CN113808181A - Medical image processing method, electronic device and storage medium - Google Patents
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Abstract
The invention discloses a medical image processing method, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring target case images of the same patient; acquiring a reference image in the target case image; registering other images in the target case image to the reference image to obtain a registration result; and displaying the reference image and the registration result. The invention realizes that all case images are registered to the same posture, and then the reference images and the registration results are sequentially displayed according to the set sequence, thereby improving the image registration efficiency and the robustness of the registration effect, facilitating the timely check and the integral analysis of the patient's condition by a doctor, being capable of quickly knowing and mastering the change trend of the current patient's condition, greatly improving the film reading efficiency of the doctor, optimizing the diagnosis process and effectively improving the experience of the doctor in reading the film.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to a medical image processing method, an electronic device, and a storage medium.
Background
In recent years, the rapid advance of deep learning technology has particularly achieved breakthrough development in the fields of computer vision and medical images, and the computer-aided diagnosis technology based on artificial intelligence provides an image reading system for accurate and detailed quantitative information of a focus, so that radiologists and clinicians are helped to improve daily work efficiency and diagnosis accuracy. However, in a doctor working scene, a patient often has a plurality of cases, a radiologist needs to refer to a follow-up comparison condition and a follow-up suggestion of the patient in a patient image report, the radiologist needs to compare the cases by manually comparing the cases and turn over the images layer by layer to compare the cases, the change trend of a focus, a related tissue and an interested region is preliminarily obtained, a rough quantitative information can be obtained through repeated manual measurement, and then the language is organized to write into the patient image report.
Disclosure of Invention
The invention aims to solve the technical problems that a mainstream film reading system in the prior art only can support some rough one-to-one follow-up image display, only can roughly analyze follow-up images, is not intuitive in display form, influences judgment of doctors and seriously influences interaction experience and working efficiency of users; in the follow-up process of a doctor, the focus can be adjusted according to the disease condition or the personal habit of the doctor, for example, the focus can flow, and the diagnosis process of the doctor can be greatly accelerated by an interactive mode of aligning and linking the focus. These film reading systems are all aligned based on images, and are difficult to meet the needs of doctors. In addition, after the image diagnosis is completed, the doctor needs to summarize the disease condition and organize the language to edit a diagnosis report, and the process undoubtedly has the problems of increasing the workload and time consumption of the doctor, namely the defect that the existing medical image processing mode cannot meet the actual film reading requirement of the doctor, and provides a medical image processing method, an electronic device and a storage medium.
The invention solves the technical problems through the following technical scheme:
the invention provides a medical image processing method, which comprises the following steps:
acquiring target case images of the same patient;
wherein the target case image comprises a current case image and/or a number of historical case images;
acquiring a reference image in the target case image;
registering other images in the target case image to the reference image to obtain a registration result;
and displaying the reference image and the registration result.
Preferably, the acquiring a reference image in the target case image includes:
calculating to obtain a reference image corresponding to the target case image;
traversing and calculating the target similarity between each image in the target case image and the reference image;
and selecting an image of which the target similarity meets a set condition in the target case image as the reference image.
Preferably, the calculating to obtain the reference image corresponding to the target case image includes:
performing binarization processing on each image in the target case image to obtain a first case image after binarization;
calculating the first case image after binarization to obtain a corresponding target average image;
and taking the target average image as the reference image.
Preferably, the calculating to obtain the reference image corresponding to the target case image includes:
performing binarization processing on each image in the target case image by adopting a group of set window width and set window level to obtain a first processing result;
calculating to obtain first average images corresponding to all the first processing results;
and taking the first average image as the reference image.
Preferably, the calculating to obtain the reference image corresponding to the target case image includes:
carrying out binarization processing on each image in the target case image by adopting a plurality of groups of set window widths and set window levels to obtain a plurality of groups of second processing results;
calculating to obtain a second average image corresponding to each group of second processing results;
and taking each second average image as the reference image to acquire a plurality of reference images.
Preferably, the calculating to obtain the reference image corresponding to the target case image includes:
carrying out average processing on the target case image to obtain the reference image;
the traversing calculates a target similarity between each of the target case images and the reference image, including:
calculating the absolute error rate between each image in the target case image and the reference image in a traversing manner;
selecting a case image with the similarity smaller than a set threshold value in the target case image as the reference image, wherein the selecting comprises the following steps:
and selecting the target case image corresponding to the minimum absolute error rate as the reference image.
