CN113823400A - Method and device for monitoring speed of endoscope withdrawal of intestinal tract and computer readable storage medium - Google Patents
Method and device for monitoring speed of endoscope withdrawal of intestinal tract and computer readable storage medium Download PDFInfo
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
- A61B1/00002—Operational features of endoscopes
- A61B1/00004—Operational features of endoscopes characterised by electronic signal processing
- A61B1/00009—Operational features of endoscopes characterised by electronic signal processing of image signals during a use of endoscope
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
- A61B1/04—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor combined with photographic or television appliances
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B1/00—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
- A61B1/31—Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor for the rectum, e.g. proctoscopes, sigmoidoscopes, colonoscopes
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Abstract
The application provides a method and a device for monitoring the speed of endoscope withdrawal of an intestinal tract and a computer readable storage medium, wherein the method comprises the following steps: identifying a fuzzy image in a first enteroscopy image set corresponding to a pre-acquired target enteroscopy video; removing the fuzzy image in the first enteroscopy image set to obtain a second enteroscopy image set; calculating the endoscope withdrawing speed of the current enteroscopy image in the second enteroscopy image set; and monitoring the endoscope withdrawing speed of the intestinal tract based on the endoscope withdrawing speed of the current enteroscope image and a preset endoscope withdrawing speed threshold value. The application embodiment can avoid the influence on the monitored withdrawal speed when interference conditions such as flushing, adherence and the like are met in the checking process due to the fact that the fuzzy image in the first enteroscope image set corresponding to the target enteroscope video is actively identified and then the fuzzy image in the first enteroscope image set is eliminated to obtain the second enteroscope image set, and the accuracy of the withdrawal speed monitoring is improved.
Description
Technical Field
The application relates to the technical field of medical assistance, in particular to a method and a device for monitoring the speed of intestinal tract endoscope withdrawal and a computer-readable storage medium.
Background
In recent years, colorectal cancer is younger and the incidence rate is gradually increased due to the change of dietary habits of people, such as high calorie and high fat diet, low cellulose intake, sedentary and lack of exercise. The colonoscopy can clearly observe the colorectal mucosal state, and can carry out the pathological and cytological examination of the living body, and the colorectal cancer can be effectively prevented by removing precancerous lesions such as colorectal adenoma and the like. However, the quality of the enteroscopy of different endoscopists is uneven, for example, the speed of endoscope withdrawal is too high, which results in insufficient observation of mucosa, and thus results in missed diagnosis of adenoma.
However, because the intestinal examination process combines the diagnosis and treatment functions into a whole, when the examination process encounters the interference conditions of flushing, adherence and the like, the speed value is easy to greatly vibrate, and the accuracy of monitoring the speed of endoscope withdrawal is reduced.
Therefore, how to improve the accuracy of monitoring the speed of withdrawing the endoscope is a technical problem which needs to be solved urgently in the technical field of medical assistance at present.
Disclosure of Invention
The application provides a method and a device for monitoring the speed of endoscope withdrawal of an intestinal tract and a computer readable storage medium, aiming at solving the problem of how to improve the accuracy of monitoring the speed of endoscope withdrawal.
In one aspect, the application provides a method for monitoring the speed of endoscope withdrawal of an intestinal tract, the method comprising:
identifying a fuzzy image in a first enteroscopy image set corresponding to a pre-acquired target enteroscopy video;
removing the fuzzy image in the first enteroscopy image set to obtain a second enteroscopy image set;
calculating the endoscope withdrawing speed of the current frame enteroscopy image in the second enteroscopy image set;
and monitoring the intestinal endoscope withdrawing speed based on the endoscope withdrawing speed of the current frame enteroscope image and a preset endoscope withdrawing speed threshold value.
In one possible implementation manner of the present application, the identifying a blurred image in a first enteroscopy image set corresponding to a pre-acquired target enteroscopy video includes:
acquiring a target enteroscope video shot by an endoscopy device;
decoding the target enteroscopy video to obtain a first enteroscopy image set;
preprocessing the first enteroscopy image set;
blurred images in the pre-processed first enteroscopy image set are identified.
In one possible implementation manner of the present application, the identifying a blurred image in the preprocessed first enteroscopy image set includes:
converting each image in the preprocessed first enteroscopy image set into a gray image to obtain a third enteroscopy image set;
calculating a sharpness value for each image in the third set of enteroscopic images;
determining a blurred image in the third enteroscopic image set based on the sharpness values.
In one possible implementation manner of the present application, the calculating a sharpness value of each image in the third enteroscopic image set includes:
selecting a plurality of edge regions in each image in the third set of enteroscopic images;
selecting a target pixel dot matrix in each edge area of the plurality of edge areas;
calculating the target pixel dot matrix based on a Laplace operator to obtain a definition value of each edge region;
and summing the definition values of each edge region, and determining the obtained sum value as the definition value of each image in the three-enteroscopy image set.
In one possible implementation manner of the present application, the identifying a blurred image in the preprocessed first enteroscopy image set includes:
and inputting the preprocessed first enteroscope image set into a pre-trained fuzzy image recognition model to obtain a fuzzy image.
In a possible implementation manner of the present application, before inputting the preprocessed first enteroscopy image set into a pre-trained blurred image recognition model to obtain a blurred image, the method further includes:
acquiring an enteroscope image sample set;
classifying and labeling the images in the enteroscopy image sample set to obtain a fuzzy image sample set and a clear image sample set;
and training an initial fuzzy image recognition model by adopting the fuzzy image sample set and the clear image sample set to obtain the pre-trained fuzzy image recognition model.
