CN107230198A - Gastroscope image intelligent processing method and processing device - Google Patents
Gastroscope image intelligent processing method and processing device Download PDFInfo
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- 238000012545 processing Methods 0.000 title claims abstract description 33
- 238000003672 processing method Methods 0.000 title claims abstract description 17
- 210000002787 omasum Anatomy 0.000 claims abstract description 38
- 238000004458 analytical method Methods 0.000 claims abstract description 26
- 239000000284 extract Substances 0.000 claims description 18
- 238000012216 screening Methods 0.000 claims description 11
- 230000015572 biosynthetic process Effects 0.000 claims description 10
- 210000002784 stomach Anatomy 0.000 claims description 2
- 238000003745 diagnosis Methods 0.000 abstract description 7
- 201000010099 disease Diseases 0.000 abstract description 7
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 abstract description 7
- 238000000034 method Methods 0.000 abstract description 7
- 230000007170 pathology Effects 0.000 abstract description 3
- 238000000605 extraction Methods 0.000 description 17
- 238000003384 imaging method Methods 0.000 description 5
- 230000003902 lesion Effects 0.000 description 5
- 230000002452 interceptive effect Effects 0.000 description 4
- 230000011218 segmentation Effects 0.000 description 4
- 230000001575 pathological effect Effects 0.000 description 3
- 210000001519 tissue Anatomy 0.000 description 3
- 208000005718 Stomach Neoplasms Diseases 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 206010017758 gastric cancer Diseases 0.000 description 2
- 208000010749 gastric carcinoma Diseases 0.000 description 2
- 238000002372 labelling Methods 0.000 description 2
- 238000010827 pathological analysis Methods 0.000 description 2
- 201000011549 stomach cancer Diseases 0.000 description 2
- 201000000498 stomach carcinoma Diseases 0.000 description 2
- 206010028980 Neoplasm Diseases 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000001574 biopsy Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
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- 230000007812 deficiency Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000001861 endoscopic biopsy Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000686 essence Substances 0.000 description 1
- 210000001156 gastric mucosa Anatomy 0.000 description 1
- 206010020718 hyperplasia Diseases 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
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- 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
- A61B1/273—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 upper alimentary canal, e.g. oesophagoscopes, gastroscopes
- A61B1/2736—Gastroscopes
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
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- G06T2207/30092—Stomach; Gastric
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Abstract
The application provides a kind of gastroscope image intelligent processing method and processing device, and wherein method includes：Dynamic access the first gastroscope image；The cut zone that there is focus characteristic on the first gastroscope image is partitioned into, the one-level characteristic information and positional information of the correspondence cut zone are marked on the first gastroscope image as the second gastroscope image, the second gastroscope image is exported；Receive the second gastroscope image, one-level characteristic information and positional information in multiple second gastroscope images is combined the zone position information that analysis forms secondary characteristics information and the correspondence secondary characteristics information, output is labeled with the omasum mirror image of the secondary characteristics information and the zone position information；Receive the omasum mirror image and show.The application can help doctor to be diagnosed, and obtain pathology suggestion areas, greatly reduce fail to pinpoint a disease in diagnosis, the wrong probability examined.
Description
Technical field
The application is related to gastroscope image processing field, more particularly to a kind of gastroscope image intelligent processing method and processing device.
Background technology
At present, gastrocopy is still that stomach cancer is most direct, accurate, reliable diagnostic method.The precancerous lesion of stomach cancer is often
It is related to gastric mucosa extensive region, conventional endoscopic biopsy takes segmentation, random multidraw method, the focal cancerous issue of missing inspection
Possibility is very big, and the scope doctor that particularly experience lacks then is easier " loseing depending on cancer " occur；Secondly, under the microscope for
The judgement of Atypical hyperplasia and early carcinoma of stomach needs substantial amounts of case accumulation, training, between different pathologists or same
One pathologist repeatedly diagnoses same group of gastroscopic biopsy sample, all there is certain difference.
But traditional gastrocopy has a disadvantage that：1st, early carcinoma of stomach diagnosis is low, and loss, misdiagnosis rate are high.2nd, it is existing
Gastroscope system only give doctor to provide image, and primary dcreening operation and feature extraction are not carried out to image and are labeled, and
Doctor has to face substantial amounts of medical image, and which increase the workload of doctor, efficiency is low and easily causes doctor to go out
The situation of existing " failing to pinpoint a disease in diagnosis ".Though the 3, can carry out protruding imaging to focus, specific aim is had no for cancerous tissue, therefore to lesion nature
Judgement still by operation doctor experience.
