CN110459299A - A kind of retina color fundus photograph image screening technique - Google Patents
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- 210000001525 retina Anatomy 0.000 title claims abstract description 39
- 238000012216 screening Methods 0.000 title claims abstract description 26
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000013316 zoning Methods 0.000 claims abstract description 13
- 238000004364 calculation method Methods 0.000 claims description 6
- 241001269238 Data Species 0.000 claims description 5
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000003759 clinical diagnosis Methods 0.000 abstract description 3
- 238000012545 processing Methods 0.000 abstract description 3
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- 201000010099 disease Diseases 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 230000000052 comparative effect Effects 0.000 description 2
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- 230000002207 retinal effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
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- 208000001491 myopia Diseases 0.000 description 1
- 230000004379 myopia Effects 0.000 description 1
- 231100000915 pathological change Toxicity 0.000 description 1
- 230000036285 pathological change Effects 0.000 description 1
- 230000003716 rejuvenation Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
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- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
- A61B3/12—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions for looking at the eye fundus, e.g. ophthalmoscopes
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- A—HUMAN NECESSITIES
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
- A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
- A61B3/14—Arrangements specially adapted for eye photography
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Abstract
The invention belongs to technical field of medical image processing, it is related to handling extensive retina color fundus photograph image using the script of automation, by retina color fundus photograph image from containing screening in a large amount of eye appearance images.The present invention filters out the eye fundus image of high quality using image data screening technique from extensive retina color fundus picture image data, allow user to modify image similarity zoning and whether select cutting image, finally obtains the high quality retina color fundus picture image data that can be used for clinical diagnosis and intelligent algorithm exploitation.
Description
Technical field
The present invention relates to technical field of medical image processing, more specifically, being a kind of retina color fundus photograph image
Screening technique.
Background technique
In recent years, with the improvement of people's living standards, high-intensitive life, unsound life style, being gradually increased
Operating pressure rises the illness rate of fundus oculi disease constantly, and gradually develops to rejuvenation.Further, since undesirable study
Habit, adolescent myopia number are also riseing year after year.Sudden, the seriousness of these ophthalmology diseases have seriously jeopardized people
Life.Therefore, had according to retina color fundus photo-early detection ophthalmology disease related to correct diagnosis extremely important
Effect.
Currently, being all to carry out read tablet to each eye fundus image by oculist to obtain for the dependent diagnostic of eyeground pathological changes
.Therefore, the eye fundus image of high quality has a very important role for clinical diagnosis, such as obtains ophthalmology diagnosis level
It is further promoted, patient is made to enjoy more good medical services etc..Meanwhile with the fast development of computer technology, utilize
Artificial intelligence technology is analyzed eye fundus image the research hotspot for also becoming medical field, due to artificial intelligence related algorithm mould
Based on type needs a large amount of data, therefore, the data for how being quickly obtained high quality also become limit algorithm model efficiency
The major reason of promotion.
Since various eye fundus image acquisition equipment qualities are irregular at present, image taking personnel lack training, cause to obtain
Although the image quantity obtained is very big but not can guarantee quality, further, then it is not used to the exploitation of intelligent algorithm, is caused
Medical artificial intelligence's progress is slow.Therefore, how to be filtered out from extensive retina color fundus picture image data
The can be used for diagnosis and the image data of intelligent algorithm model development of high quality also become a great problem.
Summary of the invention
For can not filter out high quality from extensive retina color fundus picture image data in the prior art
The problem of image data, the present invention provides a kind of retina color fundus photograph image screening technique,
A kind of retina color fundus photograph image screening technique, the retina color fundus photograph image screening technique
The following steps are included:
S1. batch pretreatment is carried out to the retina color fundus picture image data of distinct device acquisition；
S2. select an image as standard form image from pretreated image data, by image to be screened with
Standard form image carries out image similarity calculating；
S3. similarity threshold is arranged according to image similarity calculated result, is filtered out and is met the requirements according to similarity threshold
Image and obtain the attribute information of image；
S4. using the attribute information for the image for meeting similarity threshold and the progress of the attribute information of all image datas
Match, completes the screening of retina color fundus photograph image.