In a possible implementation manner of the present application, the calculating a speed of endoscope retraction of the current enteroscopy image in the second enteroscopy image set includes:
calculating a first hash value of the current enteroscopic image in the second enteroscopic image set and a second hash value of a preset enteroscopic image frame in front of the current enteroscopic image frame;
calculating the Hamming distance between the current frame enteroscopy image and each image in the preset frame enteroscopy images based on the first hash value and the second hash value;
determining a weighted average hamming distance of the current frame enteroscopy image based on the hamming distance of each image in the current frame enteroscopy image and the preset frame enteroscopy image;
and determining the endoscope retreating speed of the current enteroscopy image based on the weighted average Hamming distance of the current enteroscopy image.
On the other hand, this application provides a mirror speed monitoring devices is moved back to intestinal, the device includes:
the first identification unit is used for identifying a fuzzy image in a first enteroscopy image set corresponding to a target enteroscopy video acquired in advance;
the first eliminating unit is used for eliminating the fuzzy image in the first enteroscope image set to obtain a second enteroscope image set;
the first calculating unit is used for calculating the endoscope withdrawing speed of the current enteroscopy image in the second enteroscopy image set;
and the first monitoring unit is used for monitoring the intestinal tract endoscope withdrawing speed based on the endoscope withdrawing speed of the current frame enteroscope image and a preset endoscope withdrawing speed threshold value.
In a possible implementation manner of the present application, the first identifying unit specifically includes:
the first acquisition unit is used for acquiring a target enteroscope video shot by the endoscopy equipment;
the first decoding unit is used for decoding the target enteroscopy video to obtain a first enteroscopy image set;
a first preprocessing unit for preprocessing the first enteroscope image set;
and the second identification unit is used for identifying the blurred image in the preprocessed first enteroscope image set.
In a possible implementation manner of the present application, the second identifying unit specifically includes:
the first conversion unit is used for converting each image in the preprocessed first enteroscopy image set into a gray image to obtain a third enteroscopy image set;
a second calculation unit for calculating a sharpness value of each image in the third set of enteroscopic images;
a first determination unit for determining a blurred image in the third enteroscopic image set based on the sharpness value.
In a possible implementation manner of the present application, the second calculating unit is specifically configured to:
selecting a plurality of edge regions in each image in the third set of enteroscopic images;
selecting a target pixel dot matrix in each edge area of the plurality of edge areas;
calculating the target pixel dot matrix based on a Laplace operator to obtain a definition value of each edge region;
and summing the definition values of each edge region, and determining the obtained sum value as the definition value of each image in the three-enteroscopy image set.
In a possible implementation manner of the present application, the second identifying unit is specifically configured to:
and inputting the preprocessed first enteroscope image set into a pre-trained fuzzy image recognition model to obtain a fuzzy image.
In a possible implementation manner of the present application, before inputting the preprocessed first enteroscopy image set into a pre-trained blurred image recognition model to obtain a blurred image, the apparatus is further configured to:
acquiring an enteroscope image sample set;
classifying and labeling the images in the enteroscopy image sample set to obtain a fuzzy image sample set and a clear image sample set;
and training an initial fuzzy image recognition model by adopting the fuzzy image sample set and the clear image sample set to obtain the pre-trained fuzzy image recognition model.
In a possible implementation manner of the present application, the first calculating unit is specifically configured to:
calculating a first hash value of the current enteroscopic image in the second enteroscopic image set and a second hash value of a preset enteroscopic image frame in front of the current enteroscopic image frame;
calculating the Hamming distance between the current frame enteroscopy image and each image in the preset frame enteroscopy images based on the first hash value and the second hash value;
determining a weighted average hamming distance of the current frame enteroscopy image based on the hamming distance of each image in the current frame enteroscopy image and the preset frame enteroscopy image;
and determining the endoscope retreating speed of the current enteroscopy image based on the weighted average Hamming distance of the current enteroscopy image.
In another aspect, the present application further provides a computer device, including:
one or more processors;
a memory; and
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the method for monitoring of end-of-bowel speed.
In another aspect, the present application further provides a computer readable storage medium, on which a computer program is stored, where the computer program is loaded by a processor to execute the steps in the method for monitoring the speed of endoscope withdrawal.
The method for monitoring the speed of endoscope withdrawal of the intestinal tract comprises the steps of identifying a fuzzy image in a first enteroscope image set corresponding to a target enteroscope video which is acquired in advance; removing the fuzzy image in the first enteroscopy image set to obtain a second enteroscopy image set; calculating the endoscope withdrawing speed of the current enteroscopy image in the second enteroscopy image set; and monitoring the endoscope withdrawing speed of the intestinal tract based on the endoscope withdrawing speed of the current enteroscope image and a preset endoscope withdrawing speed threshold value. Compared with the prior art, the method and the device have the advantages that the fuzzy image in the first enteroscope image set corresponding to the target enteroscope video is actively identified, then the fuzzy image in the first enteroscope image set is eliminated, and the second enteroscope image set is obtained, so that the influence on the monitored endoscope withdrawing speed caused by the interference conditions of flushing, adherence and the like in the checking process can be avoided, and the accuracy of the endoscope withdrawing speed monitoring is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic view of a scene of an intestinal endoscope retracting speed monitoring system provided in an embodiment of the present application;
fig. 2 is a schematic flow chart of an embodiment of a method for monitoring a speed of endoscope withdrawal provided in an embodiment of the present application;
FIG. 3 is a flowchart illustrating an embodiment of step 201 provided in an embodiment of the present application;
FIG. 4 is a flowchart illustrating one embodiment of step 304 provided in embodiments of the present application;
FIG. 5 is a flowchart of one embodiment of step 402 provided in an embodiment of the present application;
FIG. 6 is a schematic flow chart diagram illustrating another embodiment of step 201 provided in an embodiment of the present application;
FIG. 7 is a flowchart illustrating an embodiment of step 203 provided in an embodiment of the present application;
fig. 8 is a schematic structural diagram of an embodiment of an intestinal tract endoscope retracting speed monitoring device provided in an embodiment of the present application;
FIG. 9 is a schematic structural diagram of an embodiment of a computer device provided in the embodiments of the present application;
FIG. 10 is a schematic diagram of a process of image pre-processing provided in an embodiment of the present application;
FIG. 11 is a schematic diagram of a recognition process of a blurred image recognition model provided in an embodiment of the present application;
fig. 12 is a schematic diagram for comparing and analyzing the mirror-down speed before and after the interference elimination provided in the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application.