The content of the invention
The application provides a kind of gastroscope image intelligent processing method and processing device, to solve the deficiency in correlation technique.
According to the first aspect of the embodiment of the present application there is provided a kind of gastroscope image intelligent processing method, comprise the following steps：
Dynamic access the first gastroscope image；
The cut zone that there is focus characteristic on the first gastroscope image is partitioned into, in the first gastroscope image subscript
The one-level characteristic information and positional information of the note correspondence cut zone export the second gastroscope figure as the second gastroscope image
Picture；
The second gastroscope image is received, by the one-level characteristic information and positional information in multiple second gastroscope images
The zone position information that analysis forms secondary characteristics information and the correspondence secondary characteristics information is combined, output mark is
State the omasum mirror image of secondary characteristics information and the zone position information；
Receive the omasum mirror image and show.
Further, it is described to be partitioned into the cut zone that there is focus characteristic on the first gastroscope image, described
The one-level characteristic information and positional information of the correspondence cut zone are marked on one gastroscope image as the second gastroscope image, output
The second gastroscope image, including：
The first gastroscope image is scanned, the regional frame that there is focus characteristic on the first gastroscope image is selected and carried out
Mark, extract mark the cut zone in one-level characteristic information and the cut zone in the gastroscope image
Positional information；
One-level characteristic information and the positional information conduct of the correspondence cut zone are marked on the first gastroscope image
Second gastroscope image, exports the second gastroscope image.
Further, it is described to receive the second gastroscope image, by the one-level feature in multiple second gastroscope images
Information and positional information are combined analysis to form the regional location of secondary characteristics information and the correspondence secondary characteristics information
Information, output is labeled with the omasum mirror image of secondary characteristics information and zone position information, including：
Receive and carry the second gastroscope image, extract in multiple second gastroscopes and characteristic information and position letter
Breath, multiple one-level characteristic informations, multiple one-level feature letters of combinatory analysis association are associated according to the positional information
Breath forms secondary characteristics information, and determines the regional extent on image of the secondary characteristics information, and output is labeled with described
The omasum mirror image of secondary characteristics information and the regional extent.
Further, described receive carries the second gastroscope image, extracts in multiple second gastroscopes and special
Reference ceases and positional information, associates multiple one-level characteristic informations according to the positional information, it is multiple that combinatory analysis is associated
One-level characteristic information formation secondary characteristics information, and determine the regional extent on image of the secondary characteristics information,
Output is labeled with the omasum mirror image of the secondary characteristics information and the regional extent, including：
Receive and carry the second gastroscope image, extract in multiple second gastroscopes and characteristic information and position letter
Breath, multiple one-level characteristic informations, multiple one-level feature letters of combinatory analysis association are associated according to the positional information
Breath forms secondary characteristics information, determines the regional extent on image of the secondary characteristics information, described two grades of retrieval correspondence
The feature description information of characteristic information, output is labeled with the secondary characteristics information, the feature description information and the region
The omasum mirror image of scope.
Further, it is partitioned into after the first gastroscope of dynamic access image on the first gastroscope image and there is focus
Before the cut zone of feature, in addition to：
The gastroscope image of acquisition is subjected to initial screening, the gastroscope image is divided into the gastroscope image of no stove feature and ill
The gastroscope image of stove feature.
According to the second aspect of the embodiment of the present application, there is provided a kind of gastroscope image intelligent processing unit, it is characterised in that bag
Include：
Acquisition module, for dynamic access the first gastroscope image；
First processing module, the cut zone that there is focus characteristic on the first gastroscope image for being partitioned into, in institute
One-level characteristic information and positional information that the correspondence cut zone is marked on the first gastroscope image are stated as the second gastroscope image,
Export the second gastroscope image；
Second processing module, for receiving the second gastroscope image, by the one-level in multiple second gastroscope images
Characteristic information and positional information are combined the region position that analysis forms secondary characteristics information and the correspondence secondary characteristics information
Confidence ceases, and output is labeled with the omasum mirror image of the secondary characteristics information and the zone position information；
Display module, for receiving the omasum mirror image and showing.
