CN108830788A - A kind of plain splice synthetic method of histotomy micro-image - Google Patents
A kind of plain splice synthetic method of histotomy micro-image Download PDFInfo
- Publication number
- CN108830788A CN108830788A CN201810379858.1A CN201810379858A CN108830788A CN 108830788 A CN108830788 A CN 108830788A CN 201810379858 A CN201810379858 A CN 201810379858A CN 108830788 A CN108830788 A CN 108830788A
- Authority
- CN
- China
- Prior art keywords
- image
- micro
- histotomy
- point
- synthetic method
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000010189 synthetic method Methods 0.000 title claims abstract description 16
- 230000015572 biosynthetic process Effects 0.000 claims abstract description 24
- 238000003786 synthesis reaction Methods 0.000 claims abstract description 24
- 239000012188 paraffin wax Substances 0.000 claims description 24
- 239000011159 matrix material Substances 0.000 claims description 20
- 230000000007 visual effect Effects 0.000 claims description 20
- 238000000605 extraction Methods 0.000 claims description 9
- 238000001914 filtration Methods 0.000 claims description 9
- 238000004043 dyeing Methods 0.000 claims description 6
- 238000012805 post-processing Methods 0.000 claims description 6
- 238000013459 approach Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000000399 optical microscopy Methods 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 4
- 238000009877 rendering Methods 0.000 claims description 4
- 238000012795 verification Methods 0.000 claims description 4
- 229920002334 Spandex Polymers 0.000 claims description 3
- 230000003321 amplification Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 3
- 239000003086 colorant Substances 0.000 claims description 3
- 230000018044 dehydration Effects 0.000 claims description 3
- 238000006297 dehydration reaction Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
- 239000004759 spandex Substances 0.000 claims description 3
- 230000001629 suppression Effects 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 230000007812 deficiency Effects 0.000 abstract description 3
- 230000008901 benefit Effects 0.000 abstract description 2
- 238000000034 method Methods 0.000 description 10
- 238000013527 convolutional neural network Methods 0.000 description 5
- 238000001000 micrograph Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 238000005034 decoration Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000005304 joining Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 239000001993 wax Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
- G06T3/4038—Image mosaicing, e.g. composing plane images from plane sub-images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10056—Microscopic image
- G06T2207/10061—Microscopic image from scanning electron microscope
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a kind of plain splice synthetic methods of histotomy micro-image, include the following steps：(1) individual MIcrosope image to be spliced is obtained；(2) feature of micro-image is extracted；(3) feature of micro-image is matched；(4) splicing of micro-image is synthesized.The advantage of the invention is that：Deficiency existing for existing slice micro-image synthetic method is solved, efficient, the accurate plain splice synthesis for completing animal tissue sections micro-image is reached.
Description
Technical field
The present invention relates to field of image processing, in particular to a kind of plain splice synthesis side of histotomy micro-image
Method.
Background technique
During the observation of animal tissue and three-dimensional reconstruction, need to obtain the complete micro-image of histotomy, still
In shooting process in order to guarantee image clearly, reaches certain pixel degree, can not directly be obtained completely by once photo taking
Tissue slice images, the partial region that can only be sliced to field of microscope undertissue are shot.Therefore, a stone in order to obtain
Wax is sliced clearly complete micro-image, needs to shoot multiple high-resolution images, subsequent micrograph complete in order to obtain
Picture needs to carry out all photos plain splice synthesis, and then generates complete high-resolution histotomy micro-image.Mesh
Before, the splicing synthesis for slice micro-image is most of by manually utilizing the software realizations such as Photoshop, but by soft
The artificial synthesized micro-image of part takes time and effort, while the requirement to image synthesis personnel is very high, therefore existing slice micrograph
As synthetic method has some limitations.
Summary of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of plain splices of histotomy micro-image
Synthetic method reaches efficient, accurate completion animal tissue and cuts to solve deficiency existing for existing slice micro-image synthetic method
The plain splice of piece micro-image synthesizes.
To achieve the goals above, the technical solution adopted by the present invention is：A kind of plane spelling of histotomy micro-image
It is bonded into method, is included the following steps：
(1) individual MIcrosope image to be spliced is obtained；
(2) feature of micro-image is extracted；
(3) feature of micro-image is matched；
(4) splicing of micro-image is synthesized.