In the description of the present application, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are used merely for convenience of description and for simplicity of description, and do not indicate or imply that the referenced device or element must have a particular orientation, be constructed in a particular orientation, and be operated, and thus should not be considered as limiting the present application. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
In this application, the word "exemplary" is used to mean "serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, details are set forth for the purpose of explanation. It will be apparent to one of ordinary skill in the art that the present application may be practiced without these specific details. In other instances, well-known structures and processes are not set forth in detail in order to avoid obscuring the description of the present application with unnecessary detail. Thus, the present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
The embodiments of the present application provide a method and an apparatus for monitoring a speed of endoscope withdrawal of an intestinal tract, and a computer-readable storage medium, which are described in detail below.
As shown in fig. 1, fig. 1 is a schematic view of a scene of an intestinal endoscope speed monitoring system according to an embodiment of the present application, where the intestinal endoscope speed monitoring system may include a plurality of terminals 100 and a server 200, the terminals 100 are connected to the server 200 through a network, and an intestinal endoscope speed monitoring device is integrated in the server 200, such as the server in fig. 1, and the terminals 100 may access the server 200.
In the embodiment of the present application, the server 200 is mainly configured to identify a blurred image in a first enteroscopy image set corresponding to a pre-acquired target enteroscopy video; removing the fuzzy image in the first enteroscopy image set to obtain a second enteroscopy image set; calculating the endoscope withdrawing speed of the current enteroscopy image in the second enteroscopy image set; and monitoring the endoscope withdrawing speed of the intestinal tract based on the endoscope withdrawing speed of the current enteroscope image and a preset endoscope withdrawing speed threshold value.
In this embodiment, the server 200 may be an independent server, or may be a server network or a server cluster composed of servers, for example, the server 200 described in this embodiment includes, but is not limited to, a computer, a network terminal, a single network server, a plurality of network server sets, or a cloud server composed of a plurality of servers. Among them, the Cloud server is constituted by a large number of computers or web servers based on Cloud Computing (Cloud Computing). In the embodiment of the present application, the server and the terminal may implement communication through any communication manner, including but not limited to mobile communication based on the third Generation Partnership Project (3 GPP), Long Term Evolution (LTE), Worldwide Interoperability for Microwave Access (WiMAX), or computer network communication based on the TCP/IP Protocol Suite (TCP/IP), User Datagram Protocol (UDP), and the like.
It is to be understood that the terminal 100 used in the embodiments of the present application may be a device that includes both receiving and transmitting hardware, as well as a device that has both receiving and transmitting hardware capable of performing two-way communication over a two-way communication link. Such a terminal may include: a cellular or other communication device having a single line display or a multi-line display or a cellular or other communication device without a multi-line display. The terminal 100 may specifically be a desktop terminal or a mobile terminal, and the terminal 100 may also specifically be one of a mobile phone, a tablet computer, a notebook computer, and the like.
Those skilled in the art will understand that the application environment shown in fig. 1 is only one application scenario of the present application, and does not constitute a limitation to the application scenario of the present application, and other application environments may also include more or fewer terminals than those shown in fig. 1, or a server network connection relationship, for example, only 1 server and 2 terminals are shown in fig. 1. It is understood that the system for monitoring the speed of intestinal tract retrospective endoscope may further include one or more other servers, or/and one or more terminals connected to the server network, and is not limited herein.
In addition, as shown in fig. 1, the system for monitoring the speed of endoscope withdrawing from the intestinal tract may further include a memory 300 for storing data, such as a video of the enteroscope of the user and monitoring data of the speed of endoscope withdrawing from the intestinal tract, for example, the monitoring data of the speed of endoscope withdrawing from the intestinal tract when the system for monitoring the speed of endoscope withdrawing from the intestinal tract is running.
It should be noted that the scene schematic diagram of the intestinal tract endoscope retracting speed monitoring system shown in fig. 1 is only an example, and the intestinal tract endoscope retracting speed monitoring system and the scene described in the embodiment of the present application are for more clearly illustrating the technical solution of the embodiment of the present application, and do not form a limitation on the technical solution provided in the embodiment of the present application.
Next, a method for monitoring the speed of endoscope withdrawal of the intestinal tract provided by the embodiment of the present application is introduced.
