CN109117837A - Area-of-interest determines method and apparatus - Google Patents
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Abstract
The present invention provides a kind of area-of-interest and determines method and apparatus, which comprises obtains the prime area that user selectes in the picture；Multiple first seed points are extracted in the prime area；Region growing is carried out based on the multiple first seed point to determine at least one growth district；Area-of-interest is determined according at least one described growth district and the prime area.
Description
Technical field
The present invention relates to field of image processings, and in particular to a kind of area-of-interest determines method and apparatus.
Background technique
By machine learning algorithm and model image is carried out identification be it is a kind of it is efficient in the way of, and such as drive automatically
It sails, the Floor layer Technology of the various fields such as intelligent camera, robot.
Before carrying out image recognition using machine learning model (such as neural network), first have to using sample image to mould
Type is trained, and trained mode is usually: 1. by manually marking interesting target in the picture, and generates target area mark
Know information；2. the identification information and image that are generated using mark input deep neural network together；3. training deep neural network,
To its convergence.Then it then can use trained machine learning model to identify from image and mark interesting target.
During manually marking interesting target in the picture, need labeler according to the position of interesting target
And situations such as shape, carries out hand drawing.Fig. 1 shows the eye fundus image in a secondary medical field, wherein including a diseased region
Domain, the profile in the region are an irregular polygons.Labeler needs to mark out the lesion region in the picture, schemes at present
The mark means of picture are normally based on the annotation tools such as Labelimg and smart mark assistant, use the polygons such as circle, rectangle
The lesion characteristics of eye fundus image are marked, there is very big error between the profile in the region marked and the profile of target.
Above-mentioned annotation results are only able to satisfy the demand of image recognition and detection, are not able to satisfy more advanced demand but, such as scheme
The demand that interesting target is split.When facing more advanced demand, the profile for generally requiring interesting target is carried out
More accurate mark, if a large amount of manpower and time cost will be consumed using artificial mark.
Summary of the invention
In view of this, the present invention, which provides a kind of area-of-interest, determines method, comprising:
Obtain the prime area that user selectes in the picture；
Multiple first seed points are extracted in the prime area；
Region growing is carried out based on the multiple first seed point to determine at least one growth district；
Area-of-interest is determined according at least one described growth district and the prime area.
Preferably, the prime area for obtaining user and selecting in the picture, comprising:
Obtain user in the picture sketch the contours track；
The prime area is determined according to the track of sketching the contours.
It is preferably, described that multiple first seed points are extracted in the prime area, comprising:
Obtain whole pixels in the prime area；
Corrosion treatment is carried out to filter out multiple discrete pixels as the first seed point to whole pixels.
Preferably, it is described based on the multiple first seed point carry out region growing with determine at least one growth district,
Include:
The characteristic value of the multiple first seed point is extracted respectively；
Determine the profile in described image；
Respectively using the multiple first seed point as starting point, region growing is carried out with first condition and second condition,
At least one growth district is obtained, wherein whether the difference that the first condition is the characteristic value of neighbor pixel is less than default threshold
Value, the second condition are whether pixel is pixel on the profile.
It preferably, include whole growth districts and the prime area in the area-of-interest.
Preferably, described at least one growth district according to and the prime area determine area-of-interest, comprising:
The pixel in described image is traversed, judges whether each pixel belongs to the growth district and/or institute respectively
State prime area；
Area-of-interest is determined according to the pixel in the growth district and/or the prime area.
Preferably, the pixel according in the growth district and/or the prime area determines area-of-interest,
Include:
Label belongs to the prime area and is not belonging to the pixel of the growth district；
Multiple second seed points are selected from the pixel of label；
Region growing, which is carried out, based on selected second seed point supplements region to determine；
The region of pixel composition in the supplement region, the growth district and the prime area is determined as institute
State area-of-interest.
Preferably, multiple second seed points are selected in the pixel from label, comprising:
Obtain all labeled pixel；
Corrosion treatment is carried out to filter out multiple discrete pixels as second seed to all labeled pixel
Point.
It is preferably, described that region growing is carried out to determine supplement region based on selected second seed point, comprising:
The characteristic value of the multiple second seed point is extracted respectively；
Determine the profile in described image；
Respectively using the multiple second seed point as starting point, with the progress of first condition, second condition and third condition
Region growing, obtain at least one supplement region, wherein the first condition be neighbor pixel characteristic value difference whether
Less than preset threshold, the second condition is whether pixel is pixel on the profile, and the third condition is pixel
Whether the characteristic value and mean eigenvalue difference of point are less than the preset threshold, and the mean eigenvalue is all labeled picture
The average value of the characteristic value of vegetarian refreshments.