Preferably, the displaying the reference image and the registration result includes:
acquiring time information corresponding to each image in the target case image;
sequentially displaying the registration result corresponding to each image in the reference image and the target case image according to the time information;
and/or the presence of a gas in the gas,
the method further comprises the following steps:
acquiring an external operation instruction;
matching according to the external operation instruction to obtain a corresponding target focus;
and acquiring the registration result and a focus image corresponding to the target focus in the reference image, and displaying the focus image.
Preferably, the displaying the reference image and the registration result further includes:
acquiring an analysis result corresponding to the current case image and/or the historical case image;
wherein the analysis result comprises at least one of overall disease evaluation information, follow-up time information and focus quantification information at each follow-up visit;
acquiring habit data of a user for viewing an image report;
generating a corresponding target image report based on the habit data and the analysis result;
and/or the presence of a gas in the gas,
when the target case image comprises a current case image, the method further comprises:
processing the current case image to generate a focus image and corresponding focus quantitative information;
comparing the focus quantitative information with a symptom preset value, and generating prompt information when the focus quantitative information is greater than or equal to the symptom preset value;
the prompt information comprises case information representing the severity of the patient condition and/or triage information corresponding to the case information.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the medical image processing method described above when executing the computer program.
The invention also provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of processing a medical image as described above.
On the basis of the common knowledge in the field, the preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows:
according to the invention, the reference image is obtained from the current case image and the historical case image, other case images in all case images are registered to the reference image so as to realize that all case images are registered to the same posture, and then the reference image and the registration result are displayed in sequence according to the set sequence, so that the image registration efficiency and the robustness of the registration effect are improved, and meanwhile, a doctor can conveniently check and integrally analyze the state of an illness of a patient in time, the change trend of the state of the illness of the current patient can be rapidly known and mastered, the film reading efficiency of the doctor is improved to a great extent, the diagnosis process is optimized, and the experience of the doctor in reading is effectively improved.
Drawings
Fig. 1 is a flowchart of a medical image processing method according to embodiment 1 of the present invention.
Fig. 2 is a first flowchart of a medical image processing method according to embodiment 2 of the present invention.
Fig. 3 is a second flowchart of a medical image processing method according to embodiment 2 of the present invention.
Fig. 4 is a third flowchart of a medical image processing method according to embodiment 2 of the present invention.
Fig. 5 is a fourth flowchart of the medical image processing method according to embodiment 2 of the present invention.
Fig. 6 is a fifth flowchart of a medical image processing method according to embodiment 2 of the present invention.
Fig. 7 is a sixth flowchart of a medical image processing method according to embodiment 2 of the present invention.
Fig. 8 is a seventh flowchart of the medical image processing method according to embodiment 2 of the present invention.
Fig. 9 is an eighth flowchart of a medical image processing method according to embodiment 2 of the present invention.
Fig. 10 is a schematic view of a display interface after registration of a medical image in embodiment 2 of the present invention.
Fig. 11 is a schematic view of a target image report according to embodiment 2 of the present invention.
Fig. 12 is a block diagram schematically showing a medical image processing system according to embodiment 3 of the present invention.
Fig. 13 is a schematic structural diagram of an electronic device implementing a medical image processing method according to embodiment 5 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the scope of the invention.
Example 1
As shown in fig. 1, the medical image processing method of the present embodiment includes:
s101, acquiring a target case image of the same patient;
wherein the target case image comprises a current case image and/or a plurality of historical case images, and the case images comprise but are not limited to corresponding brain CT (computed tomography) images during follow-up examination of intracranial hemorrhage of a patient.
S102, acquiring a reference image in the target case image;
s103, registering other images in the target case image to a reference image to obtain a registration result;
s104, displaying the reference image and the registration result;
the current case image after the registration processing and the historical case image are compared and displayed in the same floating window, and the method can be but not limited to a secondary window, a side bar and other display modes.
In addition, the comparison of the analysis results between the current case image and the historical case image is in the form of a table and a line graph, and the comparison also comprises but is not limited to various expression modes such as a bar graph, a pie chart, a character description and the like.