In the embodiment of the method for monitoring the speed of endoscope withdrawing from the intestinal tract, an execution main body of an apparatus for monitoring the speed of endoscope withdrawing from the intestinal tract is used, and in order to simplify and facilitate description, the execution main body is omitted in the following method embodiments, and the apparatus for monitoring the speed of endoscope withdrawing from the intestinal tract is applied to a computer device, and the method includes: identifying a fuzzy image in a first enteroscopy image set corresponding to a pre-acquired target enteroscopy video; removing the fuzzy image in the first enteroscopy image set to obtain a second enteroscopy image set; calculating the endoscope withdrawing speed of the current enteroscopy image in the second enteroscopy image set; and monitoring the endoscope withdrawing speed of the intestinal tract based on the endoscope withdrawing speed of the current enteroscope image and a preset endoscope withdrawing speed threshold value.
Referring to fig. 2 to 12, fig. 2 is a schematic flow chart of an embodiment of a method for monitoring a speed of intestinal tract retroendoscopy provided in the embodiment of the present application, where the method specifically includes steps 201 to 204:
201. identifying a fuzzy image in a first enteroscopy image set corresponding to a pre-acquired target enteroscopy video;
the enteroscopy is a method of inserting the enteroscopy circulation cavity into the ileocecal part through the anus and observing the colon lesion from the side of the mucous membrane. Enteroscopy can meet the needs of examination of all colon areas. In the observation of enteroscopy, the colon sequentially passes through the cecum, ascending colon, transverse colon, descending colon, sigmoid colon and rectum, wherein the lengths of the different intestinal sections and the corresponding anatomical marks of the different intestinal sections have certain differences. Thus, when the enteroscope is at a different bowel segment, a corresponding target enteroscope video will be acquired. Then, through corresponding operations, the target enteroscopy video is converted into a corresponding first enteroscopy image set, it should be noted that the first enteroscopy image set includes a plurality of enteroscopy images, the plurality of enteroscopy images may be thousands to tens of thousands, and the specific number is not limited.
When interference conditions such as flushing, adherence and the like are met in the inspection process, the speed value is easy to greatly vibrate, so that some fuzzy images exist in the shot target enteroscopy video, and the accuracy of monitoring the speed of endoscope withdrawal is reduced. For details, please refer to the following embodiments, which are not described herein again, how to identify a blurred image in a first enteroscopy image set corresponding to a pre-acquired target enteroscopy video.
202. Removing the fuzzy image in the first enteroscopy image set to obtain a second enteroscopy image set;
after step 201, that is, after the blurred image in the first enteroscope image set is identified, the blurred image therein needs to be removed, so as to obtain a second enteroscope image set which is not interfered.
203. Calculating the endoscope withdrawing speed of the current enteroscopy image in the second enteroscopy image set;
and calculating the endoscope withdrawing speed of the current enteroscope image in the second enteroscope image set after the interference is removed as the current endoscope withdrawing speed. For details, please refer to the following embodiments, which are not described herein in detail, how to calculate the speed of the current colonoscopy image in the second colonoscopy image set.
204. And monitoring the endoscope withdrawing speed of the intestinal tract based on the endoscope withdrawing speed of the current enteroscope image and a preset endoscope withdrawing speed threshold value.
The preset endoscope withdrawing speed threshold is an overspeed judgment value (or an overspeed line) of the endoscope withdrawing speed, please refer to fig. 12, when the endoscope withdrawing speed of the current frame enteroscopy image exceeds the preset endoscope withdrawing speed threshold, it is determined that the current endoscope withdrawing speed is overspeed, and at this time, corresponding alarm feedback information can be generated to corresponding operating doctors to remind the doctors to properly reduce the endoscope withdrawing speed, so that the intestinal endoscope withdrawing speed can be effectively monitored in real time. As can be seen from fig. 12, after the interference is removed or the blurred image is eliminated, the mirror-removing speed is obviously better than the original mirror-removing speed feedback condition.
The method for monitoring the speed of endoscope withdrawal of the intestinal tract comprises the steps of identifying a fuzzy image in a first enteroscope image set corresponding to a target enteroscope video which is acquired in advance; removing the fuzzy image in the first enteroscopy image set to obtain a second enteroscopy image set; calculating the endoscope withdrawing speed of the current enteroscopy image in the second enteroscopy image set; and monitoring the endoscope withdrawing speed of the intestinal tract based on the endoscope withdrawing speed of the current enteroscope image and a preset endoscope withdrawing speed threshold value. Compared with the prior art, the method and the device have the advantages that the fuzzy image in the first enteroscope image set corresponding to the target enteroscope video is actively identified, then the fuzzy image in the first enteroscope image set is eliminated, and the second enteroscope image set is obtained, so that the influence on the monitored endoscope withdrawing speed caused by the interference conditions of flushing, adherence and the like in the checking process can be avoided, and the accuracy of the endoscope withdrawing speed monitoring is improved.
In the embodiment of the present application, please refer to fig. 3, step 201, identifying a blurred image in a first enteroscopy image set corresponding to a pre-acquired target enteroscopy video, specifically includes steps 301 to 304:
301. acquiring a target enteroscope video shot by an endoscopy device;
it should be noted that, the target enteroscopy video shot by the endoscopy equipment in the embodiment meets the real-time requirement, and is transmitted to the intestinal endoscope retracting speed monitoring device in real time in a corresponding communication mode, so that the intestinal endoscope retracting speed can be monitored in real time.
302. Decoding a target enteroscopy video to obtain a first enteroscopy image set;
and decoding the target enteroscopy video to obtain a corresponding first enteroscopy image set (preset frames per second), such as 8 frames per second.