Further, the first processing module includes：
Pretreatment unit, for scanning the first gastroscope image, to there is focus characteristic on the first gastroscope image
Regional frame select and be marked, extract mark the cut zone in one-level characteristic information and the cut zone
Positional information in the gastroscope image；
Output unit is marked, the one-level feature letter for marking the correspondence cut zone on the first gastroscope image
Breath and positional information export the second gastroscope image as the second gastroscope image.
Further, the Second processing module includes：
First calculates output unit, and the second gastroscope image is carried for receiving, and extracts in multiple second gastroscopes
And characteristic information and positional information, multiple one-level characteristic informations are associated according to the positional information, combinatory analysis is closed
Multiple one-level characteristic information formation secondary characteristics information of connection, and determine the area on image of the secondary characteristics information
Domain scope, output is labeled with the omasum mirror image of the secondary characteristics information and the regional extent.
Further, the Second processing module also includes：
Second calculates output unit, and the second gastroscope image is carried for receiving, and extracts in multiple second gastroscopes
And characteristic information and positional information, multiple one-level characteristic informations are associated according to the positional information, combinatory analysis is closed
Multiple one-level characteristic information formation secondary characteristics information of connection, the region on image for determining the secondary characteristics information
Scope, the feature description information of the retrieval correspondence secondary characteristics information, output is labeled with the secondary characteristics information, the spy
Levy descriptive information and the omasum mirror image of the regional extent.
Further, the gastroscope image intelligent processing unit also includes：
Screening module, for the gastroscope image of acquisition to be carried out into initial screening, is divided into no stove feature by the gastroscope image
Gastroscope image and the gastroscope image for having focus characteristic.
The technical scheme that embodiments herein is provided can include the following benefits：
The application, which is split by the first gastroscope image to acquisition and is directed to cut zone, to be carried out feature extraction and marks
Note and form the second gastroscope image, then the progress of multiple second gastroscope images is integrated and draws the pathological characters letter with can be for reference
The omasum mirror image of breath, the omasum mirror image is given and shown, doctor can be helped to be diagnosed, and obtains pathology suggestion area
Domain, greatly reduce fail to pinpoint a disease in diagnosis, the wrong probability examined.
Brief description of the drawings
Fig. 1 is a kind of flow chart of gastroscope image intelligent processing method in the embodiment of the application one.
Fig. 2 is a kind of flow chart of gastroscope image intelligent processing method in another embodiment of the application.
Fig. 3 is a kind of module frame chart of gastroscope image intelligent processing unit in another embodiment of the application.
Embodiment
Here exemplary embodiment will be illustrated in detail, its example is illustrated in the accompanying drawings.Following description is related to
During accompanying drawing, unless otherwise indicated, the same numbers in different accompanying drawings represent same or analogous key element.Following exemplary embodiment
Described in embodiment do not represent all embodiments consistent with the application.On the contrary, they be only with it is such as appended
The example of the consistent apparatus and method of some aspects be described in detail in claims, the application.
It is the purpose only merely for description specific embodiment in term used in this application, and is not intended to be limiting the application.
" one kind ", " described " and "the" of singulative used in the application and appended claims are also intended to including majority
Form, unless context clearly shows that other implications.It is also understood that term "and/or" used herein refers to and wrapped
Containing one or more associated any or all combination for listing project.
Fig. 1 is a kind of flow chart of gastroscope image intelligent processing method in the embodiment of the application one.As shown in figure 1, described
Gastroscope image intelligent processing method comprises the following steps：
In step S101, dynamic access the first gastroscope image.
The first gastroscope image can be that pending image is obtained by gastroscope in this step, be obtained by optical treatment
Obtain white light or NBI (NarrowBand Imaging scope Narrow-Band Imagings art) gastroscope image.The gastroscope image is to above-mentioned
The real-time dynamic access of gastroscopic apparatus, the dynamic image for obtaining presetting time interval can be specifically pre-seted by one.Institute
The operation that stating the first gastroscope image can first pass through including rotation, the adjustment of aberration colour temperature in advance is pre-processed, and completes low performance
It is required that local image procossing.In addition, the first gastroscope image can also receive the information input of interactive display device.
In step s 102, the cut zone that there is focus characteristic on the first gastroscope image is partitioned into, described
The one-level characteristic information and positional information of the correspondence cut zone are marked on one gastroscope image as the second gastroscope image, output
The second gastroscope image.