In the embodiment, the reference image is obtained from the current case image and the historical case image, and other case images in all case images are registered to the reference image, so that all case images are registered to the same posture, and then the reference image and the registration result are sequentially displayed according to the set sequence, so that the image registration efficiency and the robustness of the registration effect are improved, a doctor can conveniently check and integrally analyze the state of an illness of a patient in time, the change trend of the state of the illness of the current patient can be quickly known and mastered, the film reading efficiency of the doctor is improved to a great extent, the diagnosis process is optimized, and the experience of the doctor in reading is effectively improved.
Example 2
The medical image processing method of the present embodiment is a further improvement of embodiment 1, specifically:
in an embodiment, as shown in fig. 2, step S102 includes:
s1021, calculating to obtain a reference image corresponding to the target case image;
s1022, traversing and calculating the target similarity between each image in the target case image and the reference image;
and S1023, selecting an image of which the target similarity meets the set condition in the target case image as a reference image.
The method comprises the steps of obtaining reference images corresponding to all case images through calculation, then calculating the similarity between each case image and the reference image (representing the difference or distance between any two compared case images), selecting the image with the similarity larger than a set value as a registration reference point (for example, selecting the image with the similarity larger than 98%, the set value can be determined and adjusted according to actual requirements), and registering other case images to the reference point.
In an embodiment, as shown in fig. 3, step S1021 includes:
s102111, performing binarization processing on each image in the target case image to obtain a binarized first case image;
s102112, calculating the first case image after binarization, and acquiring a corresponding target average image;
s102113, the target average image is set as a reference image.
The method comprises the steps of calculating to obtain a corresponding target average image by carrying out binarization processing on a case image and then carrying out summation averaging or weighted averaging on all images subjected to binarization processing, and then taking the target average image as a reference image so as to quickly obtain the reference image capable of representing all case images, thereby ensuring the registration efficiency and accuracy.
In an embodiment, as shown in fig. 4, step S1021 includes:
s102121, carrying out binarization processing on each image in the target case image by adopting a set of set window width and set window level to obtain a first processing result;
s102122, calculating to obtain first average images corresponding to all first processing results;
s102123, taking the first average image as a reference image;
step S1022 includes:
s102211, traversing and calculating the target similarity between the first case image after binarization in the target case image and the reference image;
the target similarity corresponds to an average absolute difference, an intersection ratio, a euclidean distance, an absolute error rate, or the like.
By adopting a set of set window width and set window level (such as the window level 800HU, the window width 800HU is actually equivalent to the threshold 400 for binarization, HU is a Hausfeld unit), all case images are binarized, then all images after binarization processing are summed, averaged or weighted and averaged, a corresponding target average image is obtained by calculation, then the target average image is used as a reference image, at the moment, the target average image corresponds to a reference image, then the similarity between all case images and the reference image is calculated in a traversing manner, and all case images are registered to the same posture, so that the registration effect and efficiency are ensured.
In an embodiment, as shown in fig. 5, step S1021 includes:
s102131, carrying out binarization processing on each image in the target case image by adopting multiple groups of set window widths and set window levels to obtain multiple groups of second processing results;
s102132, calculating to obtain a second average image corresponding to each group of second processing results;
s102133, taking each second average image as a reference image, and acquiring a plurality of reference images;
step S1022 includes:
s102221, calculating to obtain first similarity between the binarized first case image and each of the plurality of reference images;
s102222, calculating to obtain a third average image corresponding to all the first similarity, and taking the third average image as the target similarity corresponding to the current first case image;
wherein the target similarity corresponds to an average absolute difference, an intersection ratio, a Euclidean distance, or an absolute error rate.
Carrying out binarization processing on all case images by adopting a plurality of groups of set window widths and set window levels (for example, respectively adopting the window levels of 700HU, 800HU and 900HU), further carrying out summation and averaging or weighted average and equalization on all images after binarization processing, calculating to obtain an average image corresponding to each group, further taking each average image as a reference image, and corresponding to a plurality of reference images at the moment; and then for the same case image in all the case images, calculating the similarity between the same case image and each reference image, calculating the average value corresponding to the similarities to be used as the target similarity corresponding to the current case image, and repeating the steps in the same way to calculate each case image in all the case images so as to calculate the target similarity corresponding to each case image.