303. Preprocessing the first enteroscope image set;
in order to enable the images in the first enteroscopy image set to meet the requirements of the subsequent processing equipment on the image size, each image in the first enteroscopy image set can be subjected to cropping processing and size normalization processing. Specifically, referring to fig. 10, the enteroscope image a before the preprocessing is cut to obtain a cut image b, a portion of the image edge without valid information is cut off, only a main area of the enteroscope of interest is left, then the cut image b is subjected to size normalization processing, specifically, the size normalization processing may further include black edge supplement processing on the image b to obtain an image c, so that the length and the width of the image c are the same, and finally, the size of the image c is adjusted (may be enlarged or reduced), which is exemplified by reduction in this embodiment, the image c is reduced to obtain an image d, and the uniform size is N × N, such as 480.
304. Blurred images in the pre-processed first enteroscopy image set are identified.
For details, please refer to the following embodiments, which are not described herein in detail, how to identify the blurred image in the first enteroscopy image set after the preprocessing.
In the embodiment of the present application, please refer to fig. 4, wherein the step 304 of identifying the blurred image in the first enteroscopy image set after the preprocessing specifically includes steps 401 to 403:
401. converting each image in the preprocessed first enteroscopy image set into a gray image to obtain a third enteroscopy image set;
402. calculating a sharpness value of each image in the third enteroscopy image set;
403. based on the sharpness values, blurred images in the third set of enteroscopic images are determined.
In step 403, determining the blurred image in the third enteroscopy image set based on the sharpness value, specifically, a preset sharpness threshold may be given, and by comparing the sharpness value calculated in step 402 with the preset sharpness threshold, if the sharpness value of the target image in the third enteroscopy image set is greater than or equal to the preset sharpness threshold, determining that the target image is a non-blurred image (sharp image), otherwise, determining that the target image is a blurred image.
In the embodiment of the present application, referring to fig. 5, step 402 of calculating a sharpness value of each image in the third enteroscopy image set specifically includes steps 501 and 504:
501. selecting a plurality of edge regions in each image in the third set of enteroscopic images;
specifically, the selection of a plurality of edge regions in each image in the third enteroscopic image set can be realized by adopting an edge gradient evaluation algorithm.
502. Selecting a target pixel dot matrix in each edge area of the plurality of edge areas;
503. calculating the target pixel dot matrix to obtain the definition value of each edge area;
specifically, the target pixel lattice can be calculated by using a laplacian operator to obtain the definition value of each edge region.
504. And summing the definition values of each edge region, and determining the sum value as the definition value of each image in the three-enteroscopy image set.
In the embodiment of the present application, please refer to fig. 6, step 201, and identify a blurred image in the first enteroscopy image set after preprocessing, which specifically includes step 601:
601. and inputting the preprocessed first enteroscope image set into a pre-trained fuzzy image recognition model to obtain a fuzzy image.
As shown in fig. 11, after the first enteroscopy image set is input into the pre-trained blurred image recognition model, each image will obtain two labels of 0 and 1, where 0 is a normal image and 1 is determined as a blurred image.
In this embodiment of the application, before inputting the preprocessed first enteroscope image set into the pre-trained blurred image recognition model to obtain a blurred image, the method further includes: acquiring an enteroscope image sample set; classifying and labeling the images in the enteroscopy image sample set to obtain a fuzzy image sample set and a clear image sample set; and training the initial fuzzy image recognition model by adopting the fuzzy image sample set and the clear image sample set to obtain a pre-trained fuzzy image recognition model.
Specifically, in the fuzzy image recognition model, ResNet50 can be used as a basic neural network structure; the loss function adopts binary cross entropy loss:
where m is the number of input samples, the predicted value of the model is Y, and the true value is Ŷ. ŷiFor the sign function 0 or 1, if the true class of sample i is equal to j taken 1 and otherwise 0, yiIs the probability value of the output. Specifically, through experimental tests, an initial learning rate is set to be 0.001, a learning rate attenuation parameter decay _ rate is set to be 0.9, Adam is selected by a model optimization algorithm, and a model with the minimum verification set loss is trained and stored to be an optimal fuzzy image recognition model.
In the embodiment of the present application, please refer to fig. 7, wherein the step 203 of calculating the scope retracting speed of the current enteroscopic image in the second enteroscopic image set includes steps 701 to 704:
701. calculating a first hash value of a current enteroscopy image in a second enteroscopy image set and a second hash value of a preset enteroscopy image frame in front of the current enteroscopy image frame;
in the embodiment of the application, before calculating the scope retracting speed of the enteroscopy image of the current frame in the second enteroscopy image set, each image in the second enteroscopy image set can be reduced to obtain a fourth enteroscopy image set; and carrying out gray level conversion on each image in the fourth enteroscope image set to obtain a fifth enteroscope image set. Since a picture with size N × N, such as a picture with size 480 × 480, has more than 20 ten thousand pixels, and each pixel stores an RGB value, this is a huge amount of information, and the details to be processed are very large. Thus, reducing the picture to w × h can hide its detail, e.g. scaling to 9 × 8, which facilitates its subsequent conversion to a hash value. It is rather complicated to directly use the RGB values to compare the color intensity differences, and thus it can be converted into gray values-only one integer from 0 to 255 representing gray. This simplifies the three-dimensional comparison to a one-dimensional comparison.
When calculating the dHash value, each line is calculated respectively, the size of the previous pixel and the size of the next pixel in each line of pixels are compared, the larger the front and the back is 1, otherwise, the front and the back are 0. A total of 8 comparisons can be made with 9 pixels per row, resulting in a hash of 8 x 8 for a total of 64 bits. The calculation formula is as follows:
where (m ', n') represents each pixel point on the picture.
702. Calculating the Hamming distance between the current frame enteroscopy image and each image in the preset frame enteroscopy image based on the first hash value and the second hash value;
the Hamming distance is the number of different positions corresponding to the hash values of two pictures. The calculation formula is as follows:
where (j ', k') represents the value at the corresponding location of the hash matrix.