In this step, the first gastroscope image is received, the first gastroscope image is entered using algorithm of target detection
Row region division, separates and feature extraction including focus.For example, by there may be disease on the first gastroscope image
Stove characteristic area is scanned, and the region that there may be focus characteristic on the first gastroscope image is split, and right
Cut zone carries out suspicious region mark.Then, the image information data cut zone that frame is selected obtained is as input, from
Scratched in one gastroscope image and select cut zone, the position relationship between record cut zone extracts the image of the cut zone
Comprising one-level characteristic information, including carry out the extraction of the features such as sharpness of border degree, color, surface flatness, form.Finally exist
Preliminary mark is carried out on the first gastroscope image, so as to obtain the second gastroscope image.Second gastroscope figure described in the step
As display can be interacted on the display device.
In step s 103, the second gastroscope image is received, by the one-level feature in multiple second gastroscope images
Information and positional information are combined the zone position that analysis forms secondary characteristics information and the correspondence secondary characteristics information
Breath, output is labeled with the omasum mirror image of the secondary characteristics information and the zone position information.
In this step, the second gastroscope image may carry multiple one-level characteristic informations and positional information, can pass through
Two or more one-level characteristic informations and positional information are combined analysis, multiple one-level features by operation rule
New feature will can be formed by operation rule, this feature turns into secondary characteristics information.Such as one-level characteristic information is certain figure
As field color is rubescent and form protuberance, " redness " focus characteristic is judged as according to specific operation rule, should " redness " focus
Feature is secondary characteristics information.And the zone position information of the correspondence secondary characteristics information can then pass through multiple one-levels
Positional information present in characteristic information is determined.In one embodiment, the zone position information is multiple one-level characteristic informations
The set in the region of rubicundity and form protuberance in the location sets on positioned image, such as certain image-region is exactly institute
State the zone position information of secondary characteristics information.
In the present embodiment, the extraction of secondary characteristics information mainly obtains two aspect information, one is obtaining secondary characteristics
Information region that may be present, this region depends on one-level characteristic information position that may be present, the second is obtaining two grades of spies
The specific features of reference breath, its focus characteristic information obtained dependent on the combinatory analysis of one-level characteristic information.It is described drawing
After secondary characteristics information and the zone position information, can by zone position information described in the secondary characteristics information labeling (
The region that image upper ledge is selected) on, omasum mirror image is formed, then exports to interactive display device and to be referred to for doctor.
In step S104, receive the omasum mirror image and show.
Receive the omasum mirror image and (two grades described above of the lesion information associated with the omasum mirror image
Characteristic information and the zone position information).In practical application, the omasum mirror is observed on the display device by local
Image, and noted image of the needs in the pathological tissue being locally extracted with reference to mark feature description on omasum mirror image and circle
Afterwards, linked groups cell extractions is carried out, facilitate the operation of doctor, while improving doctor's pathological analysis and locally diagnosing
Efficiency.
From above-described embodiment, the application is split by the first gastroscope image to acquisition and is directed to cut zone
Carry out feature extraction simultaneously to mark to form the second gastroscope image, then draw the progress integration of multiple second gastroscope images with being available for
The omasum mirror image of the pathological characters information of reference, the omasum mirror image is given and shown, doctor can be helped to be examined
It is disconnected, obtain pathology suggestion areas, greatly reduce fail to pinpoint a disease in diagnosis, the wrong probability examined.In addition, the application utilizes the extraction side of focus characteristic
Formula can improve imaging precision, scope, and focus is carried out to protrude imaging, especially has specific aim to cancerous tissue, can be accurate
Cancerous issue is marked out to aid in diagnosis.
Fig. 2 is a kind of flow chart of gastroscope image intelligent processing method in another embodiment of the application.As shown in Fig. 2 institute
Stating gastroscope image intelligent processing method includes：
In step s 201, IMAQ.
The dynamic image of gastroscopic apparatus collection is for example obtained, a time interval can be pre-set, is obtained in the time interval
Interior dynamic image, it is automatic to obtain without manual intervention.Obtain NBI gastroscopes image and be used as the first gastroscope image.In some embodiment party
The presetting pretreatment mode that can also be followed up in formula is pre-processed to the NBI gastroscopes image, it may for example comprise image pair
The processing such as burnt, increase image definition.
In step S202, initial screening image.