The present invention also endures a kind of electronic equipment characterized by comprising at least one processor；And with it is described at least
The memory of one processor communication connection；Wherein, the memory is stored with the instruction that can be executed by one processor,
Described instruction is executed by least one described processor, so that at least one described processor executes above-mentioned area-of-interest and determines
Method.
The area-of-interest provided according to the present invention determines method and apparatus, and user substantially given in the picture can feel emerging
The position of interesting target draws prime area, then this programme will extract seed point in prime area automatically, and be based on seed
Point carries out region growing and determines growth district, determines area-of-interest eventually by growth district and prime area, thus
To the profile of area-of-interest be more nearly the actual profile of interesting target, even up to identical effect, with this
The operation of user is reduced, so that the mark of interesting target is more accurate, achievees the purpose that save manpower and time cost.
Detailed description of the invention
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution in the prior art
Embodiment or attached drawing needed to be used in the description of the prior art be briefly described, it should be apparent that, it is described below
Attached drawing is some embodiments of the present invention, for those of ordinary skill in the art, before not making the creative labor
It puts, is also possible to obtain other drawings based on these drawings.
Fig. 1 is the eye fundus image that a width has lesion region；
Fig. 2 is the flow chart that area-of-interest provided by the invention determines method；
Fig. 3 is the example image of a determining area-of-interest process；
Fig. 4 is the eye fundus image to be processed in a specific embodiment of the invention；
Fig. 5 is the flow chart that area-of-interest in a specific embodiment of the invention determines method；
Fig. 6 is that user carries out the result images after manual smearing to image shown in Fig. 4；
Fig. 7 is that the result images after the growth of sub-region are carried out based on content shown in Fig. 6；
Fig. 8 is area-of-interest fruit image determined by the content based on shown in Fig. 6 and Fig. 7.
Specific embodiment
Technical solution of the present invention is clearly and completely described below in conjunction with attached drawing, it is clear that described implementation
Example is a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill
Personnel's every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
As long as in addition, the non-structure each other of technical characteristic involved in invention described below different embodiments
It can be combined with each other at conflict.
The embodiment of the present invention provides a kind of area-of-interest and determines that method, this method can be by server or personal computers
Electronic equipments are waited to execute, this method can be used for making sample image, bound fraction manual operation, automatically to sample image
It is labeled.As shown in Fig. 2, this method comprises the following steps:
S1, obtains the prime area that user selectes in the picture, which can be any for training machine study mould
The image of type, includes certain interesting targets in image, and this target can be arbitrary shape.Fig. 3 show one it is exemplary
Image, including an interesting target 31 with irregular contour, which has the color value for being different from background.
User can carry out handmarking in original image first, such as draw lines or smear a region in target zone, namely
Selected prime area 32, the prime area 32 are also possible to arbitrary shape, without the profile in strict accordance with interesting target into
Row is smeared or is delineated.
S2 extracts multiple first seed points in prime area, and there are many modes for extracting seed point in the zone, such as
It can be calculated, be filtered out with outstanding feature according to rgb value, the HSV value of pixel in the shape of prime area, region
Some pixels are as seed point.These seed points can be discrete distribution, can also be continuously distributed, specific to be distributed
Mode depends on extraction algorithm.After this step process, 4 the first seed points 33 can be determined in image shown in Fig. 3.
S3 carries out region growing based on multiple first seed points to determine at least one growth district.The calculation of region growing
Method include it is a variety of, condition, dependent thresholds and the parameter of region growing can be preset according to image actual conditions, such as
It can be and given birth to according to the edge in the rgb value of neighbor pixel, the relationship of the difference of HSV value and given threshold and image
It is long.Each first seed point is grown separately as starting point, and obtained growth district may be coincidence, it is also possible to
It partially overlaps or is not overlapped.Such as there is the boundary of the corresponding growth district of multiple first seed points may phase mutual respect
Folded namely multiple growth districts are connected and become a bigger growth district, therefore finally obtained growth district may be
One or more, the corresponding relationship that the quantity of quantity and the first seed point is not specified by, the pixel in these growth districts
The feature and corresponding first seed point similarity of point are higher.Still by taking Fig. 3 as an example, based on 4 the first seed points 33 in image,
It is likely to be obtained two growth districts 34.