(5) post-processing is carried out to the image after synthesis.
In step (1), by tissue fixation, dehydration, transparent, paraffin embedding, slice, dyeing, mounting and etc. production group
Slice is knitted, then the histotomy made is observed, shoots individual micro-image obtained under the different visuals field.
The acquisition of micro-image includes the following steps：
(a) animal tissue's paraffin section is made：It selects and chooses biological tissue's block that size thickness is no more than 5 millimeters, then
Animal tissue's block is embedded using paraffin；Embedded paraffin mass is sliced using Lycra slicer, keeps slice every
With a thickness of 7 microns；Then slice is unfolded in exhibition film trap, carries out slice dyeing with coloring agent, then be dehydrated, be transparent, envelope
Piece process finally slice is made convenient for saving the paraffin section with long distance transportation；
(b) micro-image of animal tissue's paraffin section under the different visuals field is shot：Pass through optical microscopy complete set of equipments
It carries out photomicrograph and obtains planar picture；Micro-image is observed by computer；When shooting the micro-image under the different visuals field, press
According to sequential shoot micro-image from left to right, from top to bottom, and guarantee two adjacent images there are intersection, until not
It is enough to cover the region of animal tissue's sample in paraffin section with the summation of individual micro-image under the visual field；Complete the different visuals field
After the shooting of individual micro-image, the micro-image of shooting is screened, for underproof image need timely retake or
The paraffin section that person more renews re-starts the acquisition of individual micro-image.
The extraction of micro-image feature includes the following steps：
(a) micro-image in the different visuals field under n identical amplification factors is read；
(b) the SURF feature vector of the micro-image under the n different visuals field of input is extracted；
(c) Hessian matrix is constructed：Hessian matrix can letter after filtering
It turns to：H-matrix discriminate is calculated, and judges to be greater than 0 or less than 0, to differentiate
This yes or no extreme point；
(d) gaussian pyramid scale space is constructed using Hessian matrix, and to the box filters of different scale
Original micro-image is made to remain unchanged and only change the size of filter with original picture convolution；
(e) candidate point is determined first with Hessian matrix, characteristic point is then primarily determined by non-maxima suppression, then
The further explication characteristic point of key point by filtering out the weak key point of energy comparison and location of mistake；
(f) the haar small echo response that the direction x, y in each fan-shaped range is finally calculated in the circle shaped neighborhood region of characteristic point, is looked for
To the maximum fan-shaped direction of mould and as characteristic point principal direction, that is, complete the extraction of the SURF feature vector of 64 dimensions.
Step (3) micro-image character matching step is as follows：
(a) for two adjacent two image A, B are chosen in multiple image, the key of A and B is generated using step (2)
Point description uses the standardization Euclidean distance of key point SURF feature vector to describe sub- similitude inspection as two images key point
The foundation of survey；
It (b) is that each key point finds immediate 2 match points in B in A by force search；
(c) for two match points in (b), if the standard European distance of closest approach is divided by the standard Euclidean of secondary near point
Distance is less than some proportion threshold value, then receives this pair of of match point.In order to guarantee the stability of characteristic matching, we are special herein
Not defining proportion threshold value is 0.5.
Step (4) Microscopic Image Mosaicing synthesis step is as follows：
(a) consistency matching is found out using ICP algorithm, so that the key point of two kinds of images corresponds；
(b) eigenmatrix of all micro-images is obtained using VGG16 training, it is effective carries out images match on this basis
The verification of property；VGG16 is a kind of CNN of 16 layer networks, wherein 13 convolutional layers, 3 full linking layers；
(c) after verifying matching effectively, the connected component in matching image is found out；
(d) it is directed to each connected component, finds out suitable rotation angle and suitable matching tie point；
(e) rendering optimization is carried out using filtering, is smoothly connected the splicing synthesis for completing micro-image；
(f) it obtains being sliced complete micro-image.