In this step, the NBI gastroscopes image of acquisition is subjected to initial screening, according to presetting screening rule by institute
NBI gastroscope images are stated to be divided into the NBI gastroscopes image of no stove feature and there are the NBI gastroscope images of focus characteristic.In one embodiment,
The image for meeting default feature can be filtered out from substantial amounts of image and preliminary judgement is carried out, and will confirm that focus characteristic
Image reaches interactive display device and shown, formula judgement can be interacted by doctor.For example, doctor to be determined with to the figure of focus characteristic
As notifying server that the image is further processed, doctor is judged that disease-free image notification server divides the image
To a disease-free class, operated without next step.So, access of the doctor to the NBI gastroscopes image is facilitated, doctor is improved
Operating efficiency.
In step S203, one-level feature extraction and mark.
In this step, can be special to there is focus on the first gastroscope image by scanning the first gastroscope image
The regional frame levied is selected and is marked, the one-level characteristic information and the cut zone in the cut zone of extraction mark
Positional information in the gastroscope image.The one-level feature of the correspondence cut zone is marked on the first gastroscope image
Information and positional information export the second gastroscope image as the second gastroscope image.
For example, after receiving the NBI gastroscopes image that primary dcreening operation diagnostic module is transmitted, using algorithm of target detection to NBI stomaches
Mirror image carries out region division and carries out first class index (one-level characteristic information) extraction.Extraction process including lesion segmentation and
Feature extraction, lesion segmentation is by by there may be the sector scanning of focus and on figure on the NBI gastroscopes image
Probability Area frame choosing is carried out, and carries out suspicious region mark；Feature extraction mainly completes the characteristic extraction procedure of suspicious region, will
Split obtained image information data as input, scratched from segmentation figure picture and select cut zone, carry out sharpness of border degree, face
The extraction of the features such as color, surface flatness, form, and preliminary mark is carried out on image, form the second gastroscope image.At this
In step, equally, so that doctor consults, doctor can be facilitated to understand image by the interaction of the second gastroscope image on the display device.
In step S204, combinatory analysis feature.
The second gastroscope image is carried for example, receiving, the second gastroscope image includes multiple first class index, can be by two
Plant or two or more above-mentioned first class index are combined analysis, multiple judgements by first class index to two-level index can be passed through and advised
Then, one-level feature calculation is turned into new feature, this feature turns into two-level index.Certainly, the decision rule can also be from branch
Hold interactive display device and update the new decision rule of input.For example, first class index is that rubicundity and form are swelled, two grades
Index then represents " redness " of multiple first class index formation.
In step S205, secondary characteristics are extracted and mark.
In this step, in multiple second gastroscopes and characteristic information and positional information are extracted, according to institute's rheme
Put the multiple one-level characteristic informations of information association, multiple one-level characteristic information formation secondary characteristics of combinatory analysis association
Information.Then and the regional extent on image of the secondary characteristics information is determined, circle notes the secondary characteristics information on the diagram
Position range.
In step S206, the choosing of image-region frame and explanation.
In this step, after the secondary characteristics information of response is extracted, correspondence can also be retrieved in database described
The feature description information (such as red and swollen feature and treatment means) of secondary characteristics information.Finally in NBI gastroscope image labelings
There are the secondary characteristics information, the feature description information and the regional extent, finally regard the NBI gastroscope images as the 3rd
Gastroscope image is exported.
In step S207, image is shown.
Omasum mirror image is shown on the display device, consulted for doctor.Said based on zone circle note and secondary characteristics information
Bright image, can help doctor to carry out respective organization cell extraction and pathological analysis, improve operating efficiency and the diagnosis of doctor
Accuracy.
Fig. 3 is a kind of module frame chart of gastroscope image intelligent processing unit in another embodiment of the application.As shown in figure 3,
The gastroscope image intelligent processing unit, including：
Acquisition module 301, for dynamic access the first gastroscope image；
First processing module 302, the cut zone that there is focus characteristic on the first gastroscope image for being partitioned into,
The one-level characteristic information and positional information that the correspondence cut zone is marked on the first gastroscope image are used as the second gastroscope figure
Picture, exports the second gastroscope image；
Second processing module 303, for receiving the second gastroscope image, by one in multiple second gastroscope images
Level characteristic information and positional information are combined the region that analysis forms secondary characteristics information and the correspondence secondary characteristics information
Positional information, output is labeled with the omasum mirror image of the secondary characteristics information and the zone position information；
Display module 304, for receiving the omasum mirror image and showing.
