the airway assessment unit is as follows: ; where S is the airway stenosis value; H i,pat is the cross-sectional area of the patient's airway at position i; H i,nor is the standard cross-sectional area of the airway at position i; D pat is the length of the patient's airway; D nor is the standard length of the airway; L pat is the diameter of the patient's airway; L nor is the standard diameter of the airway; β is the airway length influence coefficient, and its value range is from 0 to 1; γ is the airway diameter influence coefficient, and its value range is from 0 to 1; The airway stenosis value S represents the degree of stenosis of the patient's airway. The smaller the airway stenosis value S, the smaller the change range of the cross-sectional area of the patient's airway, the lighter the degree of stenosis, and the normal morphological characteristics of the airway. The larger the airway stenosis value S, the larger the change range of the cross-sectional area of the patient's airway, and the more severe the degree of stenosis.

[0008] Optionally, the asthma risk assessment unit evaluates as follows: ; Where C is the asthma risk value; F is the lung expansion value; W1 is the lung expansion value influence coefficient, and its value range is from 0 to 1; S is the airway stenosis value; W2 is the airway stenosis value influence coefficient, and its value range is from 0 to 1; R pat is the patient's breathing frequency, with the unit of minute; R nor is the standard breathing frequency, with the unit of minute; V pat is the patient's vital capacity; V nor is the standard vital capacity; W3 is the breathing intensity influence coefficient, and its value range is from 0 to 1; The asthma risk value C represents the asthma risk situation of the patient. The larger the asthma risk value C, the more severe the patient's condition. Set a risk threshold one Y1 and a risk threshold two Y2 for the asthma risk value C. When the asthma risk value C is less than Y1, it indicates that the patient's asthma risk is in a low-risk state. When the asthma risk value C is greater than Y1 and less than Y2, it is in a medium-risk state. When the asthma risk value C is greater than Y2, it is in a high-risk state.

[0009] Optionally, when the asthma risk value C is greater than Y1 and less than Y2 and is in a medium-risk state, it indicates that the patient's airway has already shown mild stenosis and irregular changes. At this time, increase the lung density influence coefficient α and adjust the α value to 0.9 to adjust the influence of the lung tissue density on the lung expansion value F.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention evaluates through a lung expansion unit based on the changes in lung area and the influencing factors of lung density under different breathing states of a patient, obtaining a lung expansion value F. The size of the quantified lung expansion value F represents the quality of lung function. Then, through an airway evaluation unit, by comparing the standard cross-sectional area values of the airway at different positions, an airway stenosis value S is obtained. The airway stenosis value S is used to understand the airway obstruction condition of the patient. Finally, through an asthma risk assessment unit, in combination with the lung expansion value F, the airway stenosis value S, and the actual breathing condition of the patient, an asthma risk value C is evaluated. And according to a risk threshold one Y1 and a risk threshold two Y2, grades are assigned to the asthma risk value C. Doctors can prepare treatment plans based on the specific risk levels, reducing the error of subjective judgment, being able to accurately evaluate each patient in combination with lung tissue data, dividing different risk levels for different lung conditions, effectively meeting the needs of each patient, and improving the quality of asthma treatment.

[0011] 2. When the patient's asthma control is poor in the medium-risk state, occasional symptom exacerbations or mild airway inflammations may occur. At this time, the lung density influence coefficient is increased to adjust the influence of lung tissue density on the lung expansion value F, avoiding the deterioration of the condition due to insufficient treatment intensity. The lung expansion unit can be adjusted according to the actual situation of the patient, enabling it to capture subtle changes in the condition and improving the accuracy of treatment decisions; Optionally, the data acquisition module includes a preprocessing unit, and the preprocessing unit includes image denoising, image enhancement, and image normalization; Optionally, after the feature extraction module extracts features of the lung region and obtains lung data, the visualization module is used to display the lung data in the form of a chart for doctors to view; Optionally, the data storage module is used to store imaging data, lung data, and the data generated by the lesion analysis module, and uses distributed storage for storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0014] Embodiment 1: Please refer to Figure 1, this embodiment provides an asthma lung image processing system based on lung tissue data, including a data acquisition module, a lung segmentation module, a feature extraction module, a visualization module, a lesion analysis module, and a data storage module; The data acquisition module is used to obtain the image data of patients from the hospital database. The lung segmentation module is used to segment the image data to obtain the lung region. The lung segmentation module uses threshold segmentation technology. Then, the feature extraction module extracts features from the lung region to obtain lung data. The visualization module is used to visualize the lung data for doctors to understand. The lesion analysis module is used to judge the patient's condition based on the lung data. The lesion analysis module includes a lung expansion unit, an airway assessment unit, and an asthma risk assessment unit. The lung expansion unit obtains the lung expansion value F based on the change in lung area under different breathing states to understand the lung health condition. The airway assessment unit compares the patient's airway cross-sectional area with the standard value to obtain the airway stenosis value S to understand the patient's airway obstruction condition. The asthma risk assessment unit is used to analyze the patient's asthma condition by combining the airway stenosis value S and the lung expansion value F to obtain the asthma risk value C. Set a risk threshold one Y1 and a risk threshold two Y2 for the asthma risk value C, and classify the asthma risk value C according to the set thresholds.