S4 determines area-of-interest, the positional relationship of growth district and prime area according to growth district and prime area
A variety of situations may be presented, such as all growth districts are all in prime area, or a part in prime area, it is another
Part is outside prime area, namely the case where may completely include and partially overlap.Since the first seed point is inevitable initial
In region, so growth district is not in the case where coincidence completely with prime area.Wherein, corresponding to completely include and part
The case where coincidence, can also take different processing modes and obtain final area-of-interest.
In practical applications, the case where a kind of ratio may occur is that the summation of whole growth districts includes and is greater than just
The summation of whole growth districts can be determined as area-of-interest in this case by beginning region；Another ratio may go out
Existing situation is as shown in figure 3, growth district 34 includes the major part of prime area 32, but the two still has the part not being overlapped, i.e.,
In figure between two growth districts 34, need to consider this area of absence in the case, for example, can by area of absence with
The summation of growth district can also carry out further region growing for area of absence, according to growth as area-of-interest
As a result growth district determining and before this determines final area-of-interest.Thus make the profile of the final area-of-interest arrived
It is as identical as actual interesting target as possible.
The area-of-interest provided according to embodiments of the present invention determines method, and user substantially given in the picture can feel emerging
The position of interesting target draws prime area, then this programme will extract seed point in prime area automatically, and be based on seed
Point carries out region growing and determines growth district, determines area-of-interest eventually by growth district and prime area, thus
To the profile of area-of-interest be more nearly the actual profile of interesting target, even up to identical effect, with this
The operation of user is reduced, so that the mark of interesting target is more accurate, achievees the purpose that save manpower and time cost.
Area-of-interest provided by the invention determines that method can be applied to the mark of medical image, and Fig. 4 shows one
Eye fundus image, the image are by the way that captured by particular medical device, practical is colored image.In order to train one kind that can identify
The neural network model of optic disk needs to use the eye fundus image for being largely labelled with optic disk and corresponding label information to instruct as model
Practice data, this method can be used for being labeled eye fundus image.As shown in figure 5, the embodiment of the present invention provides a kind of region of interest
Domain determines method namely a kind of image automatic annotation method, and this method comprises the following steps:
S11, obtain user in the picture sketch the contours track, and determine prime area according to track is sketched the contours.As shown in fig. 6,
Manual smearing is carried out in user's eye fundus image shown in Fig. 4, manual application area 61 is prime area.
S12 obtains whole pixels in prime area, and it is multiple to filter out to carry out corrosion treatment to whole pixels
Discrete pixel is as the first seed point.The pixel that application area 61 includes can be specifically set as to a set, pass through corruption
It loses algorithm and this region is reduced to multiple unconnected pixels, these are by the first seed point as region growing.
S13 determines the profile in image, some profiles of lines self-assembling formation namely various realistic objectives in image
Edge, these profiles will be as the stop conditions during subsequent region growings.Gaussian Blur or frequency mistake can be passed through herein
The methods of filter obtains image border.Gaussian Blur can reduce picture noise, reduce level of detail, thus, it is possible to more easily mention
Take out a wide range of existing edge.Contour edge present in tag image, can be used for example in the figure for going noise
Gradient operator such as Sobel operator etc. can be with marker edge.
S14 extracts the characteristic value of multiple first seed points respectively, and the rgb value of pixel extractable first, then will herein
It is converted to HSV value i.e. characteristic value, the subsequent difference that color will be calculated under HSV mode；
S15 carries out region growing respectively using multiple first seed points as starting point with first condition and second condition,
At least one growth district is obtained, wherein whether the difference that the first condition is the characteristic value of neighbor pixel is less than default threshold
Value, the second condition are whether pixel is pixel on the profile.For above-mentioned first condition and second condition
With, be when in growth course growth touched profile when, terminate growth；Or adjacent pixel region during the growth process
When domain color difference is more than preset threshold, stop growing.