Step (5) composograph post-processing steps are as follows：
(a) color range processing is carried out to the complete micro-image of slice after splicing synthesis first, removes bias light and white balance
It is influenced Deng caused by, enhances picture contrast, keep its level clearly more demarcated；
(b) then edge softening is used to make background and edge more natural；
(c) finally it is cut into unified resolution and image that micro-image is placed centrally.
The advantage of the invention is that：1, the present invention can carry out plain splice synthesis to animal tissue sections micro-image,
The whole slices micro-image of unified resolution can be exported, observes picture structure convenient for experimenter, while being able to satisfy subsequent
The requirement of three-dimensional reconstruction.
2, the present invention is in the plain splice synthesis of animal tissue sections micro-image, on the high basis of image resolution ratio
On, the disadvantages of more image mosaic quantity, brightness irregularities, repeating part can be overcome few, reach quickly, be correctly completed micro-image
Plain splice synthesis purpose.
3, the present invention breaches taking time and effort when splicing synthesis flat image using artificial software, while reducing image
The technical requirements of synthesis personnel realize efficient, the accurate splicing synthesis of animal tissue sections micro-image.
Detailed description of the invention
Below to each width attached drawing of description of the invention expression content and figure in label be briefly described：
Fig. 1 is joining method flow chart of the present invention；
Fig. 2 is micro-image feature extraction flow chart of the present invention.
Specific embodiment
A specific embodiment of the invention is made further detailed below against attached drawing by the description to optimum embodiment
Thin explanation.
A kind of plain splice synthetic method of animal tissue sections micro-image, includes the following steps：
(1) individual micro-image obtains.Pass through tissue fixation, dehydration, transparent, paraffin embedding, slice, dyeing, mounting etc.
Step makes histotomy, then the histotomy made is observed, shoot obtain under the different visuals field individual is micro-
Image.It selects Olympus BX51 type optical microscopy complete set of equipments to carry out photomicrograph and obtains planar picture.By on computer
6.0 software of Image-Pro Plus observe micro-image, filter out it is several it is suitable, clearly individual slice micro-image is used
It is synthesized in subsequent whole slices Microscopic Image Mosaicing.
(2) micro-image feature extraction.Simultaneously for the high quality micro-image filtered out, first building Hessian matrix
Hessian matrix discriminate is calculated, gaussian pyramid scale space is constructed followed by Hessian matrix, is then utilized
Hessian matrix determines candidate point while carrying out accurate feature points, and last selected characteristic point principal direction construction 64 ties up SURF
(Speeded Up Robust Features) feature point description operator.
(3) micro-image characteristic matching.It is illustrated by taking the characteristic matching of A and B two images as an example, in above-mentioned micrograph
Key point description son (the SURF feature vector and K2*64 that as K1*64 is tieed up as A and B can be generated after the completion of feature extraction respectively
The SURF feature vector of dimension), use the standardization Euclidean distance of key point SURF feature vector similar as two images key point
Property detection foundation, and 2 nearest match points are found for each key point by force search.
(4) Microscopic Image Mosaicing synthesizes.One is found out using ICP (Iterative Closest Point, iteration closest approach)
The matching of cause property, so that the key point of two images corresponds, and use CNN (Convolutional Neural Network,
Convolutional neural networks) model carries out the verification of images match validity, find out suitable rotation angle and suitable matching connection
Point carries out rendering optimization using filtering, is smoothly connected the splicing synthesis for completing micro-image, obtains being sliced complete micrograph
Picture.
(5) composograph post-processing.Color range processing is carried out to the complete micro-image after splicing synthesis, removes bias light
It is influenced with caused by white balance etc., edge softening is being used to make background and edge more naturally, being finally cut into unified resolution
The image that rate and micro-image are placed centrally.