Further, the pre- place of batch is carried out to the retina color fundus picture image data of distinct device acquisition in the S1
Reason, includes but are not limited to: image grayscale conversion, z-score standardization, limitation contrast self-adapting histogram equilibrium, Gamma
Correction, data normalization.
Further, specific step is as follows by the S2:
S21. from pretreated all images, user is allowed therefrom to select an image as standard form image,
Since the difference between image data is present within the scope of specific pixel, it is therefore desirable to the pixel coverage of comparative selection similarity,
Set similarity calculation region；Meanwhile user chooses whether to carry out the image in setting range when carrying out similarity comparison
It cuts and generates cutting information, i.e., zoning is divided into different size of image block；
S22. the setting according to user to image similarity zoning, while the cutting that zoning will be cut
Information operating is divided into different size of image block into image to be screened, by zoning cutting, successively carries out figure to be screened
The calculating of the image similarity of the image block of the image block and standard form image of picture, the similarity for obtaining image to be screened refer to
Number.
Further, include that threshold value is arranged according to image similarity calculated result in the S3, filter out satisfactory figure
Picture and the attribute information for obtaining image, the specific steps are as follows: the calculated result based on image similarity sets similarity threshold,
The image that the index similarity of image is greater than similarity threshold be the image that screens of needs, will meet the image of similarity threshold into
Row screening, obtains the attribute information for meeting the image of similarity threshold.Wherein, the attribute information of described image includes, image
Title, format information.
Further, specific step is as follows by the S4: using the attribute information of the image for meeting similarity threshold and all
The attribute information of image data matched, extract image identical with the attribute information for the image for meeting similarity threshold,
Complete the screening to retina color fundus photograph image.
Compared with prior art, the beneficial effect of technical solution of the present invention is:
The present invention is filtered out from extensive retina color fundus picture image data using image data screening technique
The eye fundus image of high quality allows user to modify image similarity zoning and whether select cutting image, finally obtains
It can be used for the high quality retina color fundus picture image data of clinical diagnosis and intelligent algorithm exploitation.
Detailed description of the invention
Fig. 1 is the flow chart of retina color fundus photograph image screening technique preferred embodiment of the present invention；
Fig. 2 is collected retina color fundus photograph image；
Fig. 3 is eye appearance images；
Fig. 4 is the retinal fundus images after pretreatment；
Fig. 5 delineates similarity calculation region for user's, and A is the area size that user can set.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, only for illustration, Bu Nengli
Solution is the limitation to this patent.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative labor
Every other embodiment obtained under the premise of dynamic, shall fall within the protection scope of the present invention.
The following further describes the technical solution of the present invention with reference to the accompanying drawings and examples.
Embodiment 1
Referring to FIG. 1, it is the stream of retina color fundus photograph image screening technique preferred embodiment of the present invention
Cheng Tu.As shown in Figure 1, the retina color fundus photograph image screening technique the following steps are included:
S1. batch pretreatment is carried out to the retina color fundus picture image data of distinct device acquisition；
S2. select an image as standard form image from pretreated image data, by image to be screened with
Standard form image carries out image similarity calculating；
S3. similarity threshold is arranged according to image similarity calculated result, is filtered out and is met the requirements according to similarity threshold
Image and obtain the attribute information of image；
S4. using the attribute information for the image for meeting similarity threshold all image datas attribute information carry out
Match, extract image identical with the attribute information for the image for meeting similarity threshold, completes to retina color fundus photo figure
The screening of picture.
Embodiment 2
The method and embodiment 1 of retina color fundus photograph image screening provided in this embodiment are consistent, only to each
Step is further illustrated.
Scheme of the present invention, concrete implementation process are as follows:
S1. first acquisition distinct device shooting retina color fundus picture image data, to collected data into
Row pretreatment, including but not limited to image grayscale conversion, z-score standardization, limitation contrast self-adapting histogram equilibrium,
Gamma correction, data normalization.Fig. 2 is collected retina color fundus photograph image, and Fig. 3 is eye outside drawing
Picture, Fig. 4 are the retinal fundus images after pretreatment.