Further, the first processing module 302 includes：
Pretreatment unit, for scanning the first gastroscope image, to there is focus characteristic on the first gastroscope image
Regional frame select and be marked, extract mark the cut zone in one-level characteristic information and the cut zone
Positional information in the gastroscope image；
Output unit is marked, the one-level feature letter for marking the correspondence cut zone on the first gastroscope image
Breath and positional information export the second gastroscope image as the second gastroscope image.
Further, the Second processing module 303 includes：
First calculates output unit, and the second gastroscope image is carried for receiving, and extracts in multiple second gastroscopes
And characteristic information and positional information, multiple one-level characteristic informations are associated according to the positional information, combinatory analysis is closed
Multiple one-level characteristic information formation secondary characteristics information of connection, and determine the area on image of the secondary characteristics information
Domain scope, output is labeled with the omasum mirror image of the secondary characteristics information and the regional extent.
Further, the Second processing module 303 also includes：
Second calculates output unit, and the second gastroscope image is carried for receiving, and extracts in multiple second gastroscopes
And characteristic information and positional information, multiple one-level characteristic informations are associated according to the positional information, combinatory analysis is closed
Multiple one-level characteristic information formation secondary characteristics information of connection, the region on image for determining the secondary characteristics information
Scope, the feature description information of the retrieval correspondence secondary characteristics information, output is labeled with the secondary characteristics information, the spy
Levy descriptive information and the omasum mirror image of the regional extent.
Further, the gastroscope image intelligent processing unit also includes：
Screening module, for the gastroscope image of acquisition to be carried out into initial screening, is divided into no stove feature by the gastroscope image
Gastroscope image and the gastroscope image for having focus characteristic.
For device embodiment, because it corresponds essentially to embodiment of the method, so related part is real referring to method
Apply the part explanation of example.Device embodiment described above is only schematical, wherein described be used as separating component
The unit of explanation can be or may not be physically separate, and the part shown as unit can be or can also
It is not physical location, you can with positioned at a place, or can also be distributed on multiple NEs.Can be according to reality
Selection some or all of module therein is needed to realize the purpose of application scheme.Those of ordinary skill in the art are not paying
In the case of going out creative work, you can to understand and implement.
The preferred embodiment of the application is the foregoing is only, not to limit the application, all essences in the application
God is with principle, and any modification, equivalent substitution and improvements done etc. should be included within the scope of the application protection.
Claims (10)
1. a kind of gastroscope image intelligent processing method, it is characterised in that comprise the following steps：
Dynamic access the first gastroscope image；
The cut zone that there is focus characteristic on the first gastroscope image is partitioned into, the mark pair on the first gastroscope image
Answer the one-level characteristic information and positional information of the cut zone as the second gastroscope image, export the second gastroscope image；
The second gastroscope image is received, the one-level characteristic information in multiple second gastroscope images and positional information are carried out
Combinatory analysis forms the zone position information of secondary characteristics information and the correspondence secondary characteristics information, and output is labeled with described two
The omasum mirror image of level characteristic information and the zone position information；
Receive the omasum mirror image and show.
2. gastroscope image intelligent processing method according to claim 1, it is characterised in that described to be partitioned into the first stomach
There is the cut zone of focus characteristic on mirror image, the one-level of the correspondence cut zone is marked on the first gastroscope image
Characteristic information and positional information export the second gastroscope image as the second gastroscope image, including：
The first gastroscope image being scanned, rower of going forward side by side is selected to there is the regional frame of focus characteristic on the first gastroscope image
Note, extracts the one-level characteristic information in the cut zone of mark and the position in the gastroscope image of the cut zone
Confidence ceases；
The one-level characteristic information and positional information that the correspondence cut zone is marked on the first gastroscope image are used as second
Gastroscope image, exports the second gastroscope image.
3. gastroscope image intelligent processing method according to claim 1, it is characterised in that reception second gastroscope
Image, the one-level characteristic information and positional information in multiple second gastroscope images are combined analysis to form two grades of spies
The zone position information of reference breath and the correspondence secondary characteristics information, output is labeled with secondary characteristics information and zone position
The omasum mirror image of breath, including：
Receive and carry the second gastroscope image, extract in multiple second gastroscopes and characteristic information and positional information,
Multiple one-level characteristic informations, multiple one-level characteristic information shapes of combinatory analysis association are associated according to the positional information
Into secondary characteristics information, and the regional extent on image of the secondary characteristics information is determined, output is labeled with described two grades
The omasum mirror image of characteristic information and the regional extent.