Individual micro-image obtaining step of above-mentioned steps (1) is as follows：
(a) animal tissue's paraffin section is made first.It selects and chooses biological tissue's block that size thickness is no more than 5 millimeters,
Then animal tissue's block is embedded using paraffin.Embedded paraffin mass is sliced using Lycra slicer, keeps slice
Every with a thickness of 7 microns.Then slice is unfolded in exhibition film trap, is carried out with coloring agent (HE decoration method or Nissl decoration method)
Finally slice is made convenient for saving and the paraffin of long distance transportation for slice dyeing, then be dehydrated, be transparent, the processes such as mounting
Slice；
(b) micro-image of animal tissue's paraffin section under the different visuals field is then shot.Select Olympus BX51 type
Optical microscopy complete set of equipments carries out photomicrograph and obtains planar picture.By 6.0 software of Image-Pro Plus on computer
Observe micro-image.When shooting the micro-image under the different visuals field, need to guarantee any one image and other at least one
There are intersections for image.Therefore according to sequential shoot micro-image from left to right, from top to bottom, and guarantee adjacent two
There are intersections for image, until the summation of individual micro-image under the different visuals field is enough to cover animal tissue in paraffin section
The region of sample.After the shooting for completing different individual micro-image of the visual field, manually the micro-image of shooting is screened, for
Underproof image needs timely retake or the paraffin section more renewed to re-start the acquisition of individual micro-image.
Above-mentioned steps (2) micro-image characteristic extraction step is as follows：
(a) micro-image in the different visuals field under n identical amplification factors is read；
(b) the SURF feature vector of the micro-image under the n different visuals field of input is extracted, as shown in Figure 2；
(c) Hessian matrix is constructed：Hessian matrix can letter after filtering
It turns to：H-matrix discriminate is calculated, and judges to be greater than 0 or less than 0, to differentiate
This yes or no extreme point；Wherein, F (x, y) indicates the certain point on image.Certain point X=(x, y) in H (x, σ) image, in X
Hessian matrix on the σ scale of point, σ indicate most neighborhood.Lxx (X, σ) indicates Gauss second order derviation X at and the volume of image I
Product.Lxy (X, σ), Lyy (X, σ) have similar meaning
(d) gaussian pyramid scale space is constructed using Hessian matrix, and to the box filters of different scale
Original micro-image is made to remain unchanged and only change the size of filter with original picture convolution；
(e) candidate point is determined first with Hessian matrix, characteristic point is then primarily determined by non-maxima suppression, then
The further explication characteristic point of key point by filtering out the weak key point of energy comparison and location of mistake；
(f) the haar small echo response that the direction x, y in each fan-shaped range is finally calculated in the circle shaped neighborhood region of characteristic point, is looked for
To the maximum fan-shaped direction of mould and as characteristic point principal direction, that is, complete the extraction of the SURF feature vector of 64 dimensions.
Above-mentioned steps (3) micro-image character matching step is as follows：
(a) for two adjacent two image A, B are chosen in multiple image, the key of A and B is generated using step (2)
Point description son (as K1*64 dimension SURF feature vector and K2*64 dimension SURF feature vector), with key point SURF feature to
The standardization Euclidean distance of amount describes the foundation of sub- similitude detection as two images key point；
It (b) is that each key point finds immediate 2 match points in B in A by force search；
(c) for two match points in (b), if the standard European distance of closest approach is divided by the standard Euclidean of secondary near point
Distance is less than some proportion threshold value, then receives this pair of of match point.In order to guarantee the stability of characteristic matching, we are special herein
Not defining proportion threshold value is 0.5.
Above-mentioned steps (4) Microscopic Image Mosaicing synthesis step is as follows：
(a) consistency matching is found out using ICP (Iterative Closest Point, iteration closest approach), so that two kinds
The key point of image corresponds；
(b) institute is obtained using VGG16 (a kind of CNN of 16 layer network, wherein 13 convolutional layers, 3 full linking layers) training
There is the eigenmatrix of micro-image, carries out the verification of images match validity on this basis；
(c) connected component in matching image is found out；
(d) it is directed to each connected component, finds out suitable rotation angle and suitable matching tie point；
(e) rendering optimization is carried out using filtering, is smoothly connected the splicing synthesis for completing micro-image；
(f) it obtains being sliced complete micro-image.
Above-mentioned steps (5) composograph post-processing steps are as follows：
(a) color range processing is carried out to the complete micro-image of slice after splicing synthesis first, removes bias light and white balance
It is influenced Deng caused by, enhances picture contrast, keep its level clearly more demarcated；
(b) then edge softening is used to make background and edge more natural；
(c) finally it is cut into unified resolution and image that micro-image is placed centrally, the slice finally exported is completely shown
Micro- image resolution ratio is fixed as 4000*3000.