It should be noted that in the intestinal video, the sampling frequency is 8 frames per second, that is, the time interval between every two frames is 1/8 seconds, in this period, the farther the lens moves, the greater the difference of the captured intestinal pictures, and the closer the lens moves, the smaller the picture difference. Therefore, the hamming distance can reflect the moving speed of the lens in the intestinal tract by calculating the difference degree of the two pictures. If only two frames of pictures are used for calculating the enteroscope speed, the statistical deviation is larger, and the enteroscope speed is calculated by increasing the sampling number more typically. Therefore, the hamming distance between the current frame and the previous 10 frames is calculated, that is, in this embodiment, the enteroscopy image of the previous 10 frames of the enteroscopy image of the current frame is selected.
703. Determining a weighted average Hamming distance of the current frame enteroscopy image based on the Hamming distance of each image in the current frame enteroscopy image and a preset frame enteroscopy image;
normally, two frames that are closer in time should have smaller hamming distances and vice versa. To reduce this inherent difference, two frame hamming distances far apart should be given less weight and vice versa. The current frame weighted average hamming distance is:
wherein d isiIs the Hamming distance, w, between the current frame and the ith frame in the nearest n framesiIs the weight value of the ith frame.
Is the weighted hamming distance. Wherein the weight distribution conforms to the random variable distribution, and the distribution function is:
and it is clear that it satisfies:
in the present embodiment, n is 10.
704. And determining the endoscope withdrawing speed of the current enteroscopy image based on the weighted average Hamming distance of the current enteroscopy image.
Specifically, the hamming distance is obtained according to the position difference corresponding to the number statistics of two 64-bit numbers, the numerical range is [0,64], and the hamming distance is mapped to the [0,100] interval, which is the speed value of the current frame enteroscopy image, and the method is as follows:
in order to better implement the method for monitoring the speed of endoscope withdrawing from the intestinal tract in the embodiment of the present application, on the basis of the method for monitoring the speed of endoscope withdrawing from the intestinal tract, an apparatus for monitoring the speed of endoscope withdrawing from the intestinal tract is further provided in the embodiment of the present application, as shown in fig. 8, the apparatus 800 for monitoring the speed of endoscope withdrawing from the intestinal tract includes a first identifying unit 801, a first rejecting unit 802, a first calculating unit 803, and a first monitoring unit 804:
a first identifying unit 801, configured to identify a blurred image in a first enteroscopy image set corresponding to a target enteroscopy video acquired in advance;
a first eliminating unit 802, configured to eliminate a blurred image in the first enteroscopy image set to obtain a second enteroscopy image set;
the first calculating unit 803 is used for calculating the endoscope withdrawing speed of the current frame enteroscope image in the second enteroscope image set;
the first monitoring unit 804 is configured to monitor the endoscope retracting speed of the intestinal tract based on the endoscope retracting speed of the current frame enteroscope image and a preset endoscope retracting speed threshold.
In this embodiment of the application, the first identifying unit 801 specifically includes:
the first acquisition unit is used for acquiring a target enteroscope video shot by the endoscopy equipment;
the first decoding unit is used for decoding a target enteroscopy video to obtain a first enteroscopy image set;
the first preprocessing unit is used for preprocessing the first enteroscope image set;
and the second identification unit is used for identifying the blurred image in the preprocessed first enteroscope image set.
In an embodiment of the application, the second identifying unit specifically includes:
the first conversion unit is used for converting each image in the preprocessed first enteroscopy image set into a gray image to obtain a third enteroscopy image set;
a second calculating unit for calculating a sharpness value of each image in the third enteroscopy image set;
a first determination unit for determining a blurred image in the third enteroscopic image set based on the sharpness value.
In an embodiment of the present application, the second calculating unit is specifically configured to:
selecting a plurality of edge regions in each image in the third set of enteroscopic images;
selecting a target pixel dot matrix in each edge area of the plurality of edge areas;
calculating a target pixel lattice based on a Laplace operator to obtain a definition value of each edge region;
and summing the definition values of each edge region, and determining the sum value as the definition value of each image in the three-enteroscopy image set.
In an embodiment of the present application, the second identifying unit is specifically configured to:
and inputting the preprocessed first enteroscope image set into a pre-trained fuzzy image recognition model to obtain a fuzzy image.
In this embodiment of the application, before inputting the preprocessed first enteroscope image set into the pre-trained blurred image recognition model to obtain a blurred image, the apparatus is further configured to:
acquiring an enteroscope image sample set;
classifying and labeling the images in the enteroscopy image sample set to obtain a fuzzy image sample set and a clear image sample set;
and training the initial fuzzy image recognition model by adopting the fuzzy image sample set and the clear image sample set to obtain a pre-trained fuzzy image recognition model.
In this embodiment of the application, the first calculating unit 803 is specifically configured to:
calculating a first hash value of a current enteroscopy image in a second enteroscopy image set and a second hash value of a preset enteroscopy image frame in front of the current enteroscopy image frame;
calculating the Hamming distance between the current frame enteroscopy image and each image in the preset frame enteroscopy image based on the first hash value and the second hash value;
determining a weighted average Hamming distance of the current frame enteroscopy image based on the Hamming distance of each image in the current frame enteroscopy image and a preset frame enteroscopy image;
and determining the endoscope withdrawing speed of the current enteroscopy image based on the weighted average Hamming distance of the current enteroscopy image.