This is the first time region growing processing in the present embodiment, and the first seed point can be labeled as 1 in the process by this,
Remaining pixel is labeled as 0.Breadth first traversal is carried out to the first seed point, traversal is labeled as 8 points (8 around 1 point
Point in neighborhood), according to HSV Color space model, the difference of color can with space length, that is, Euclidean distance in model come
It indicates.If the color difference of certain point and central point in 8 neighborhoods is less than preset threshold, it is marked as 1.When in 8 neighborhoods
Edge is detected, current region growing is stopped.Region labeled as 1 point composition is growth district.After treatment
Two growth districts 71 as shown in Figure 7 are obtained, two growth districts 71 are not exclusively overlapped with prime area 61, and intermediate there are one
A area of absence 72.
S16 traverses the pixel in image, judges whether each pixel belongs to growth district and/or original area respectively
Domain；
S17 determines area-of-interest according to the pixel in growth district and/or prime area.
Above-mentioned treatment process can be regarded as doing or operation to application area 61 and growth district 71, it means that traversal
Each pixel of whole picture figure, if current pixel point is marked in artificial smear in 61 or region growing, in figure
This pixel of update mark；If this pixel does not all mark in two operations, do not change.The purpose done so
It is to guarantee in region growing operation, is not in the phenomenon that region crossed of handmarking is automatically erased, Fig. 8 finally can be obtained
Shown in area-of-interest 81.
Further, step S17 may include following steps:
S171, label belongs to prime area and is not belonging to the pixel of growth district, when doing above-mentioned or operation, if
User has smeared and point that region growing does not reach then is labeled as 2.This is done to extract the hole area of algorithm into
Traveling one-step optimization.
S172 selectes multiple second seed points from the pixel of label, and carries out area based on selected second seed point
Domain growth supplements region 72 to determine.After region growing, the region labeled as 2 is reduced to multiple single pictures by corroding
Vegetarian refreshments is that seed point carries out the processing of second of region growing in the present embodiment, the process and step S12-S15 phase with these points
Seemingly, and first seed point is determined using corrosion treatment algorithm, is then based on setting growth conditions and carries out region growing, it is therefore an objective to
The region not reached to first time region growing supplements.
The region of the pixel composition supplemented in region, growth district and prime area is determined as region of interest by S173
Domain 81.
The process of above-mentioned second of region growing has some differences compared to first time region growing, i.e. the second sub-region increases
Can be there are three growth conditions when long, specifically, step S172 may include steps of:
S1721 extracts the characteristic value of multiple second seed points respectively, namely first obtains the rgb value of pixel, then converts
For HSV value；
S1722, respectively using multiple second seed points as starting point, with first condition, second condition and third condition into
Row region growing obtains at least one supplement region, and wherein first condition and second condition can be found in above-mentioned steps S15.Third
Condition is the characteristic value of pixel and whether mean eigenvalue difference is less than preset threshold, and mean eigenvalue is all labeled
The average value of the characteristic value of pixel.
It, can be to above-mentioned label specifically after obtaining application area 61 about the utilization of above-mentioned third condition
The value of the hsv model of point is averaged on each channel, is referred to as average color.When as second of region growing, compare 8
The point of neighborhood and the color difference of central point are meeting first condition, but are unsatisfactory for second condition (when difference is greater than preset threshold
When), the color difference of also comparable neighborhood point and average color, if difference is less than preset difference value, marking the point is 1 continuation
Growth, until full terms stop growing when being not satisfied.
The embodiment of the present invention also provides a kind of electronic equipment, including at least one processor；And it is handled at least one
The memory of device communication connection；Wherein, memory is stored with the instruction that can be executed by a processor, instructs by least one
It manages device to execute, so that at least one processor executes above-mentioned area-of-interest and determines method.
It should be understood by those skilled in the art that, the embodiment of the present invention can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the present invention
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the present invention, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The present invention be referring to according to the method for the embodiment of the present invention, the process of equipment (system) and computer program product
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
Obviously, the above embodiments are merely examples for clarifying the description, and does not limit the embodiments.It is right
For those of ordinary skill in the art, can also make on the basis of the above description it is other it is various forms of variation or
It changes.There is no necessity and possibility to exhaust all the enbodiments.And it is extended from this it is obvious variation or
It changes still within the protection scope of the invention.
Claims (10)
1. a kind of area-of-interest determines method characterized by comprising
Obtain the prime area that user selectes in the picture；
Multiple first seed points are extracted in the prime area；
Region growing is carried out based on the multiple first seed point to determine at least one growth district；
Area-of-interest is determined according at least one described growth district and the prime area.
