CN111613306A - Multi-feature fusion facial paralysis automatic evaluation method - Google Patents
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Abstract
The invention discloses a multi-feature fusion facial paralysis automatic assessment method, which particularly relates to the technical field of medical diagnosis and comprises the following steps: the facial image sample of the facial paralysis patient is obtained, the facial paralysis patient is positioned at the front side of the photographing detection module in front of the patient, the facial photographing module carries out three-dimensional correction on the facial image according to facial features of the patient, the facial image of the patient is corrected from three dimensions of raw, roll and pitch, and the focal length correction is carried out when the head of the patient moves and deviates. According to the method, after the facial image sample of the patient is obtained, the reliability and relevance of the data are effectively improved during comparison of the data after multi-region feature fusion of the face of the patient, the variation precision obtained by comparison is greatly enhanced under comparison of a computer deep learning system through comparison of a plurality of facial region features and multi-feature fusion comparison of the side seat facial region features and the region perception features, the comparison efficiency is improved, meanwhile, compared with the prior art, accurate assessment of the facial paralysis grade of the patient can be effectively obtained, and treatment can be conveniently carried out according to the assessment in follow-up treatment.
Description
Technical Field
The invention relates to the technical field of medical diagnosis, in particular to a multi-feature fusion facial paralysis automatic evaluation method.
Background
Facial paralysis is a common and frequently encountered disease, and the main symptom of facial paralysis is that facial expression muscles cannot perform normal functional actions. Facial paralysis is classified into peripheral facial paralysis and central facial paralysis according to the location of nerve injury. Peripheral facial paralysis is caused by a nucleus and injury, and is manifested as ipsilateral global facial paralysis. This paralysis is caused by wind-cold, ear or meningeal infection, etc. Central facial paralysis, originating from damage above the facial nerve nucleus, manifests itself as paralysis of the lower muscles of the contralateral side, and is more common in cerebrovascular disease.
Although facial paralysis is not life-threatening, it is related to the appearance problem in the social process, which negatively affects the psychology of the patient, facial paralysis can be completely recovered as long as it is found early and treated in time, and the treatment measures are appropriate, and clinically, the facial paralysis patient is evaluated and graded according to the evaluation standard of facial paralysis, and then a proper treatment plan is made according to the grading result. The establishment of an appropriate treatment regimen for a patient is critical to the rehabilitation of facial paralysis, and the establishment of a treatment regimen is based on the grading results. Therefore, the grading evaluation of the facial paralysis has important significance for the rehabilitation therapy of the facial paralysis.
However, in the prior art, because the facial distortion conditions of different patients are different, doctors can only perform self-judgment according to standards, the standards can provide judgment bases, but the judgment of the facial grade still has great subjectivity, and meanwhile, because the facial area characteristics are different, the judgment weights of different areas are different, the doctors cannot well combine various characteristics for judgment, so that the deviation is easy to occur in the facial paralysis grade evaluation process of the patients, and unnecessary influence is brought to subsequent nursing treatment.
Disclosure of Invention
In order to overcome the above defects in the prior art, an embodiment of the present invention provides an automatic assessment method for facial paralysis with multi-feature fusion, and the technical problem to be solved by the present invention is: how to combine the facial multi-region characteristics to carry out fusion judgment to improve the accuracy of grading judgment when judging extension for facial paralysis patients.
In order to achieve the purpose, the invention provides the following technical scheme: a multi-feature fusion facial paralysis automatic evaluation method comprises the following steps:
the method comprises the steps that firstly, a facial image sample of a facial paralysis patient is obtained, the face of the patient is positioned in front of a photographing detection module in an orthographic view, a facial photographing module carries out three-dimensional correction on the facial image according to facial features of the patient, the facial image of the patient is corrected from three dimensions of raw, roll and pitch, focal length correction is carried out when the head of the patient moves and deviates, 10-30 image sets are photographed at intervals of 0.5s, 5-10 images are extracted for image combination, after the image sets are photographed, a voice module synthesizer reads out motion prompts to remind the patient to judge image set photographing, so that the patient can carry out eyebrow lifting, eye closing, tooth showing and cheek bulging operations, and the patient is prompted to sit on the side to photograph facial side information;
establishing a computer deep learning system, establishing a learning system standard database, learning the database standard male and female facial form region characteristic images to obtain a male and female facial form whole region characteristic standard image database, inquiring the perception condition of each characteristic region of a patient face partition after an image set is placed, and inputting the perception condition into the computer learning system;