Obviously present invention specific implementation is not subject to the restrictions described above, as long as using method concept and skill of the invention
The improvement for the various unsubstantialities that art scheme carries out, it is within the scope of the present invention.
Claims (8)
1. a kind of plain splice synthetic method of histotomy micro-image, which is characterized in that include the following steps：
(1) individual MIcrosope image to be spliced is obtained；
(2) feature of micro-image is extracted；
(3) feature of micro-image is matched；
(4) splicing of micro-image is synthesized.
2. a kind of plain splice synthetic method of histotomy micro-image as described in claim 1, which is characterized in that also wrap
Include step
(5) post-processing is carried out to the image after synthesis.
3. a kind of plain splice synthetic method of histotomy micro-image as described in claim 1, which is characterized in that step
(1) in, by tissue fixation, dehydration, transparent, paraffin embedding, slice, dyeing, mounting and etc. make histotomy, it is subsequent right
The histotomy made is observed, shoots individual micro-image obtained under the different visuals field.
4. a kind of plain splice synthetic method of histotomy micro-image as claimed in claim 3, which is characterized in that micro-
The acquisition of image includes the following steps：
(a) animal tissue's paraffin section is made：It selects and chooses biological tissue's block that size thickness is no more than 5 millimeters, then use
Paraffin embeds animal tissue's block；Embedded paraffin mass is sliced using Lycra slicer, keeps every thickness of slice
It is 7 microns；Then slice is unfolded in exhibition film trap, carries out slice dyeing with coloring agent, then be dehydrated, be transparent, mounting mistake
Journey finally slice is made convenient for saving the paraffin section with long distance transportation；
(b) micro-image of animal tissue's paraffin section under the different visuals field is shot：It is carried out by optical microscopy complete set of equipments
Photomicrograph obtains planar picture；Micro-image is observed by computer；When shooting the micro-image under the different visuals field, according to from
Sequential shoot micro-image left-to-right, from top to bottom, and guarantee two adjacent images there are intersection, until different views
The summation of individual micro-image under wild is enough to cover the region of animal tissue's sample in paraffin section；Complete the different visuals field individual
After the shooting of micro-image, the micro-image of shooting is screened, timely retake or more is needed for underproof image
The paraffin section renewed re-starts the acquisition of individual micro-image.
5. a kind of plain splice synthetic method of histotomy micro-image as described in claim 1, which is characterized in that micro-
The extraction of characteristics of image includes the following steps：
(a) micro-image in the different visuals field under n identical amplification factors is read；
(b) the SURF feature vector of the micro-image under the n different visuals field of input is extracted；
(c) Hessian matrix is constructed：Hessian matrix can simplify after filtering
For：H-matrix discriminate is calculated, and judges to be greater than 0 or less than 0, to differentiate this
Point yes or no extreme point；
(d) gaussian pyramid scale space is constructed using Hessian matrix, and to the box filters of different scale and original
Picture convolution makes original micro-image remain unchanged and only change the size of filter；
(e) candidate point is determined first with Hessian matrix, characteristic point is then primarily determined by non-maxima suppression, using
Filter out the further explication characteristic point of key point of the weak key point of energy comparison and location of mistake；
(f) the haar small echo response that the direction x, y in each fan-shaped range is finally calculated in the circle shaped neighborhood region of characteristic point, finds mould
Maximum sector direction and as characteristic point principal direction, that is, complete the extraction of the SURF feature vector of 64 dimensions.
6. a kind of plain splice synthetic method of histotomy micro-image as claimed in claim 5, which is characterized in that step
(3) micro-image character matching step is as follows：
(a) it for two adjacent two image A, B are chosen in multiple image, is retouched using the key point that step (2) generate A and B
Son is stated, the standardization Euclidean distance of key point SURF feature vector is used to describe sub- similitude detection as two images key point
Foundation；
It (b) is that each key point finds immediate 2 match points in B in A by force search；
(c) for two match points in (b), if the standard European distance of closest approach is divided by the standard Euclidean distance of secondary near point
Less than some proportion threshold value, then receive this pair of of match point.In order to guarantee the stability of characteristic matching, we are particularly herein
Defining proportion threshold value is 0.5.