2. the method according to claim 1, wherein described obtain the prime area selected in the picture of user,
Include:
Obtain user in the picture sketch the contours track；
The prime area is determined according to the track of sketching the contours.
3. the method according to claim 1, wherein described extract multiple first seeds in the prime area
Point, comprising:
Obtain whole pixels in the prime area；
Corrosion treatment is carried out to filter out multiple discrete pixels as the first seed point to whole pixels.
4. the method according to claim 1, wherein described carry out region life based on the multiple first seed point
Length is to determine at least one growth district, comprising:
The characteristic value of the multiple first seed point is extracted respectively；
Determine the profile in described image；
Respectively using the multiple first seed point as starting point, region growing is carried out with first condition and second condition, is obtained
At least one growth district, wherein whether the difference that the first condition is the characteristic value of neighbor pixel is less than preset threshold,
The second condition is whether pixel is pixel on the profile.
5. the method according to claim 1, wherein including whole growth districts in the area-of-interest
And the prime area.
6. the method according to claim 1, wherein described at least one growth district according to and it is described just
Beginning region determines area-of-interest, comprising:
Traverse described image in pixel, judge respectively each pixel whether belong to the growth district and/or it is described just
Beginning region；
Area-of-interest is determined according to the pixel in the growth district and/or the prime area.
7. according to the method described in claim 6, it is characterized in that, described according to the growth district and/or the original area
Pixel in domain determines area-of-interest, comprising:
Label belongs to the prime area and is not belonging to the pixel of the growth district；
Multiple second seed points are selected from the pixel of label；
Region growing, which is carried out, based on selected second seed point supplements region to determine；
The region of pixel composition in the supplement region, the growth district and the prime area is determined as the sense
Interest region.
8. the method according to the description of claim 7 is characterized in that selecting multiple second seeds in the pixel from label
Point, comprising:
Obtain all labeled pixel；
Corrosion treatment is carried out to filter out multiple discrete pixels as second seed point to all labeled pixel.
9. the method according to the description of claim 7 is characterized in that described carry out region growing based on selected second seed point
Region is supplemented to determine, comprising:
The characteristic value of the multiple second seed point is extracted respectively；
Determine the profile in described image；
Respectively using the multiple second seed point as starting point, region is carried out with first condition, second condition and third condition
Growth obtains at least one supplement region, wherein whether the difference that the first condition is the characteristic value of neighbor pixel is less than
Preset threshold, the second condition are whether pixel is pixel on the profile, and the third condition is pixel
Whether characteristic value and mean eigenvalue difference are less than the preset threshold, and the mean eigenvalue is all labeled pixel
Characteristic value average value.
10. a kind of electronic equipment characterized by comprising at least one processor；And it is logical at least one described processor
Believe the memory of connection；Wherein, the memory is stored with the instruction that can be executed by one processor, and described instruction is by institute
The execution of at least one processor is stated, so that at least one described processor executes sense as described in any one of claims 1-9
Interest area determination method.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110503705A (en) * | 2019-08-29 | 2019-11-26 | 上海鹰瞳医疗科技有限公司 | Image labeling method and equipment |
| CN110992370A (en) * | 2019-11-19 | 2020-04-10 | 东软医疗系统股份有限公司 | Pancreas tissue segmentation method and device and terminal equipment |