thirdly, carrying out facial partition on the shot and input image set by the computer deep learning system, sequentially carrying out forehead feature region, eyebrow feature region, eye feature region, nose feature region, cheek feature region and mouth feature region from top to bottom by carrying out region classification on the face of a patient in the image set, and simultaneously extracting distinguishing features from the eyebrow feature region, the eye feature region, the lip feature region and the cheek feature region in eyebrow lifting, eye closing, tooth showing and cheek bulging operations;
step four, obtaining side-sitting face side-shooting characteristics, and judging inclined angle included angles of forehead line characteristic regions, eyebrow characteristic regions, eye characteristic regions, nose characteristic regions, cheek characteristic regions and mouth characteristic regions on two sides of the face according to the positions of central lines of the nose root for the side-shooting information of the deflected side-sitting face;
step five, fusing multiple features for comparison and evaluation, fusing and comparing according to a plurality of input facial region features, regional pain features and side face region included angle features, increasing regional facial paralysis feature value evaluation data due to mutual influence among the multiple features, influencing facial paralysis feature values due to side shooting region feature gradient, and performing transverse cross comparison according to perception feature vectors and data field data, wherein a computer deep learning system firstly performs the following steps on feature image sets of the multiple regions: the facial model comprises a facial model body, a facial model body and a computer deep learning system, and is characterized in that frontal line feature regions, eyebrow feature regions, eye feature regions, nose feature regions and cheek feature regions on two sides are pre-compared, after the facial feature comparison is completed, weighted comparison is performed according to inclination angle included angle features obtained by side shooting features of the feature regions, after the facial feature comparison is completed, score is obtained according to region perception features, side region included angle features, fused image region features and male and female facial feature fusion region features in the computer deep learning system, facial feature and database standard region features, and facial paralysis grade rating of a result model is obtained according to comparison results.
The method comprises the steps of shooting a patient in multiple angles, shooting a plurality of images on a front view face to be integrated to obtain accurate images of the patient, after obtaining facial image samples of the patient, carrying out classification judgment on facial region features of the patient, simultaneously obtaining facial region feature perception through consultation of doctors, carrying out comparison learning on standard image features such as diseased male and female facial paralysis features and non-diseased image features according to a computer deep learning system in the prior art to create a standard database, selecting a large number of standard images to carry out computer data learning to obtain a judgment module with higher accuracy, comparing the extracted facial region features with standard images of the database, carrying out numerical grading on the obtained difference value technology according to H-B grading in the prior standard, and effectively increasing the reliability and relevance of data when comparing multi-region features of the face of the patient after fusion, the comparison of the multiple facial region characteristics and the multi-feature fusion comparison of the side seat facial partition characteristics and the regional perception characteristics greatly enhances the change precision obtained by comparison under the comparison of a computer deep learning system, improves the comparison efficiency, can effectively obtain accurate assessment of the facial paralysis grade of a patient, and facilitates treatment according to the assessment in subsequent treatment.
In a preferred embodiment, the brow feature region includes brow both ends inclination angle region surfaces, brow top region surfaces, and eye region surfaces.
In a preferred embodiment, the nose feature region comprises two side face regions of the nose and a bottom end region of the nose.
In a preferred embodiment, the ocular feature regions comprise an upper ocular region, a lower ocular region, and an canthus region.
In a preferred embodiment, the eye feature region further comprises an eye side and eye angle oblique clip angle.
In a preferred embodiment, the mouth feature region comprises an inner lip region, an outer lip region, and a corner lip region.
In a preferred embodiment, the face side shot information shooting mode is shooting by rotating 90 degrees around both sides of a center line.
In a preferred embodiment, the database standard regional characteristic score is set according to an H-B rating score.
1. The invention obtains the facial image sample of the patient, classifies and judges the facial area characteristics of the patient to obtain the facial area characteristic perception, and the computer deep learning system according to the prior art carries out comparison learning on standard image characteristics such as diseased male and female facial paralysis characteristics and non-diseased image characteristics to create a standard database, when the facial area characteristics are extracted, the standard database is compared with the database standard image, the data after the multi-area characteristic fusion of the facial area characteristics of the patient effectively increases the reliability and the relevance of the data during comparison, the comparison of a plurality of facial area characteristics and the multi-characteristic fusion comparison of the side seat facial area characteristics and the area perception characteristics greatly enhance the change precision obtained by comparison under the comparison of the computer deep learning system, improves the comparison efficiency and simultaneously can effectively obtain the accurate evaluation of the facial paralysis grade of the patient compared with the prior art, treatment of subsequent treatments is facilitated in accordance with this assessment.
Drawings
FIG. 1 is a process flow of the present invention.
FIG. 2 is a flow chart of a step 2 computer deep learning building method of the present invention.