4. gastroscope image intelligent processing method according to claim 3, it is characterised in that the reception carries described second
Gastroscope image, extracts in multiple second gastroscopes and characteristic information and positional information, is associated according to the positional information
Multiple one-level characteristic informations, multiple one-level characteristic information formation secondary characteristics information of combinatory analysis association, and really
The regional extent on image of the fixed secondary characteristics information, output is labeled with the secondary characteristics information and the region model
The omasum mirror image enclosed, including：
Receive and carry the second gastroscope image, extract in multiple second gastroscopes and characteristic information and positional information,
Multiple one-level characteristic informations, multiple one-level characteristic information shapes of combinatory analysis association are associated according to the positional information
Into secondary characteristics information, determine the regional extent on image of the secondary characteristics information, the retrieval correspondence secondary characteristics
The feature description information of information, output is labeled with the secondary characteristics information, the feature description information and the regional extent
Omasum mirror image.
5. gastroscope image intelligent processing method according to claim 1, it is characterised in that the gastroscope of dynamic access first
It is partitioned into after image before the cut zone that there is focus characteristic on the first gastroscope image, in addition to：
The gastroscope image of acquisition is subjected to initial screening, the gastroscope image is divided into the gastroscope image of no stove feature and has focus special
The gastroscope image levied.
6. a kind of gastroscope image intelligent processing unit, it is characterised in that including：
Acquisition module, for dynamic access the first gastroscope image；
First processing module, the cut zone that there is focus characteristic on the first gastroscope image for being partitioned into, described
The one-level characteristic information and positional information of the correspondence cut zone are marked on one gastroscope image as the second gastroscope image, output
The second gastroscope image；
Second processing module, for receiving the second gastroscope image, by the one-level feature in multiple second gastroscope images
Information and positional information are combined the zone position that analysis forms secondary characteristics information and the correspondence secondary characteristics information
Breath, output is labeled with the omasum mirror image of the secondary characteristics information and the zone position information；
Display module, for receiving the omasum mirror image and showing.
7. gastroscope image intelligent processing unit according to claim 6, it is characterised in that the first processing module bag
Include：
Pretreatment unit, for scanning the first gastroscope image, to there is the area of focus characteristic on the first gastroscope image
Domain frame is selected and is marked, extract mark the cut zone in one-level characteristic information and the cut zone described
Positional information in gastroscope image；
Mark output unit, for marked on the first gastroscope image the correspondence cut zone one-level characteristic information and
Positional information exports the second gastroscope image as the second gastroscope image.
8. gastroscope image intelligent processing unit according to claim 6, it is characterised in that the Second processing module bag
Include：
First calculates output unit, and the second gastroscope image is carried for receiving, extract in multiple second gastroscopes with
And characteristic information and positional information, multiple one-level characteristic informations are associated according to the positional information, combinatory analysis association
Multiple one-level characteristic information formation secondary characteristics information, and determine the region model on image of the secondary characteristics information
Enclose, output is labeled with the omasum mirror image of the secondary characteristics information and the regional extent.
9. gastroscope image intelligent processing unit according to claim 8, it is characterised in that the Second processing module is also wrapped
Include：
Second calculates output unit, and the second gastroscope image is carried for receiving, extract in multiple second gastroscopes with
And characteristic information and positional information, multiple one-level characteristic informations are associated according to the positional information, combinatory analysis association
Multiple one-level characteristic information formation secondary characteristics information, the region model on image for determining the secondary characteristics information
Enclose, the feature description information of the retrieval correspondence secondary characteristics information, output is labeled with the secondary characteristics information, the feature
The omasum mirror image of descriptive information and the regional extent.
