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 PDF

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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
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image
micro
histotomy
point
synthetic method
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刘再群
李蓓
陈梅芳
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Anhui Normal University
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Anhui Normal University
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00Geometric image transformations in the plane of the image
    • G06T3/40Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4038Image mosaicing, e.g. composing plane images from plane sub-images
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration
    • G06T5/50Image enhancement or restoration using two or more images, e.g. averaging or subtraction
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10056Microscopic image
    • G06T2207/10061Microscopic image from scanning electron microscope

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  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
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  • 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

A kind of plain splice synthetic method of histotomy micro-image
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.
CN201810379858.1A 2018-04-25 2018-04-25 A kind of plain splice synthetic method of histotomy micro-image Pending CN108830788A (en)

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Cited By (7)

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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
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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

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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

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Application publication date: 20181116