FIG. 3 is a flowchart of a method for performing face segmentation on a set of images in step 3 according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a multi-feature fusion facial paralysis automatic evaluation method, which comprises the following steps:
the method comprises the steps that firstly, a facial image sample of a facial paralysis patient is obtained, the face of the patient is positioned in front of a photographing detection module in an orthographic view, a facial photographing module carries out three-dimensional correction on the facial image according to facial features of the patient, the facial image of the patient is corrected from three dimensions of raw, roll and pitch, focal length correction is carried out when the head of the patient moves and deviates, 10-30 image sets are photographed at intervals of 0.5s, 5-10 images are extracted for image combination, after the image sets are photographed, a voice module synthesizer reads out motion prompts to remind the patient to judge image set photographing, so that the patient can carry out eyebrow lifting, eye closing, tooth showing and cheek bulging operations, and the patient is prompted to sit on the side to photograph facial side information;
establishing a computer deep learning system, establishing a learning system standard database, learning the database standard male and female facial form region characteristic images to obtain a male and female facial form whole region characteristic standard image database, inquiring the perception condition of each characteristic region of a patient face partition after an image set is placed, and inputting the perception condition into the computer learning system;
thirdly, carrying out facial partition on the shot and input image set by the computer deep learning system, sequentially carrying out forehead feature region, eyebrow feature region, eye feature region, nose feature region, cheek feature region and mouth feature region from top to bottom by carrying out region classification on the face of a patient in the image set, and simultaneously extracting distinguishing features from the eyebrow feature region, the eye feature region, the lip feature region and the cheek feature region in eyebrow lifting, eye closing, tooth showing and cheek bulging operations;
step four, obtaining side-sitting face side-shooting characteristics, and judging inclined angle included angles of forehead line characteristic regions, eyebrow characteristic regions, eye characteristic regions, nose characteristic regions, cheek characteristic regions and mouth characteristic regions on two sides of the face according to the positions of central lines of the nose root for the side-shooting information of the deflected side-sitting face;
step five, fusing multiple features for comparison and evaluation, fusing and comparing according to a plurality of input facial region features, regional pain features and side face region included angle features, increasing regional facial paralysis feature value evaluation data due to mutual influence among the multiple features, influencing facial paralysis feature values due to side shooting region feature gradient, and performing transverse cross comparison according to perception feature vectors and data field data, wherein a computer deep learning system firstly performs the following steps on feature image sets of the multiple regions: the facial model comprises a facial model body, a facial model body and a computer deep learning system, and is characterized in that frontal line feature regions, eyebrow feature regions, eye feature regions, nose feature regions and cheek feature regions on two sides are pre-compared, after the facial feature comparison is completed, weighted comparison is performed according to inclination angle included angle features obtained by side shooting features of the feature regions, after the facial feature comparison is completed, score is obtained according to region perception features, side region included angle features, fused image region features and male and female facial feature fusion region features in the computer deep learning system, facial feature and database standard region features, and facial paralysis grade rating of a result model is obtained according to comparison results.
Eyebrow portion characteristic region contains eyebrow portion both ends inclination angle regional face, eyebrow portion top regional face and eye regional face, nose portion characteristic region contains nose portion both sides face region and nose portion bottom region, eye characteristic region contains eye region, lower eye region and canthus region, eye characteristic region still includes the angle of clamping with canthus to the eye both sides, mouth characteristic region contains interior lip region, outer lip region and lip angle region, face portion side is clapped the information shooting mode and is rotated around central line both sides 90 degrees and shoot, database standard regional characteristic score is according to the hierarchical setting of grading of H-B.
As shown in fig. 1 to 3, the embodiment specifically is: the method comprises the steps of shooting a patient in multiple angles, shooting a plurality of images on a face in front view to obtain accurate images of the patient, after obtaining facial image samples of the patient, carrying out classification judgment on facial area features of the patient, damaging motor nerves, obtaining perception of peripheral movement such as eyebrow lifting, eye closing, gill bulging and smile influenced by the facial area features through consultation of doctors, establishing a standard database by carrying out comparison learning on standard image features such as facial paralysis features of sick men and women and image features not sick according to a computer deep learning matlab system in the prior art, selecting a large number of standard images to carry out computer data learning to obtain a judgment module with higher accuracy, comparing the extracted facial area features with standard images of the database, and carrying out numerical grading on the difference value obtained through comparison according to H-B grading in the existing standard, the data after the multi-region feature fusion of the face of the patient effectively increases the reliability and relevance of the data during comparison, the comparison of a plurality of facial region features and the multi-feature fusion comparison of the side seat facial region features and the region perception features greatly enhance the change precision obtained by comparison under the comparison of a computer deep learning system, the comparison efficiency is improved, meanwhile, the accurate assessment of the facial paralysis grade of the patient can be effectively obtained, and the subsequent treatment is convenient to treat according to the assessment.