For example, the following steps are carried out: for all case images (100), carrying out binarization processing on the 100 case images by adopting three groups of set window widths and set window levels (700 HU, 800HU and 900HU respectively), and summing and averaging the images after binarization processing to obtain three groups of average images, namely three reference images (an image A, an image B and an image C) are obtained at the moment; for any one of 100 case images, three similarities (a, B and C) between the same case image and three reference images (image a, image B and image C) need to be calculated respectively, and then the three similarities are summed up and averaged to obtain a corresponding average value avg ═ a + B + C)/3, which is used as a target similarity corresponding to the current case image; and analogizing in turn, and calculating to obtain the corresponding target similarity of each case image in the 100 images.
The method and the device have the advantages that the calculation accuracy of the similarity corresponding to each case image is improved through the acquisition of a plurality of reference images, the accuracy of the finally determined reference image is further ensured, and the integral registration accuracy and the robustness of the registration effect are improved.
In an embodiment, as shown in fig. 6, step S1021 includes:
s102141, carrying out average processing on the target case image to obtain a reference image;
step S1022 includes:
s102231, traversing and calculating an absolute error rate between each image in the target case image and the reference image;
step S1023 includes:
and S10231, selecting a case image corresponding to the minimum absolute error rate as a reference image.
The method includes the steps of directly carrying out average processing on all case images to obtain reference images, then calculating the absolute error rate of each image and the reference image in a traversing mode, and selecting the image with the minimum absolute error rate as a reference image, so that the acquisition accuracy of the reference images is guaranteed, the acquisition efficiency of the reference images is improved, and the overall registration efficiency is improved.
In an embodiment, as shown in fig. 7, step S104 includes:
s1041, acquiring time information corresponding to each image in the target case image;
and S1042, sequentially displaying the registration result corresponding to each image in the reference image and the target case image according to the time information.
According to the follow-up time corresponding to all case images, the case images after registration processing are sequentially displayed according to the time sequence, so that doctors can conveniently check one by one, the communication description of the state of an illness and the current patient corresponding to different follow-up time points is combined, the change trend of the state of the illness of the current patient can be rapidly known and mastered, the film reading efficiency of the doctors is greatly improved, and the diagnosis process is optimized.
In an embodiment, as shown in fig. 8, after step S104, the method further includes:
s105, acquiring an external operation instruction;
the external operation instruction comprises click, character input, voice input and other modes.
S106, matching according to an external operation instruction to obtain a corresponding target focus;
and S107, acquiring a registration result and a focus image corresponding to the target focus in the reference image, and displaying the focus image.
Considering that in the follow-up process of a doctor, the focus can be adjusted according to the disease condition or the personal habit of the doctor, such as some flowing focuses, at the moment, the doctor can input a corresponding operation instruction, for example, clicking the position of a target focus in any case image in a display interface or clicking an access link associated with the target focus, automatically determining the target focus which the doctor needs to check currently, locally amplifying and displaying the current case image, displaying according to the focus center, simultaneously controlling other remaining case images in a linkage manner and displaying the target focus accordingly, facilitating the diagnosis and analysis of the change condition of a specific focus by the doctor, meeting the requirements of performing overall diagnosis and analysis on the whole diagnosis part and the specific diagnosis of any local focus, effectively meeting different diagnosis requirements and diagnosis habits of different doctors, greatly improving the film reading experience of doctors.
In an embodiment, as shown in fig. 9, after step S104, the method further includes:
s108, acquiring an analysis result corresponding to the current case image and/or the historical case image;
wherein, the analysis result comprises overall evaluation information of the disease condition, follow-up visit time information, focus quantification information at each follow-up visit and the like;
s109, acquiring habit data of a user for viewing the image report;
and S1010, generating a corresponding target image report based on the habit data and the analysis result.
All case images have corresponding analysis results, the analysis results and the report viewing habit of doctors are combined to automatically generate a structured image report matched with the current doctors, manual filling processing of doctors is not needed, the report processing flow is greatly simplified, and the doctor can conveniently copy and use the case images at the later stage or directly modify the case images based on the generated image report to form the image report more conforming to the requirements of the current doctors; of course, when the analysis result changes, the image can be automatically updated based on the changed analysis result, the automation degree of image report acquisition is improved, the labor input cost is reduced, and the accuracy of the image report is ensured.