The device 800 for monitoring the speed of withdrawing the endoscope from the intestinal tract comprises a first identification unit 801 for identifying a fuzzy image in a first enteroscopy image set corresponding to a target enteroscopy video acquired in advance; a first eliminating unit 802, eliminating the blurred image in the first enteroscope image set to obtain a second enteroscope image set; the first calculating unit 803 calculates the endoscope withdrawing speed of the current frame enteroscope image in the second enteroscope image set; the first monitoring unit 804 monitors the endoscope withdrawing speed of the intestinal tract based on the endoscope withdrawing speed of the current frame enteroscope image and a preset endoscope withdrawing speed threshold. Compared with the prior art, the method and the device have the advantages that the fuzzy image in the first enteroscope image set corresponding to the target enteroscope video is actively identified, then the fuzzy image in the first enteroscope image set is eliminated, and the second enteroscope image set is obtained, so that the influence on the monitored endoscope withdrawing speed caused by the interference conditions of flushing, adherence and the like in the checking process can be avoided, and the accuracy of the endoscope withdrawing speed monitoring is improved.
In addition to the method and the device for monitoring the speed of endoscope withdrawal of the intestinal tract, an embodiment of the present application further provides a computer device, which integrates any one of the apparatuses for monitoring the speed of endoscope withdrawal of the intestinal tract provided by the embodiments of the present application, and the computer device includes:
one or more processors;
a memory; and
one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to perform the operations of any of the methods in any of the aforementioned embodiments of the method of monitoring of an end-of-bowel speed.
The embodiment of the application also provides computer equipment which integrates any intestinal tract endoscope withdrawal speed monitoring device provided by the embodiment of the application. Referring to fig. 9, fig. 9 is a schematic structural diagram of an embodiment of a computer device according to an embodiment of the present application.
As shown in fig. 9, it shows a schematic structural diagram of the intestinal tract endoscope retracting speed monitoring device designed in the embodiment of the present application, specifically:
the intestinal tract endoscope speed monitoring device can comprise one or more processors 901 of a processing core, one or more storage units 902 of a computer-readable storage medium, a power supply 903, an input unit 904 and the like. It will be appreciated by those skilled in the art that the configuration of the bowel despooling speed monitoring device shown in fig. 9 does not constitute a limitation of the bowel despooling speed monitoring device and may include more or fewer components than shown, or some components may be combined, or a different arrangement of components. Wherein:
the processor 901 is a control center of the device for monitoring the speed of endoscope withdrawing from the intestinal tract, and is connected with each part of the whole device for monitoring the speed of endoscope withdrawing from the intestinal tract by using various interfaces and lines, and executes various functions and processing data of the device for monitoring the speed of endoscope withdrawing from the intestinal tract by running or executing software programs and/or modules stored in the storage unit 902 and calling data stored in the storage unit 902, thereby integrally monitoring the device for monitoring the speed of endoscope withdrawing from the intestinal tract. Optionally, processor 901 may include one or more processing cores; preferably, the processor 901 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 901.
The storage unit 902 may be used to store software programs and modules, and the processor 901 executes various functional applications and data processing by operating the software programs and modules stored in the storage unit 902. The storage unit 902 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required by at least one function, and the like; the storage data area may store data created from use of the intestinal tract withdrawal speed monitoring device, and the like. Further, the storage unit 902 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the storage unit 902 may further include a memory controller to provide the processor 901 with access to the storage unit 902.
The intestinal endoscope withdrawing speed monitoring device further comprises a power supply 903 for supplying power to all components, preferably, the power supply 903 can be logically connected with the processor 901 through a power management system, and therefore functions of charging, discharging, power consumption management and the like can be managed through the power management system. The power supply 903 may also include any component including one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.
The device may further comprise an input unit 904, the input unit 904 being operable to receive input numeric or character information and to generate keyboard, mouse, joystick, optical or trackball signal inputs relating to user settings and function control.
Although not shown, the intestinal tract endoscope retracting speed monitoring device can further comprise a display unit and the like, which are not described in detail herein. Specifically, in this embodiment of the present application, the processor 901 in the intestinal tract endoscope retracting speed monitoring apparatus loads an executable file corresponding to a process of one or more application programs into the storage unit 902 according to the following instructions, and the processor 901 runs the application programs stored in the storage unit 902, so as to implement various functions as follows:
identifying a fuzzy image in a first enteroscopy image set corresponding to a pre-acquired target enteroscopy video; removing the fuzzy image in the first enteroscopy image set to obtain a second enteroscopy image set; calculating the endoscope withdrawing speed of the current enteroscopy image in the second enteroscopy image set; and monitoring the endoscope withdrawing speed of the intestinal tract based on the endoscope withdrawing speed of the current enteroscope image and a preset endoscope withdrawing speed threshold value.
The method for monitoring the speed of endoscope withdrawal of the intestinal tract comprises the steps of identifying a fuzzy image in a first enteroscope image set corresponding to a target enteroscope video which is acquired in advance; removing the fuzzy image in the first enteroscopy image set to obtain a second enteroscopy image set; calculating the endoscope withdrawing speed of the current enteroscopy image in the second enteroscopy image set; and monitoring the endoscope withdrawing speed of the intestinal tract based on the endoscope withdrawing speed of the current enteroscope image and a preset endoscope withdrawing speed threshold value. Compared with the prior art, the method and the device have the advantages that the fuzzy image in the first enteroscope image set corresponding to the target enteroscope video is actively identified, then the fuzzy image in the first enteroscope image set is eliminated, and the second enteroscope image set is obtained, so that the influence on the monitored endoscope withdrawing speed caused by the interference conditions of flushing, adherence and the like in the checking process can be avoided, and the accuracy of the endoscope withdrawing speed monitoring is improved.