S21. selected from pretreated image data one as standard form image, to similarity calculation region into
Row sets and chooses whether to make dividing processing to image, the specific steps are as follows: from pretreated all images, allows to use
Family therefrom selects an image as standard form image, since the difference between image data is present in specific pixel range
It is interior, it is therefore desirable to which that the pixel coverage of comparative selection similarity sets similarity calculation region；Meanwhile user can choose into
When row similarity comparison, judges whether to need to cut the image in setting range and generate cutting information, area will be calculated
Domain is divided into different size of image block, and Fig. 5 is that user delineates similarity calculation region, and A is the area size that user can set.
S22. the setting according to user to image similarity zoning, while the cutting that zoning will be cut
Information operating is divided into different size of image block into image to be screened, by zoning cutting, successively carries out figure to be screened
The calculating of the image similarity of the image block of the image block and standard form image of picture, the similarity for obtaining image to be screened refer to
Number.
S3. threshold value is arranged according to image similarity calculated result, filters out satisfactory image and obtains the category of image
Property information, specifically: the calculated result based on image similarity sets similarity threshold, and the index similarity of image is greater than phase
Image like degree threshold value is the image for needing to screen, and the image for meeting similarity threshold is screened, and acquisition meets similarity
The attribute information of the image of threshold value.Wherein, the attribute information of described image includes the title of image, format information.
S4. the image name using the attribute information for the image for meeting similarity threshold in all image datas carries out
Image identical with the attribute information for the image for meeting similarity threshold is extracted in matching, completes to retina color fundus photo
The screening of image.
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.
Claims (6)
1. a kind of retina color fundus photograph image screening technique, which comprises the following steps:
S1. batch pretreatment is carried out to the retina color fundus picture image data of distinct device acquisition；
S2. select an image as standard form image from pretreated image data, by image to be screened and standard
Template image carries out image similarity calculating；
S3. similarity threshold is arranged according to image similarity calculated result, satisfactory figure is filtered out according to similarity threshold
Picture and the attribute information for obtaining image；
S4. it is matched using the attribute information for the image for meeting similarity threshold with the attribute information of all image datas,
Complete the screening of retina color fundus photograph image.
2. retina color fundus photograph image screening technique as described in claim 1, which is characterized in that the S1's is specific
Steps are as follows: the retina color fundus picture image data successively carries out image grayscale conversion, z-score is standardized,
The step of limiting contrast self-adapting histogram equilibrium, Gamma correction, data normalization.
3. retina color fundus photograph image screening technique as claimed in claim 2, which is characterized in that the S2's is specific
Steps are as follows:
S21. from pretreated all images, user is allowed therefrom to select an image as standard form image, to figure
As similarity calculation region is set；Meanwhile user judges whether to need to setting when selection carries out image similarity comparison
Determine the image in range and cut and generated corresponding cutting information, i.e., zoning is divided into different size of image block；
S22. the setting according to user to image similarity zoning, while the cutting information that zoning will be cut
It applies in image to be screened, zoning cutting is divided into different size of image block, successively carries out image to be screened
The calculating of the image similarity of the image block of image block and standard form image, obtains the index similarity of image to be screened.
4. retina color fundus photograph image screening technique as claimed in claim 3, which is characterized in that the S3's is specific
Steps are as follows: the calculated result based on image similarity, sets similarity threshold, and the index similarity of image is greater than similarity threshold
The image of value is the image for needing to screen, and the image for meeting similarity threshold is screened, and acquisition meets similarity threshold
The attribute information of image.
5. retina color fundus photograph image screening technique as claimed in claim 4, which is characterized in that the category of described image
Property information includes: the title of image, format information.