By means of additionally arranging image acquisition equipment (such as a camera) or fingerprint acquisition equipment and the like, user information of a current doctor is acquired in time, and expression habit data of an image report matched with the user information is obtained by inquiring and matching in a database based on the user information. When the habit data of the doctor for viewing the image report changes, the corresponding recorded information can be updated in time, so that the subsequently generated image report is more appropriate to the current doctor, and the use experience of the whole film reading process of the doctor is more effectively ensured.
Taking the brain CT image corresponding to the patient intracranial hemorrhage follow-up film reading as an example, as shown in fig. 10, the reference image and the registration node can be displayed in a display window in a linkage manner, and information such as follow-up time, an analysis result, case quantitative data and the like corresponding to each case image can be displayed at the corresponding image position to guide a doctor to view and analyze, and the follow-up time, the analysis result, the case quantitative data and the like can be easily distinguished, so that confusion is not easy to generate.
As shown in fig. 11, in the current image diagnosis page, a structured image report of intracranial hemorrhage image-assisted diagnosis is provided according to the expression habit of the doctor.
In addition, the structured image report includes, but is not limited to, an analysis result of the current case image, an analysis result corresponding to the historical case image at different historical follow-up time points, or a combination of the analysis result of the current case image and the analysis result corresponding to the historical case image, and may be determined or adjusted according to actual needs.
In an embodiment, when the target case image includes a current case image, the method for processing a medical image of the present embodiment further includes:
processing the current case image to generate a focus image and corresponding focus quantitative information;
wherein, the lesion quantitative information is used for characterizing the parameter information of the specific lesion condition of the patient, and the lesion quantitative information includes but is not limited to the lesion type, the lesion level, the lesion position, the lesion volume and the lesion CT value.
Typesetting and displaying the focus image; the typesetting and displaying modes can be adjusted and set according to actual requirements.
The lesion images corresponding to different patients are visually displayed according to a certain typesetting format through the film reading system, so that doctors can directly check the diseased conditions of the patients without other complicated operations, the operation flow is simplified, and the overall film reading efficiency is improved.
Comparing the focus quantitative information with a symptom preset value, and generating prompt information when the focus quantitative information is greater than or equal to the symptom preset value;
the preset symptom value includes, but is not limited to, a value for determining whether the disease state of the patient reaches a critical state (critical value), a value for determining that the patient has a certain disease state (positive) (lesion characterization value), and the like, and may be preset by the hospital according to actual experience. The prompt information comprises case information representing the severity of the patient condition and/or triage information corresponding to the case information.
By comparing the focus quantitative information of each patient with a symptom preset value in time, once the focus quantitative information is greater than or equal to the symptom preset value, the current situation that the patient is a patient with critical hidden danger or in a critical state is indicated, and a doctor needs to perform timely intervention treatment; reach the report requirement of hospital this moment, specifically through in time triggering information prompt module propelling movement suggestion message for doctor terminal, in time pop out the case information and the triage information of pending patient in doctor terminal to inform the doctor in the very first time and handle urgent patient and let the patient get into the purpose of diagnosing the flow in the very first time, thereby improved the treatment effeciency of critical flow in the hospital effectively, avoided current because the manual mode handles uncertainty and randomness that exists, accord with the demand of hospital to critical flow more.
And the doctor terminal pushes prompt information and synchronously and visually displays case information and triage information in the processing system so as to facilitate the doctor and other related personnel to further check and process.
The prompting interface can correspond to a window gadget, the interaction form can adopt a suspension component form or other forms, and the prompting part can be composed of elements which are convenient to distinguish and warn, such as numbers, colors, characters and the like, for example, the number of patients needing to be treated by a doctor is displayed through numbers, and whether the state of the patients is critical or not is displayed through colors.
In addition, the corresponding case information and triage information can be reported on the doctor terminal through the prompting mode.
The medical image processing method of the embodiment further includes:
and generating and displaying patient list information when the lesion quantitative information is greater than or equal to a symptom preset value, and displaying detailed information corresponding to the patient list information.
That is, the information of the patients needing to be treated within a period of time is listed in the form of a list, and the list contains the basic information of the patients, the disease condition and other specific information.