10. gastroscope image intelligent processing unit according to claim 6, it is characterised in that also include：
Screening module, for the gastroscope image of acquisition to be carried out into initial screening, the gastroscope image is divided into the gastroscope of no stove feature
Image and the gastroscope image for having focus characteristic.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107730489A (en) * | 2017-10-09 | 2018-02-23 | 杭州电子科技大学 | Wireless capsule endoscope small intestine disease variant computer assisted detection system and detection method |
| CN109461147A (en) * | 2018-10-26 | 2019-03-12 | 广州金域医学检验中心有限公司 | The pathology mask method and device of FOV picture applied to mobile terminal |
| CN112263740A (en) * | 2020-10-09 | 2021-01-26 | 湘南学院附属医院 | Clinical multi-head reinforced gastric lavage device for digestive system department and control method |
| CN112991325A (en) * | 2021-04-14 | 2021-06-18 | 上海孚慈医疗科技有限公司 | Intelligent coding-based speckled red-emitting image acquisition and processing method and system |
| CN116965765A (en) * | 2023-08-01 | 2023-10-31 | 西安交通大学医学院第二附属医院 | Early gastric cancer endoscope real-time auxiliary detection system based on target detection algorithm |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1912927A (en) * | 2006-08-25 | 2007-02-14 | 西安理工大学 | Semi-automatic partition method of lung CT image focus |
| CN1943513A (en) * | 2006-11-08 | 2007-04-11 | 沈阳东软医疗系统有限公司 | Processing device method and system for CT image |
| US20140016844A1 (en) * | 2012-07-10 | 2014-01-16 | Zakrytoe Akcionernoe Obshchestvo "Impul's" | Method for acquisition of subtraction angiograms |
| CN104616296A (en) * | 2015-01-23 | 2015-05-13 | 上海联影医疗科技有限公司 | Method and device for improving quality of radiotherapy images and radiotherapy system |
| CN105894517A (en) * | 2016-04-22 | 2016-08-24 | 北京理工大学 | CT image liver segmentation method and system based on characteristic learning |
| CN106097335A (en) * | 2016-06-08 | 2016-11-09 | 安翰光电技术（武汉）有限公司 | Digestive tract focus image identification system and recognition methods |
-
2017
- 2017-06-09 CN CN201710434430.8A patent/CN107230198B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1912927A (en) * | 2006-08-25 | 2007-02-14 | 西安理工大学 | Semi-automatic partition method of lung CT image focus |
| CN1943513A (en) * | 2006-11-08 | 2007-04-11 | 沈阳东软医疗系统有限公司 | Processing device method and system for CT image |
| US20140016844A1 (en) * | 2012-07-10 | 2014-01-16 | Zakrytoe Akcionernoe Obshchestvo "Impul's" | Method for acquisition of subtraction angiograms |
| CN104616296A (en) * | 2015-01-23 | 2015-05-13 | 上海联影医疗科技有限公司 | Method and device for improving quality of radiotherapy images and radiotherapy system |
| CN105894517A (en) * | 2016-04-22 | 2016-08-24 | 北京理工大学 | CT image liver segmentation method and system based on characteristic learning |
| CN106097335A (en) * | 2016-06-08 | 2016-11-09 | 安翰光电技术（武汉）有限公司 | Digestive tract focus image identification system and recognition methods |
Non-Patent Citations (2)
| Title |
|---|
| PAN LIANG等: "A Computer-Aided Lesion Diagnose Method Based on Gastroscopeimage", 《PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION》 * |
| 关沫等: "基于胃镜图像的病灶区域检测方法研究", 《微型机与应用》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107730489A (en) * | 2017-10-09 | 2018-02-23 | 杭州电子科技大学 | Wireless capsule endoscope small intestine disease variant computer assisted detection system and detection method |
| CN109461147A (en) * | 2018-10-26 | 2019-03-12 | 广州金域医学检验中心有限公司 | The pathology mask method and device of FOV picture applied to mobile terminal |
| CN109461147B (en) * | 2018-10-26 | 2020-05-19 | 广州金域医学检验中心有限公司 | Pathological labeling method and device applied to FOV picture of mobile terminal |
| CN112263740A (en) * | 2020-10-09 | 2021-01-26 | 湘南学院附属医院 | Clinical multi-head reinforced gastric lavage device for digestive system department and control method |
| CN112991325A (en) * | 2021-04-14 | 2021-06-18 | 上海孚慈医疗科技有限公司 | Intelligent coding-based speckled red-emitting image acquisition and processing method and system |
| CN116965765A (en) * | 2023-08-01 | 2023-10-31 | 西安交通大学医学院第二附属医院 | Early gastric cancer endoscope real-time auxiliary detection system based on target detection algorithm |
| CN116965765B (en) * | 2023-08-01 | 2024-03-08 | 西安交通大学医学院第二附属医院 | Early gastric cancer endoscope real-time auxiliary detection system based on target detection algorithm |
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