[0015] More specifically, in this embodiment: after obtaining the patient's image data, the lung segmentation module segments the image data to obtain the lung region. By separating the lungs from other tissues, the segmentation result is intuitive and easy to understand, so that the subsequent feature extraction module can extract features from the lung region to obtain lung data. The lung data includes morphological feature data and texture feature data, and the visualization module visualizes the lung data so that doctors can more quickly and accurately understand the patient's lung condition. Then, through the lung expansion unit in the lesion analysis module, the lung expansion unit evaluates based on the change in lung area and the influencing factors of lung density under different breathing states of the patient to obtain the lung expansion value F. The size of the lung expansion value F represents the quality of lung function. Then, through the airway assessment unit, the airway cross-sectional area of the patient is compared with the standard value to obtain the airway stenosis value S. The airway stenosis value S is used to understand the patient's airway obstruction condition.

[0016] Finally, the asthma risk assessment unit combines the lung expansion value F, the airway stenosis value S, and the actual breathing condition of the patient to conduct an assessment, obtaining the asthma risk value C. A first risk threshold Y1 and a second risk threshold Y2 are set for the asthma risk value C, and the asthma risk value C is graded according to the first risk threshold Y1 and the second risk threshold Y2. Doctors can prepare subsequent operations based on the specific risk levels, reducing the error of subjective judgment, being able to accurately assess each patient in combination with lung tissue data, dividing different risk levels for different disease stages, effectively meeting the needs of each patient, and improving the treatment effect of asthma.

[0017] Further, the process of obtaining the lung expansion value by the lung expansion unit is as follows: ; where F is the lung expansion value; A exp is the lung area in the deep breathing state; A ori is the lung area in the normal breathing state; P lung is the lung tissue density; P air is the air density; α is the lung density influence coefficient, with a value of 0.7; Specifically, the lung expansion value L represents the lung expansion ability of the patient. When the lung expansion value L is greater than 1, it indicates that the lung expansion ability of the lung area is strong, that is, in the deep breathing state, the expansion area of the lung is significantly larger than that in the normal state, indicating good lung elasticity. The smaller the lung expansion value L is less than 1, the worse the lung expansion ability of the lung area is, and the greater the probability of problems in the patient's lungs. Doctors can know the specific situation of the patient's lung function through the lung expansion value L, so as to be able to more accurately judge the condition and reduce the error caused by subjectivity.

[0018] Further, the assessment process of the airway assessment unit is as follows: ; where S is the airway stenosis value; H i,pat is the cross-sectional area of the patient's airway at position i; H i,nor is the standard cross-sectional area of the airway at position i; D pat is the length of the patient's airway; D nor is the standard length of the airway; L pat is the diameter of the patient's airway; L nor is the standard diameter of the airway; β is the airway length influence coefficient, and its value range is from 0 to 1; γ is the airway diameter influence coefficient, and its value range is from 0 to 1; Specifically, the airway stenosis value S represents the degree of airway stenosis in the patient. For example, the curvature of the airway, the branching angle, etc. may affect the airflow in the airway. By calculating the cross-sectional area of the airway at different positions, the calculation of the airway stenosis value S becomes more accurate. The smaller the airway stenosis value S, the smaller the change range of the cross-sectional area of the patient's airway, the lighter the degree of stenosis, and the normal morphological characteristics of the airway. The larger the airway stenosis value S, the larger the change range of the cross-sectional area of the patient's airway, and the more severe the degree of stenosis.