And summarizing and generating list information of all patients needing to be processed in a triage processing system, so that doctors can further check the case information corresponding to each patient in detail based on the list information, and further diagnose and treat the patients based on the checked content.
Specifically, the doctor clicks the patient list information displayed in the display screen of the triage processing system, and then directly jumps to the radiographing system to view the case results of different patients in detail for subsequent diagnosis.
The patient list information includes, but is not limited to, disease type information, part information, type information, criticality information, time information, processing status information, and department information corresponding to each patient, and these information may be filtered through a filtering box to specifically display the contents to be displayed.
The medical image processing method has the advantages that the problem that doctors are difficult to search due to the fact that the number of cases of patients is large is avoided for the doctors, and general operation functions such as disease types and timing zero clearing can be set, so that the medical image processing flow in hospitals is further optimized, and the medical image processing efficiency is improved.
In the embodiment, the reference image is obtained from the current case image and the historical case image, and other case images in all case images are registered to the reference image, so that all case images are registered to the same posture, and then the reference image and the registration result are sequentially displayed according to the set sequence, so that the image registration efficiency and the robustness of the registration effect are improved, a doctor can conveniently check and integrally analyze the state of an illness of a patient in time, the change trend of the state of the illness of the current patient can be quickly known and mastered, the film reading efficiency of the doctor is improved to a great extent, the diagnosis process is optimized, and the experience of the doctor in reading the film is effectively improved; meanwhile, all case images have corresponding analysis results and the habit of checking reports by doctors are combined to automatically generate a structured image report matched with the current doctor, manual filling processing by the doctor is not needed, the report processing flow is greatly simplified, later copying and use by the doctor are facilitated, the automation degree of image report acquisition is improved, the manual input cost is reduced, and the accuracy of the image report is ensured.
Example 3
As shown in fig. 12, the medical image processing system of the present embodiment includes:
the target case image acquisition module 1 is used for acquiring target case images of the same patient;
wherein the target case image comprises a current case image and/or a plurality of historical case images;
a reference image obtaining module 2, configured to obtain a reference image in the target case image;
a registration result obtaining module 3, configured to perform registration on other images in the target case image to the reference image, so as to obtain a registration result;
and the display module 4 is used for displaying the reference image and the registration result.
In the embodiment, the reference image is obtained from the current case image and the historical case image, the other case images in all the case images are registered to the reference image, so that all the case images are registered to the same posture, and then the reference image and the registration result are sequentially displayed according to the set sequence, so that the image registration efficiency and the robustness of the registration effect are improved, a doctor can conveniently check and integrally analyze the state of an illness of a patient in time, and the experience of reading the film by the doctor is effectively improved.
Example 4
The medical image processing system of the present embodiment is a further improvement of embodiment 3, specifically:
in an embodiment, the reference image obtaining module 2 includes:
the reference image acquisition unit is used for calculating and obtaining a reference image corresponding to the target case image;
the target similarity calculation unit is used for calculating the target similarity between each image in the target case image and the reference image in a traversing manner;
and the reference image acquisition unit is used for selecting an image of which the target similarity meets the set condition in the target case image as a reference image.
In an aspect of an embodiment, the reference image acquiring unit includes:
the first case image acquiring subunit is used for carrying out binarization processing on each image in the target case image to acquire a binarized first case image;
the target average image acquisition subunit is used for calculating the first case image after binarization to acquire a corresponding target average image;
and the reference image acquisition subunit is used for taking the target average image as a reference image.
In an aspect of an embodiment, the reference image acquiring unit includes:
a first processing result obtaining subunit, configured to perform binarization processing on each image in the target case image by using a set of set window width and set window level to obtain a first processing result;
the first average image acquisition subunit is used for calculating to obtain first average images corresponding to all the first processing results;
a reference image acquisition subunit configured to take the first average image as a reference image;
the target similarity calculation unit is used for calculating the target similarity between the first case image after binarization and the reference image in the target case image in a traversing manner;
wherein the target similarity corresponds to an average absolute difference, an intersection ratio, a Euclidean distance, or an absolute error rate.