| CN112258522A (en) * | 2020-10-19 | 2021-01-22 | 哈尔滨体育学院 | Martial arts competition area segmentation method based on secondary area growth |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030152262A1 (en) * | 2002-02-11 | 2003-08-14 | Fei Mao | Method and system for recognizing and selecting a region of interest in an image |
| CN101697229A (en) * | 2009-10-30 | 2010-04-21 | 宁波大学 | Method for extracting region of interest of medical image |
| CN102044069A (en) * | 2010-12-01 | 2011-05-04 | 华中科技大学 | Method for segmenting white blood cell image |
| CN102737376A (en) * | 2012-03-31 | 2012-10-17 | 常熟市支塘镇新盛技术咨询服务有限公司 | Improved region growing method applied to coronary artery angiography image segmentation |
| CN104021368A (en) * | 2013-02-28 | 2014-09-03 | 株式会社理光 | Method and system for estimating road height shape |
| CN104376556A (en) * | 2014-10-31 | 2015-02-25 | 四川大学 | Rock CT image target segmentation method |
| CN104792792A (en) * | 2015-04-27 | 2015-07-22 | 武汉武大卓越科技有限责任公司 | Stepwise-refinement pavement crack detection method |
| CN105139409A (en) * | 2015-09-11 | 2015-12-09 | 浙江工商大学 | Two-dimensional image segmentation method based on ant colony algorithm |
| CN105229701A (en) * | 2013-05-23 | 2016-01-06 | 爱克发医疗保健公司 | The method of definition area-of-interest |
| CN105261006A (en) * | 2015-09-11 | 2016-01-20 | 浙江工商大学 | Medical image segmentation algorithm based on Fourier transform |
| CN105513050A (en) * | 2015-11-25 | 2016-04-20 | 北京邮电大学世纪学院 | Target image extraction method and apparatus |
| CN106898004A (en) * | 2017-01-04 | 2017-06-27 | 努比亚技术有限公司 | A kind of preprocess method for realizing interactive image segmentation, device and terminal |
| CN107194925A (en) * | 2017-05-31 | 2017-09-22 | 上海联影医疗科技有限公司 | Image processing method and system |
-
2018
- 2018-07-26 CN CN201810836098.2A patent/CN109117837B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030152262A1 (en) * | 2002-02-11 | 2003-08-14 | Fei Mao | Method and system for recognizing and selecting a region of interest in an image |
| CN101697229A (en) * | 2009-10-30 | 2010-04-21 | 宁波大学 | Method for extracting region of interest of medical image |
| CN102044069A (en) * | 2010-12-01 | 2011-05-04 | 华中科技大学 | Method for segmenting white blood cell image |
| CN102737376A (en) * | 2012-03-31 | 2012-10-17 | 常熟市支塘镇新盛技术咨询服务有限公司 | Improved region growing method applied to coronary artery angiography image segmentation |
| CN104021368A (en) * | 2013-02-28 | 2014-09-03 | 株式会社理光 | Method and system for estimating road height shape |
| CN105229701A (en) * | 2013-05-23 | 2016-01-06 | 爱克发医疗保健公司 | The method of definition area-of-interest |
| CN104376556A (en) * | 2014-10-31 | 2015-02-25 | 四川大学 | Rock CT image target segmentation method |
| CN104792792A (en) * | 2015-04-27 | 2015-07-22 | 武汉武大卓越科技有限责任公司 | Stepwise-refinement pavement crack detection method |
| CN105139409A (en) * | 2015-09-11 | 2015-12-09 | 浙江工商大学 | Two-dimensional image segmentation method based on ant colony algorithm |
| CN105261006A (en) * | 2015-09-11 | 2016-01-20 | 浙江工商大学 | Medical image segmentation algorithm based on Fourier transform |
| CN105513050A (en) * | 2015-11-25 | 2016-04-20 | 北京邮电大学世纪学院 | Target image extraction method and apparatus |
| CN106898004A (en) * | 2017-01-04 | 2017-06-27 | 努比亚技术有限公司 | A kind of preprocess method for realizing interactive image segmentation, device and terminal |
| CN107194925A (en) * | 2017-05-31 | 2017-09-22 | 上海联影医疗科技有限公司 | Image processing method and system |
Non-Patent Citations (4)
| Title |
|---|
| QING LIN 等: "Extracting regions of interest based on visual attention model", 《2011 INTERNATIONAL CONFERENCE ON MULTIMEDIA TECHNOLOGY》 * |
| 司岳鹏 等: "基于腹部MRI的肝脏图像分割技术的研究与实现", 《中国优秀博硕士学位论文全文数据库 (硕士) 信息科技辑》 * |
| 王一丁 等: "《数字图像处理》", 31 August 2015 * |
| 苗语 等: "基于边缘检测终止条件的区域生长算法", 《长春理工大学学报（自然科学版）》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110503705A (en) * | 2019-08-29 | 2019-11-26 | 上海鹰瞳医疗科技有限公司 | Image labeling method and equipment |
| CN110992370A (en) * | 2019-11-19 | 2020-04-10 | 东软医疗系统股份有限公司 | Pancreas tissue segmentation method and device and terminal equipment |
| CN110992370B (en) * | 2019-11-19 | 2023-07-04 | 东软医疗系统股份有限公司 | Pancreas tissue segmentation method and device and terminal equipment |
| CN112258522A (en) * | 2020-10-19 | 2021-01-22 | 哈尔滨体育学院 | Martial arts competition area segmentation method based on secondary area growth |
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