[0019] Further, the asthma risk assessment unit evaluates as follows: ; where C is the asthma risk value; F is the lung expansion value; W1 is the lung expansion value influence coefficient, and its value range is from 0 to 1; S is the airway stenosis value; W2 is the airway stenosis value influence coefficient, and its value range is from 0 to 1; R pat is the patient's breathing frequency, with the unit of minute; R nor is the standard breathing frequency, with the unit of minute; V pat is the patient's vital capacity; V nor is the standard vital capacity; W3 is the breathing intensity influence coefficient; Specifically, the asthma risk value C represents the asthma risk situation of the patient. The larger the asthma risk value C, the more severe the patient's condition. Set a risk threshold one Y1 and a risk threshold two Y2 for the asthma risk value C. When the asthma risk value C is less than Y1, it indicates that the patient's asthma risk is in a low-risk state. When the asthma risk value C is greater than Y1 and less than Y2, it is in a medium-risk state. When the asthma risk value C is greater than Y2, it is in a high-risk state. The asthma risk assessment unit combines the lung expansion value F and the airway stenosis value S, and compares the patient's breathing frequency and vital capacity with the standard data. The obtained asthma risk value C has actual data support, reduces the error of subjective judgment, can accurately evaluate each patient by combining lung tissue data, and classifies the asthma risk value C into a low-risk state, a medium-risk state, and a high-risk state. Treatment plans are formulated according to different risk states, enabling it to effectively meet the needs of each patient and improve the quality of asthma treatment.

[0020] The specific treatment processes for the low-risk state, medium-risk state, and high-risk state are as follows: Low-risk state: In the low-risk state, the patient's asthma is well controlled, the condition is relatively stable, and acute exacerbations are less likely to occur. The treatment goal is to maintain the existing symptom control, use inhaled corticosteroids at the lowest standard dose for long-term control of airway inflammation, and conduct regular follow-up visits to check lung function; Medium-risk state: The patient's asthma control is poor, and occasional symptom exacerbations or mild airway inflammation may occur. At this time, inhaled corticosteroids are used at a medium standard dose and are used in combination with long-acting bronchodilators to help relieve bronchospasm and control long-term symptoms. If occasional symptom exacerbations occur, oral or intravenous corticosteroids are provided, and the asthma control situation, including symptoms, dyspnea, nocturnal symptoms, etc., is evaluated monthly; High-risk state: The patient's asthma control is poor, with frequent acute attacks or symptom exacerbations, relatively severe airway inflammation, and the condition is in a relatively dangerous stage. At this time, inhaled corticosteroids are used at the highest standard dose and are combined with long-acting bronchodilators and biologics. For patients with severe allergic asthma or those who do not respond to conventional treatments, the use of biologics can effectively reduce airway inflammation. During acute attacks or symptom exacerbations, the use of short-acting bronchodilators is strengthened, and short-acting anticholinergic drugs are used to synergistically dilate the airway, and the patient's lung condition is regularly examined.

[0021] Furthermore, when the asthma risk value C is greater than Y1 and less than Y2, indicating a medium-risk state, it means that the patient's airway has shown mild stenosis and irregular changes. At this time, the lung density influence coefficient α is increased, and the α value is adjusted to 0.9.

[0022] Specifically, in the medium-risk state, the patient's asthma control is poor, and occasional symptom exacerbations or mild airway inflammation may occur. At this time, the lung density influence coefficient α is increased to adjust the influence of lung tissue density on the lung expansion value F, so that the lung expansion unit can capture subtle changes in the condition and improve the accuracy of treatment decisions.

[0023] Furthermore, the data acquisition module includes a preprocessing unit, and the preprocessing unit includes image denoising, image enhancement, and image standardization.

[0024] Specifically, image denoising uses mean filtering to remove noise in the lung image, image enhancement uses contrast enhancement and edge sharpening techniques to improve the quality of the lung image, and image standardization standardizes the size and resolution of the lung image for subsequent calculation of lung tissue data to ensure the accuracy of the data.

[0025] Furthermore, the visualization module is used to display the lung data in the form of charts for doctors to view.

[0026] Specifically, the visualization module can dynamically display the changes in the patient's condition. By comparing the pulmonary imaging data at different time points, it helps doctors track the progress of the condition in real time. Doctors can evaluate the treatment effect and adjust the treatment plan by comparing the airway diameter and the changes in the lung tissue in the images. Doctors can directly view the pulmonary imaging data, quickly identify pathological changes such as airway stenosis, changes in lung structure, and inflammatory areas in patients, and improve the efficiency of pulmonary image processing.