In an aspect of an embodiment, the reference image acquiring unit includes:
a second processing result obtaining subunit, configured to perform binarization processing on each image in the target case image by using multiple sets of set window widths and set window levels to obtain multiple sets of second processing results;
the second average image acquisition subunit is used for calculating to obtain a second average image corresponding to each group of second processing results;
a reference image acquisition subunit configured to acquire a plurality of reference images using each of the second average images as a reference image;
the target similarity calculation unit includes:
the first similarity measurement operator unit is used for calculating and obtaining first similarities between the same binarized first case image and the plurality of reference images respectively;
the target similarity obtaining subunit is used for calculating to obtain a third average image corresponding to all the first similarities, and taking the third average image as the target similarity corresponding to the current first case image;
the target similarity corresponds to an average absolute difference, an intersection ratio, a euclidean distance, an absolute error rate, or the like.
In an embodiment, the reference image acquiring unit is configured to perform averaging processing on the target case image to acquire a reference image;
the target similarity calculation unit is used for calculating the absolute error rate between each image in the target case image and the reference image in a traversing manner;
the reference image acquisition unit is used for selecting a case image corresponding to the minimum absolute error rate as a reference image.
In an embodiment, the display module 4 comprises:
the time information acquisition unit is used for acquiring time information corresponding to each image in the target case image;
and the display unit is used for sequentially displaying the registration result corresponding to each image in the reference image and the target case image according to the time information.
In an embodiment, the system for processing a medical image of the present embodiment further includes:
an external operation instruction acquisition block for acquiring an external operation instruction;
the target focus determining module is used for obtaining a corresponding target focus according to external operation instruction matching;
the display module 4 is further configured to obtain the registration result and a lesion image corresponding to the target lesion in the reference image, and display the lesion image.
In an embodiment, the system for processing a medical image of the present embodiment further includes:
the analysis result acquisition module is used for acquiring the analysis result corresponding to the current case image and/or the historical case image;
wherein the analysis result comprises at least one of overall disease evaluation information, follow-up visit time information and focus quantification information at each follow-up visit;
the habit data acquisition module is used for acquiring habit data of a user for viewing the image report;
the target image report generation module is used for generating a corresponding target image report based on the habit data and the analysis result;
in an embodiment, when the target case image includes a current case image, the processing system of medical images of the present embodiment further includes:
the system comprises a focus quantitative information generation module, a focus quantitative information acquisition module and a focus quantitative information acquisition module, wherein the focus quantitative information generation module is used for processing a current case image to generate a focus image and corresponding focus quantitative information;
the information comparison module is used for comparing the focus quantitative information with a symptom preset value, and calling the prompt information generation module to generate prompt information when the focus quantitative information is greater than or equal to the symptom preset value;
the prompt information comprises case information representing the severity of the disease condition of the patient and/or triage information corresponding to the case information.
It should be noted that the working principle of the medical image processing system of this embodiment is similar to the implementation principle corresponding to the medical image processing method of embodiment 1 or 2, and therefore, no further description is given in this embodiment.
In the embodiment, the reference image is obtained from the current case image and the historical case image, and other case images in all case images are registered to the reference image, so that all case images are registered to the same posture, and then the reference image and the registration result are sequentially displayed according to the set sequence, so that the image registration efficiency and the robustness of the registration effect are improved, a doctor can conveniently check and integrally analyze the state of an illness of a patient in time, the change trend of the state of the illness of the current patient can be quickly known and mastered, the film reading efficiency of the doctor is improved to a great extent, the diagnosis process is optimized, and the experience of the doctor in reading is effectively improved.
Example 5
Fig. 13 is a schematic structural diagram of an electronic device according to embodiment 5 of the present invention. The electronic device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, and the processor implements the processing method of the medical image in embodiment 1 or 2 when executing the program. The electronic device 30 shown in fig. 13 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 13, the electronic device 30 may be embodied in the form of a general purpose computing device, which may be, for example, a server device. The components of the electronic device 30 may include, but are not limited to: the at least one processor 31, the at least one memory 32, and a bus 33 connecting the various system components (including the memory 32 and the processor 31).
The bus 33 includes a data bus, an address bus, and a control bus.
The memory 32 may include volatile memory, such as Random Access Memory (RAM)321 and/or cache memory 322, and may further include Read Only Memory (ROM) 323.