To this end, an embodiment of the present application provides a computer-readable storage medium, which may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like. The computer readable storage medium has stored therein a plurality of instructions that can be loaded by the processor to perform the steps of any one of the methods for monitoring a withdrawal speed of an intestinal tract provided by the embodiments of the present application. For example, the instructions may perform the steps of:
identifying a fuzzy image in a first enteroscopy image set corresponding to a pre-acquired target enteroscopy video; removing the fuzzy image in the first enteroscopy image set to obtain a second enteroscopy image set; calculating the endoscope withdrawing speed of the current enteroscopy image in the second enteroscopy image set; and monitoring the endoscope withdrawing speed of the intestinal tract based on the endoscope withdrawing speed of the current enteroscope image and a preset endoscope withdrawing speed threshold value.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The method, the device and the computer-readable storage medium for monitoring the speed of endoscope withdrawal of the intestinal tract provided by the embodiment of the application are introduced in detail, a specific example is applied in the description to explain the principle and the implementation manner of the application, and the description of the embodiment is only used for helping to understand the method and the core idea of the application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A method for monitoring the speed of endoscope withdrawal of an intestinal tract, the method comprising:
identifying a fuzzy image in a first enteroscopy image set corresponding to a pre-acquired target enteroscopy video;
removing the fuzzy image in the first enteroscopy image set to obtain a second enteroscopy image set;
calculating the endoscope withdrawing speed of the current frame enteroscopy image in the second enteroscopy image set;
and monitoring the intestinal endoscope withdrawing speed based on the endoscope withdrawing speed of the current frame enteroscope image and a preset endoscope withdrawing speed threshold value.
2. The method for monitoring the speed of intestinal endoscopy according to claim 1, wherein the identifying of the blurred image in the first enteroscopy image set corresponding to the pre-acquired target enteroscopy video comprises:
acquiring a target enteroscope video shot by an endoscopy device;
decoding the target enteroscopy video to obtain a first enteroscopy image set;
preprocessing the first enteroscopy image set;
blurred images in the pre-processed first enteroscopy image set are identified.
3. The method for monitoring the speed of intestinal endoscopy according to claim 2, wherein the step of identifying blurred images in the preprocessed first set of enteroscopy images comprises:
converting each image in the preprocessed first enteroscopy image set into a gray image to obtain a third enteroscopy image set;
calculating a sharpness value for each image in the third set of enteroscopic images;
determining a blurred image in the third enteroscopic image set based on the sharpness values.
4. The method for monitoring the speed of intestinal endoscopy according to claim 3, wherein the calculating the sharpness value of each image of the third set of colonoscopic images includes:
selecting a plurality of edge regions in each image in the third set of enteroscopic images;
selecting a target pixel dot matrix in each edge area of the plurality of edge areas;
calculating the target pixel dot matrix based on a Laplace operator to obtain a definition value of each edge region;
and summing the definition values of each edge region, and determining the obtained sum value as the definition value of each image in the three-enteroscopy image set.
5. The method for monitoring the speed of intestinal endoscopy according to claim 2, wherein the step of identifying blurred images in the preprocessed first set of enteroscopy images comprises:
and inputting the preprocessed first enteroscope image set into a pre-trained fuzzy image recognition model to obtain a fuzzy image.
6. The method for monitoring the speed of intestinal endoscopy retraction according to claim 5, wherein before inputting the preprocessed first enteroscopic image set into a pre-trained blurred image recognition model to obtain blurred images, the method further comprises:
acquiring an enteroscope image sample set;
classifying and labeling the images in the enteroscopy image sample set to obtain a fuzzy image sample set and a clear image sample set;
and training an initial fuzzy image recognition model by adopting the fuzzy image sample set and the clear image sample set to obtain the pre-trained fuzzy image recognition model.
7. The method for monitoring the speed of endoscope withdrawal of intestinal tract according to claim 1, wherein said calculating the speed of endoscope withdrawal of the current enteroscopic image in the second enteroscopic image set comprises:
calculating a first hash value of the current enteroscopic image in the second enteroscopic image set and a second hash value of a preset enteroscopic image frame in front of the current enteroscopic image frame;
calculating the Hamming distance between the current frame enteroscopy image and each image in the preset frame enteroscopy images based on the first hash value and the second hash value;
determining a weighted average hamming distance of the current frame enteroscopy image based on the hamming distance of each image in the current frame enteroscopy image and the preset frame enteroscopy image;
and determining the endoscope retreating speed of the current enteroscopy image based on the weighted average Hamming distance of the current enteroscopy image.
8. An intestinal tract endoscope withdrawal speed monitoring device, which is characterized by comprising:
the first identification unit is used for identifying a fuzzy image in a first enteroscopy image set corresponding to a target enteroscopy video acquired in advance;
the first eliminating unit is used for eliminating the fuzzy image in the first enteroscope image set to obtain a second enteroscope image set;
the first calculating unit is used for calculating the endoscope withdrawing speed of the current enteroscopy image in the second enteroscopy image set;
and the first monitoring unit is used for monitoring the intestinal tract endoscope withdrawing speed based on the endoscope withdrawing speed of the current frame enteroscope image and a preset endoscope withdrawing speed threshold value.
9. A computer device, characterized in that the computer device comprises:
one or more processors;
a memory; and
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the method of monitoring of bowel withdrawal speed according to any of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program which is loaded by a processor to perform the steps of the method for monitoring of speed of bowel hyperopia according to any one of claims 1 to 7.
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