[0027] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An asthma lung image processing system based on lung tissue data, characterized in that, It includes a data acquisition module, a lung segmentation module, a feature extraction module, a visualization module, a lesion analysis module, and a data storage module; The data acquisition module is used to obtain the imaging data of patients from the hospital database; The lung segmentation module is used to segment the imaging data to obtain the lung region, and then the feature extraction module is used to extract features from the lung region to obtain lung data. The visualization module is used to visualize the lung data for doctors to understand; The lesion analysis module is used to judge the patient's condition based on the lung data. The lesion analysis module includes a lung expansion unit, an airway evaluation unit, and an asthma risk assessment unit; The lung expansion unit obtains the lung expansion value F according to the change in lung area under different breathing states. The airway evaluation unit compares the cross-sectional area of the patient's airway with the standard value to obtain the airway stenosis value S, which is used to understand the patient's airway obstruction situation. The asthma risk assessment unit is used to analyze the patient's asthma situation by combining the airway stenosis value S and the lung expansion value F to obtain the asthma risk value C. A risk threshold one Y1 and a risk threshold two Y2 are set for the asthma risk value C, and the asthma risk value C is classified according to the set thresholds.

2. The asthma lung image processing system based on lung tissue data according to claim 1, wherein: The process of obtaining the lung expansion value by the lung expansion unit is as follows: ； Where F is the lung expansion value; A exp is the area of the lung region in a deep breathing state; A ori is the area of the lung region in the normal breathing state; P lung is the density of lung tissue; P air is the air density; α is the lung density influence coefficient, and its value is 0.7; The lung expansion value L represents the patient's lung expansion ability. When the lung expansion value L is greater than 1, it means that the expansion ability of the lung region is strong, that is, under deep breathing, the expanded area of the lung is significantly larger than that in the normal state, indicating good lung elasticity. The smaller the lung expansion value L is, the worse the expansion ability of the lung region is.

3. The asthma lung image processing system based on lung tissue data according to claim 2, characterized in that: The evaluation process of the airway evaluation unit is as follows: ； Where S is the airway stenosis value; H i,pat is the cross-sectional area of the patient's airway at position i; H i,nor is the standard cross-sectional area of the airway at position i; D pat is the airway length of the patient; D nor is the standard airway length; L pat is the airway diameter of the patient; L nor is the standard airway diameter; β is the airway length influence coefficient, and its value range is from 0 to 1; γ is the airway diameter influence coefficient, and its value range is from 0 to 1; The airway stenosis value S represents the stenosis degree of the patient's airway. The smaller the airway stenosis value S is, the smaller the change range of the cross-sectional area of the patient's airway is, the lighter the stenosis degree is, and the normal morphological characteristics of the airway. The larger the airway stenosis value S is, the larger the change range of the cross-sectional area of the patient's airway is, and the more severe the stenosis degree is.

4. The asthma lung image processing system based on lung tissue data according to claim 3, wherein: The evaluation process of the asthma risk assessment unit is as follows: ； Where C is the asthma risk value; F is the lung expansion value; W1 is the lung expansion value influence coefficient, and its value range is from 0 to 1; S is the airway stenosis value; W2 is the airway stenosis value influence coefficient, and its value range is from 0 to 1; R pat is the patient's respiratory rate, in breaths per minute; R nor is the standard respiratory rate, in units of per minute; V pat is the vital capacity of the patient; V nor is the standard vital capacity; W3 is the breathing intensity influence coefficient, and its value range is from 0 to 1; The asthma risk value C represents the patient's asthma risk situation. The larger the asthma risk value C is, the more serious the patient's condition is. A risk threshold one Y1 and a risk threshold two Y2 are set for the asthma risk value C. When the asthma risk value C is less than Y1, it means that the patient's asthma risk is in a low-risk state. When the asthma risk value C is greater than Y1 and less than Y2, it is in a medium-risk state. When the asthma risk value C is greater than Y2, it is in a high-risk state.

5. The asthma lung image processing system based on lung tissue data according to claim 4, characterized in that: When the asthma risk value C is in the medium-risk state where it is greater than Y1 and less than Y2, it indicates that the patient's airway has already shown mild stenosis and irregular changes. At this time, increase the lung density influence coefficient α and adjust the α value to 0.9 to adjust the influence of lung tissue density on the lung expansion value F.

6. The asthma lung image processing system based on lung tissue data according to claim 1, characterized in that: The data acquisition module includes a preprocessing unit, and the preprocessing unit includes image denoising, image enhancement, and image normalization.

7. The asthma lung image processing system based on lung tissue data according to claim 1, characterized in that: After the feature extraction module extracts features from the lung region and obtains the lung data, the visualization module is used to display the lung data in the form of a chart for doctors to view.

8. The asthma lung image processing system based on lung tissue data according to claim 1, wherein: The data storage module is used to store imaging data, lung data, and the data generated by the lesion analysis module, and uses distributed storage for storage.

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