7. a kind of plain splice synthetic method of histotomy micro-image as claimed in claim 6, which is characterized in that
Step (4) Microscopic Image Mosaicing synthesis step is as follows：
(a) consistency matching is found out using ICP algorithm, so that the key point of two kinds of images corresponds；
(b) eigenmatrix of all micro-images is obtained using VGG16 training, carries out images match validity on this basis
Verification；
(c) after verifying matching effectively, the connected component in matching image is found out；
(d) it is directed to each connected component, finds out suitable rotation angle and suitable matching tie point；
(e) rendering optimization is carried out using filtering, is smoothly connected the splicing synthesis for completing micro-image；
(f) it obtains being sliced complete micro-image.
8. a kind of plain splice synthetic method of histotomy micro-image as claimed in claim 2, which is characterized in that
Step (5) composograph post-processing steps are as follows：
(a) color range processing is carried out to the complete micro-image of slice after splicing synthesis first, removal bias light and white balance etc. are made
At influence, enhance picture contrast, keep its level clearly more demarcated；
(b) then edge softening is used to make background and edge more natural；
(c) finally it is cut into unified resolution and image that micro-image is placed centrally.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810379858.1A CN108830788A (en) | 2018-04-25 | 2018-04-25 | A kind of plain splice synthetic method of histotomy micro-image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810379858.1A CN108830788A (en) | 2018-04-25 | 2018-04-25 | A kind of plain splice synthetic method of histotomy micro-image |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108830788A true CN108830788A (en) | 2018-11-16 |
Family
ID=64155585
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810379858.1A Pending CN108830788A (en) | 2018-04-25 | 2018-04-25 | A kind of plain splice synthetic method of histotomy micro-image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108830788A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109829871A (en) * | 2019-01-11 | 2019-05-31 | 安徽师范大学 | A kind of animal brain's slice micro-image Enhancement Method |
| CN110246116A (en) * | 2019-04-24 | 2019-09-17 | 创新工场(北京)企业管理股份有限公司 | Digital pathological section dyes the computer automatic generation method dyed to IHC by HE |
| CN110490805A (en) * | 2019-08-16 | 2019-11-22 | 上海昌岛医疗科技有限公司 | A kind of joining method of microscope pathological section scanned picture |
| CN110807732A (en) * | 2019-10-11 | 2020-02-18 | 武汉兰丁医学高科技有限公司 | Panoramic stitching system and method for microscopic images |
| CN112819735A (en) * | 2020-12-31 | 2021-05-18 | 四川大学 | Real-time large-scale image synthesis algorithm of microscope system |
| CN114087998A (en) * | 2021-11-25 | 2022-02-25 | 重庆海浦洛自动化科技有限公司 | Online film thickness detection system for cavity wax injection system |
| CN116229297A (en) * | 2023-03-09 | 2023-06-06 | 广东精益空间信息技术股份有限公司 | Mapping data processing method, mapping data processing system, mapping data processing medium and mapping data processing computer |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107451985A (en) * | 2017-08-01 | 2017-12-08 | 中国农业大学 | A kind of joining method of the micro- sequence image of mouse tongue section |
-
2018
- 2018-04-25 CN CN201810379858.1A patent/CN108830788A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107451985A (en) * | 2017-08-01 | 2017-12-08 | 中国农业大学 | A kind of joining method of the micro- sequence image of mouse tongue section |
Non-Patent Citations (1)
| Title |
|---|
| 杨瑞阳: "基于SURF算法的医学显微图像拼接研究", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109829871A (en) * | 2019-01-11 | 2019-05-31 | 安徽师范大学 | A kind of animal brain's slice micro-image Enhancement Method |
| CN109829871B (en) * | 2019-01-11 | 2022-12-09 | 安徽师范大学 | Microscopic image enhancement method for animal brain tissue section |
| CN110246116A (en) * | 2019-04-24 | 2019-09-17 | 创新工场(北京)企业管理股份有限公司 | Digital pathological section dyes the computer automatic generation method dyed to IHC by HE |