The points to be finally explained are: first, in the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "mounted," "connected," and "connected" should be understood broadly, and may be a mechanical connection or an electrical connection, or a communication between two elements, and may be a direct connection, and "upper," "lower," "left," and "right" are only used to indicate a relative positional relationship, and when the absolute position of the object to be described is changed, the relative positional relationship may be changed;
secondly, the method comprises the following steps: in the drawings of the disclosed embodiments of the invention, only the structures related to the disclosed embodiments are referred to, other structures can refer to common designs, and the same embodiment and different embodiments of the invention can be combined with each other without conflict;
and finally: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (8)
1. A multi-feature fusion facial paralysis automatic evaluation method is characterized by comprising the following steps:
the method comprises the steps that firstly, a facial image sample of a facial paralysis patient is obtained, the face of the patient is positioned in front of a photographing detection module in an orthographic view, a facial photographing module carries out three-dimensional correction on the facial image according to facial features of the patient, the facial image of the patient is corrected from three dimensions of raw, roll and pitch, focal length correction is carried out when the head of the patient moves and deviates, 10-30 image sets are photographed at intervals of 0.5s, 5-10 images are extracted for image combination, after the image sets are photographed, a voice module synthesizer reads out motion prompts to remind the patient to judge image set photographing, so that the patient can carry out eyebrow lifting, eye closing, tooth showing and cheek bulging operations, and the patient is prompted to sit on the side to photograph facial side information;
establishing a computer deep learning system, establishing a learning system standard database, learning the database standard male and female facial form region characteristic images to obtain a male and female facial form whole region characteristic standard image database, inquiring the perception condition of each characteristic region of a patient face partition after an image set is placed, and inputting the perception condition into the computer learning system;
thirdly, carrying out facial partition on the shot and input image set by the computer deep learning system, sequentially carrying out forehead feature region, eyebrow feature region, eye feature region, nose feature region, cheek feature region and mouth feature region from top to bottom by carrying out region classification on the face of a patient in the image set, and simultaneously extracting distinguishing features from the eyebrow feature region, the eye feature region, the lip feature region and the cheek feature region in eyebrow lifting, eye closing, tooth showing and cheek bulging operations;
step four, obtaining side-sitting face side-shooting characteristics, and judging inclined angle included angles of forehead line characteristic regions, eyebrow characteristic regions, eye characteristic regions, nose characteristic regions, cheek characteristic regions and mouth characteristic regions on two sides of the face according to the positions of central lines of the nose root for the side-shooting information of the deflected side-sitting face;
step five, fusing multiple features for comparison and evaluation, fusing and comparing according to a plurality of input facial region features, regional pain features and side face region included angle features, increasing regional facial paralysis feature value evaluation data due to mutual influence among the multiple features, influencing facial paralysis feature values due to side shooting region feature gradient, and performing transverse cross comparison according to perception feature vectors and data field data, wherein a computer deep learning system firstly performs the following steps on feature image sets of the multiple regions: the facial model comprises a facial model body, a facial model body and a computer deep learning system, and is characterized in that frontal line feature regions, eyebrow feature regions, eye feature regions, nose feature regions and cheek feature regions on two sides are pre-compared, after the facial feature comparison is completed, weighted comparison is performed according to inclination angle included angle features obtained by side shooting features of the feature regions, after the facial feature comparison is completed, score is obtained according to region perception features, side region included angle features, fused image region features and male and female facial feature fusion region features in the computer deep learning system, facial feature and database standard region features, and facial paralysis grade rating of a result model is obtained according to comparison results.
2. The method for automatically assessing facial paralysis through multi-feature fusion according to claim 1, wherein: the eyebrow feature area comprises eyebrow two-end inclination angle area faces, an eyebrow top area face and an eye area face.
3. The method for automatically assessing facial paralysis through multi-feature fusion according to claim 1, wherein: the nose feature region includes two side face regions of the nose and a bottom end region of the nose.
4. The method for automatically assessing facial paralysis through multi-feature fusion according to claim 1, wherein: the ocular feature regions include an upper ocular region, a lower ocular region, and an canthus region.
5. The method of claim 4, wherein the method comprises: the eye feature region further comprises inclined clip angles between two sides of the eye and the eye angle.
6. The method for automatically assessing facial paralysis through multi-feature fusion according to claim 1, wherein: the mouth feature region includes an inner lip region, an outer lip region, and a corner lip region.
7. The method for automatically assessing facial paralysis through multi-feature fusion according to claim 1, wherein: the side shooting information shooting mode of the face part is shooting by rotating around two sides of a central line by 90 degrees.
8. The method for automatically assessing facial paralysis through multi-feature fusion according to claim 1, wherein: and the characteristic score of the standard region of the database is set according to the H-B grading score.
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| CN113488200A (en) * | 2021-07-26 | 2021-10-08 | 平安科技（深圳）有限公司 | Intelligent inquiry method, device, computer equipment and storage medium |
| CN113488200B (en) * | 2021-07-26 | 2023-07-25 | 平安科技（深圳）有限公司 | Intelligent inquiry method, intelligent inquiry device, computer equipment and storage medium |
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