The processor 31 executes various functional applications and data processing, such as the processing method of the medical image in embodiment 1 or 2 of the present invention, by executing the computer program stored in the memory 32.
The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication may be through input/output (I/O) interfaces 35. Also, model-generating device 30 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via network adapter 36. As shown in FIG. 13, network adapter 36 communicates with the other modules of model-generating device 30 via bus 33. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the model-generating device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 6
The present embodiment provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor, implements the steps in the method of processing a medical image in embodiment 1 or 2.
More specific examples, among others, that the readable storage medium may employ may include, but are not limited to: a portable disk, a hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the present invention can also be implemented in the form of a program product including program code for causing a terminal device to execute the steps in the processing method for implementing a medical image in embodiment 1 or 2 when the program product is run on the terminal device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (10)
1. A method of processing a medical image, the method comprising:
acquiring target case images of the same patient;
wherein the target case image comprises a current case image and/or a number of historical case images;
acquiring a reference image in the target case image;
registering other images in the target case image to the reference image to obtain a registration result;
and displaying the reference image and the registration result.
2. The method for processing medical images according to claim 1, wherein the acquiring a reference image in the target case image comprises:
calculating to obtain a reference image corresponding to the target case image;
traversing and calculating the target similarity between each image in the target case image and the reference image;
and selecting an image of which the target similarity meets a set condition in the target case image as the reference image.
3. The method for processing medical images according to claim 2, wherein the calculating to obtain the reference image corresponding to the target case image comprises:
performing binarization processing on each image in the target case image to obtain a first case image after binarization;
calculating the first case image after binarization to obtain a corresponding target average image;
and taking the target average image as the reference image.
4. The method for processing a medical image according to claim 3, wherein the calculating to obtain the reference image corresponding to the target case image includes:
performing binarization processing on each image in the target case image by adopting a group of set window width and set window level to obtain a first processing result;
calculating to obtain first average images corresponding to all the first processing results;
and taking the first average image as the reference image.
5. The method for processing a medical image according to claim 3, wherein the calculating to obtain the reference image corresponding to the target case image includes:
carrying out binarization processing on each image in the target case image by adopting a plurality of groups of set window widths and set window levels to obtain a plurality of groups of second processing results;
calculating to obtain a second average image corresponding to each group of second processing results;
and taking each second average image as the reference image to acquire a plurality of reference images.
6. The method for processing a medical image according to claim 3, wherein the calculating to obtain the reference image corresponding to the target case image includes:
carrying out average processing on the target case image to obtain the reference image;
the traversing calculates a target similarity between each of the target case images and the reference image, including:
calculating the absolute error rate between each image in the target case image and the reference image in a traversing manner;
selecting a case image with the similarity smaller than a set threshold value in the target case image as the reference image, wherein the selecting comprises the following steps:
and selecting the target case image corresponding to the minimum absolute error rate as the reference image.
7. The method for processing medical images according to any one of claims 1-6, wherein said displaying the reference image and the registration result comprises:
acquiring time information corresponding to each image in the target case image;
sequentially displaying the registration result corresponding to each image in the reference image and the target case image according to the time information;
and/or the presence of a gas in the gas,
the method further comprises the following steps:
acquiring an external operation instruction;
matching according to the external operation instruction to obtain a corresponding target focus;
and acquiring the registration result and a focus image corresponding to the target focus in the reference image, and displaying the focus image.
8. The method for processing medical images according to any one of claims 1-6, wherein said displaying the reference image and the registration result further comprises:
acquiring an analysis result corresponding to the current case image and/or the historical case image;
wherein the analysis result comprises at least one of overall disease evaluation information, follow-up time information and focus quantification information at each follow-up visit;
acquiring habit data of a user for viewing an image report;
generating a corresponding target image report based on the habit data and the analysis result;
and/or the presence of a gas in the gas,
when the target case image comprises a current case image, the method further comprises:
processing the current case image to generate a focus image and corresponding focus quantitative information;
comparing the focus quantitative information with a symptom preset value, and generating prompt information when the focus quantitative information is greater than or equal to the symptom preset value;
the prompt information comprises case information representing the severity of the patient condition and/or triage information corresponding to the case information.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of processing a medical image according to any one of claims 1-8 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of processing a medical image of any one of claims 1 to 8.
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