| CN110246116B (en) * | 2019-04-24 | 2020-12-25 | 创新工场(北京)企业管理股份有限公司 | Computer automatic generation method for digital pathological section from HE staining to IHC staining |
| CN110490805A (en) * | 2019-08-16 | 2019-11-22 | 上海昌岛医疗科技有限公司 | A kind of joining method of microscope pathological section scanned picture |
| CN110807732A (en) * | 2019-10-11 | 2020-02-18 | 武汉兰丁医学高科技有限公司 | Panoramic stitching system and method for microscopic images |
| CN110807732B (en) * | 2019-10-11 | 2023-08-29 | 武汉兰丁智能医学股份有限公司 | Panoramic stitching system and method for microscopic images |
| CN112819735A (en) * | 2020-12-31 | 2021-05-18 | 四川大学 | Real-time large-scale image synthesis algorithm of microscope system |
| CN114087998A (en) * | 2021-11-25 | 2022-02-25 | 重庆海浦洛自动化科技有限公司 | Online film thickness detection system for cavity wax injection system |
| CN114087998B (en) * | 2021-11-25 | 2023-11-03 | 重庆海浦洛自动化科技有限公司 | Online film thickness detection system for cavity wax injection system |
| CN116229297A (en) * | 2023-03-09 | 2023-06-06 | 广东精益空间信息技术股份有限公司 | Mapping data processing method, mapping data processing system, mapping data processing medium and mapping data processing computer |
| CN116229297B (en) * | 2023-03-09 | 2023-10-13 | 广东精益空间信息技术股份有限公司 | Mapping data processing method, mapping data processing system, mapping data processing medium and mapping data processing computer |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108830788A (en) | A kind of plain splice synthetic method of histotomy micro-image | |
| CN105894045B (en) | A kind of model recognizing method of the depth network model based on spatial pyramid pond | |
| CN107437092A (en) | The sorting algorithm of retina OCT image based on Three dimensional convolution neutral net | |
| CN109801292A (en) | A kind of bituminous highway crack image partition method based on generation confrontation network | |
| CN108765344A (en) | A method of the single image rain line removal based on depth convolutional neural networks | |
| CN107358636A (en) | A kind of rarefaction defect image generating method based on textures synthesis | |
| CN111145176A (en) | Method and system for automatically identifying lymph node staining pathological image of gastric cancer based on deep neural network | |
| CN106611413A (en) | Image segmentation method and system | |
| CN108564077A (en) | It is a kind of based on deep learning to detection and recognition methods digital in video or picture | |
| CN106709517A (en) | Mangrove recognition method and system | |
| CN101394573A (en) | Panoramagram generation method and system based on characteristic matching | |
| CN109086777A (en) | A kind of notable figure fining method based on global pixel characteristic | |
| CN110059539A (en) | A kind of natural scene text position detection method based on image segmentation | |
| Marochov et al. | Image classification of marine-terminating outlet glaciers in Greenland using deep learning methods | |
| CN113160062A (en) | Infrared image target detection method, device, equipment and storage medium | |
| CN112990085B (en) | Method and device for detecting change of culture pond and computer readable storage medium | |
| CN113269724A (en) | Fine-grained cancer subtype classification method | |
| CN103544505A (en) | Ship recognition system and ship recognition method for aerial image pickup of unmanned plane | |
| CN106855996A (en) | A kind of gray scale image color method and its device based on convolutional neural networks | |
| CN109886170A (en) | A kind of identification of oncomelania intelligent measurement and statistical system | |
| CN113111740A (en) | Characteristic weaving method for remote sensing image target detection | |
| CN112085017A (en) | Tea tender shoot image segmentation method based on significance detection and Grabcut algorithm | |
| CN115588024A (en) | Artificial intelligence-based complex industrial image edge extraction method and device | |
| CN109886221A (en) | Sand dredger recognition methods based on saliency detection | |
| CN112330613A (en) | Method and system for evaluating quality of cytopathology digital image |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181116 |