6. retina color fundus photograph image screening technique as described in claim 1, which is characterized in that the S4's is specific
Steps are as follows: using the attribute information for the image for meeting similarity threshold and the attribute information progress of all image datas
Match, extract image identical with the attribute information for the image for meeting similarity threshold, completes to retina color fundus photo figure
The screening of picture.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022042348A1 (en) * | 2020-08-26 | 2022-03-03 | 北京至真互联网技术有限公司 | Medical image annotation method and apparatus, device, and storage medium |
| CN116363132A (en) * | 2023-06-01 | 2023-06-30 | 中南大学湘雅医院 | Ophthalmic image processing method and system |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103870838A (en) * | 2014-03-05 | 2014-06-18 | 南京航空航天大学 | Eye fundus image characteristics extraction method for diabetic retinopathy |
| CN106530295A (en) * | 2016-11-07 | 2017-03-22 | 首都医科大学 | Fundus image classification method and device of retinopathy |
| CN106780439A (en) * | 2016-11-29 | 2017-05-31 | 瑞达昇科技(大连)有限公司 | A kind of method for screening eye fundus image |
| CN107423571A (en) * | 2017-05-04 | 2017-12-01 | 深圳硅基仿生科技有限公司 | Diabetic retinopathy identifying system based on eye fundus image |
| CN107832452A (en) * | 2017-11-23 | 2018-03-23 | 苏州亿科赛卓电子科技有限公司 | A kind of photo management method and device |
| CN108229574A (en) * | 2018-01-18 | 2018-06-29 | 维沃移动通信有限公司 | A kind of method, apparatus and mobile terminal of picture screening |
| CN108416371A (en) * | 2018-02-11 | 2018-08-17 | 艾视医疗科技成都有限公司 | A kind of diabetic retinopathy automatic testing method |
| CN108629769A (en) * | 2018-05-02 | 2018-10-09 | 山东师范大学 | Eye fundus image optic disk localization method and system based on best fraternal similarity |
| CN109166117A (en) * | 2018-08-31 | 2019-01-08 | 福州依影健康科技有限公司 | A kind of eye fundus image automatically analyzes comparison method and a kind of storage equipment |
| CN109993079A (en) * | 2019-03-19 | 2019-07-09 | 嘉兴智设信息科技有限公司 | A kind of indoor design picture recognition and screening technique and its device |
-
2019
- 2019-07-10 CN CN201910619610.2A patent/CN110459299B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103870838A (en) * | 2014-03-05 | 2014-06-18 | 南京航空航天大学 | Eye fundus image characteristics extraction method for diabetic retinopathy |
| CN106530295A (en) * | 2016-11-07 | 2017-03-22 | 首都医科大学 | Fundus image classification method and device of retinopathy |
| CN106780439A (en) * | 2016-11-29 | 2017-05-31 | 瑞达昇科技(大连)有限公司 | A kind of method for screening eye fundus image |
| CN107423571A (en) * | 2017-05-04 | 2017-12-01 | 深圳硅基仿生科技有限公司 | Diabetic retinopathy identifying system based on eye fundus image |
| CN107832452A (en) * | 2017-11-23 | 2018-03-23 | 苏州亿科赛卓电子科技有限公司 | A kind of photo management method and device |
| CN108229574A (en) * | 2018-01-18 | 2018-06-29 | 维沃移动通信有限公司 | A kind of method, apparatus and mobile terminal of picture screening |
| CN108416371A (en) * | 2018-02-11 | 2018-08-17 | 艾视医疗科技成都有限公司 | A kind of diabetic retinopathy automatic testing method |
| CN108629769A (en) * | 2018-05-02 | 2018-10-09 | 山东师范大学 | Eye fundus image optic disk localization method and system based on best fraternal similarity |
| CN109166117A (en) * | 2018-08-31 | 2019-01-08 | 福州依影健康科技有限公司 | A kind of eye fundus image automatically analyzes comparison method and a kind of storage equipment |
| CN109993079A (en) * | 2019-03-19 | 2019-07-09 | 嘉兴智设信息科技有限公司 | A kind of indoor design picture recognition and screening technique and its device |
Non-Patent Citations (1)
| Title |
|---|
| 李达辉: "《数字图像处理核心技术及应用》", 31 March 2019, 电子科技大学出版社, pages: 149 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022042348A1 (en) * | 2020-08-26 | 2022-03-03 | 北京至真互联网技术有限公司 | Medical image annotation method and apparatus, device, and storage medium |
| CN116363132A (en) * | 2023-06-01 | 2023-06-30 | 中南大学湘雅医院 | Ophthalmic image processing method and system |
| CN116363132B (en) * | 2023-06-01 | 2023-08-22 | 中南大学湘雅医院 | Ophthalmic image processing method and system |
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