CN105809703A - Adhesion hemocyte image segmentation method based on improved fractional differential and graph theory - Google Patents
Adhesion hemocyte image segmentation method based on improved fractional differential and graph theory Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000003709 image segmentation Methods 0.000 title abstract description 4
- 210000003677 hemocyte Anatomy 0.000 title abstract 5
- 229940000351 hemocyte Drugs 0.000 title abstract 5
- 210000004027 cell Anatomy 0.000 claims abstract description 18
- 230000011218 segmentation Effects 0.000 claims abstract description 16
- 210000000601 blood cell Anatomy 0.000 claims description 18
- 239000013598 vector Substances 0.000 claims description 17
- 238000004458 analytical method Methods 0.000 claims description 3
- 230000001174 ascending effect Effects 0.000 claims description 3
- 238000004043 dyeing Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 230000001464 adherent effect Effects 0.000 abstract 1
- 238000011109 contamination Methods 0.000 abstract 1
- 238000013507 mapping Methods 0.000 abstract 1
- 230000000877 morphologic effect Effects 0.000 abstract 1
- 238000010186 staining Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 4
- 238000005192 partition Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000021615 conjugation Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000013316 zoning Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
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- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20152—Watershed segmentation
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- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20172—Image enhancement details
- G06T2207/20192—Edge enhancement; Edge preservation
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Abstract
The invention relates to an adhesion hemocyte image segmentation method based on the improved fractional differential and graph theory.The method comprises the steps of pretreating a hemocyte image by combining morphological denoising with the improved quasi-circular mask operator fractional differential algorithm aiming at the phenomenon that the hemocyte image is fuzzy and low in contrast ratio, wherein by the adoption of the improved fractional differential algorithm, cell edge details are reserved while staining contamination and grain noise of the hemocyte image are filtered out; then conducting preliminary segmentation on the pretreated image with the watershed algorithm, and mapping an over-segmentation area into a node; finally conducting resegmentation on the cell image obtained in the second step with the improved graph theory minimum spanning tree (MST) algorithm.By the adoption of the method, segmentation precision of adherent cells in the cell image can be improved.
Description
Technical field
The present invention relates to Medical Image Segmentation Techniques field, particularly a kind of adhesion blood cell image dividing method based on the fractional order differential improved and graph theory.
Background technology
Cell is the basic composition unit of all life body.Whether the biomedical important directions quickly grown in recent years is through the identification of cell, counting and texture image and is distorted and diagnoses the illness.And the data of cell image can be analyzed exactly, it is critically depend on and can split cell image exactly.
Dividing method based on graph theory is the focus studied both at home and abroad in the last few years, and a kind of layering dividing method from top to down based on drawing method that Sharon in 2006 etc. above propose at " Nature ", segmentation result is accurate and efficiency is high.Vanhamel etc. propose the non-linear multiple dimensioned Segmentation of Color Image based on graph theory；Hu Xuegang etc. propose based on graph theory and normalized segmentation criterion, and digital image processing model (LIP model) is applied in image procossing；Ye Wei etc. are theoretical in conjunction with Mumford Shah, it is proposed that a kind of optimization method, by considering the geometric properties in interregional combination degree and each region in image, based on the weights of conjugation the image partition method adding it to minimum spanning tree between zoning；Zhang et al. proposes watershed and the image partition method of graph theory combination.AnnaFabijanska et al. proposes the innovatory algorithm of a kind of minimum spanning tree based on graph theory, and this algorithm passes through the number of vertex in minimizing figure thus improving the speed of image segmentation.Multi-spectral Satellite Images is split by the Mean-Shift algorithm that BiplabBanerjee et al. proposes on the basis of the algorithm of a kind of minimum spanning tree based on graph theory in conjunction with clustering algorithm and improvement.Additionally, Felzenszwalb and Huttenlocher (be called for short FH) propose " little and and it " merging criterion, carrying out the improvement of minimum spanning tree partitioning algorithm, the method efficiency is higher, it is possible to partly carry out different segmentation according to different picture characteristics.But it also has the shortcoming of self, the k value preset in algorithm is difficult to ground manual control, if its value is more big, will produce merging phenomenon；If too small, it is impossible to effectively to suppress the generation of zonule, more redundant area will be produced.Just improve based on FH algorithm herein.
Summary of the invention
In view of this, the purpose of the present invention is to propose to a kind of adhesion blood cell image dividing method based on the fractional order differential improved and graph theory, improve the precision of AC segmentation in cell image.
The present invention adopts below scheme to realize: a kind of adhesion blood cell image dividing method based on the fractional order differential improved and graph theory, comprises the following steps:
Step S1: for the phenomenon that blood cell image is fuzzy and contrast is not high, being combined by the fractional order differential algorithm of the similar round mask operator of morphology denoising and improvement and blood cell image carries out pretreatment, the fractional order differential algorithm of described improvement pollutes in the dyeing filtering blood cell image and remains cell edges details preferably while grain noise；
Step S2: with watershed algorithm, pretreated image is carried out just segmentation, overdivided region is mapped as node；
Step S3: cell image step S2 obtained with graph theory minimum spanning tree (MST) algorithm improved is split again.
Further, in described step S1, it is proposed to a kind of Tiansi operator based on vector merge similar round improve mask template, specifically include following steps:
Step S11: the square Tiansi fractional order differential mask template of one 5x5 of structure, Tiansi mask operator is square structure, a on the position of 4 drift angles of outermost layer2Treating as is the starting point 4 vectors at template initial pointAnd take its half
Step S12: the upper left corner of the mask operator template obtained after described step S11 is processed is with the lower left cornerMerge into level new vector to the left
Step S13: the combined vector obtained after described step S12 is processedBe added to the vector in original third layer centre position, obtains the mask template of similar round.
Further, described step S2 specifically includes following steps:
Step S21: assume that image obtains 6 overdivided regions after watershed algorithm is split: 1,2,3,4,5,6}, the limit collection of its mapped node is:
E={e1,e2,e3,e4,e5,e6,e7,e8,e9}={ (1,2), (1,5), (2,3), (2,5), (3,4), (3,5), (4,5), (4,6), (5,6) }；
The weighted value of described limit collection respectively 5,9,6,8,4,1,7,4,2；
Step S22: the weighted value on overdivided region and limit is mapped one by one and obtains initial weighted undirected graph.
Further, described step S3 specifically includes following steps:
Step S31: map: the overdivided region of input picture is mapped as node, calculates weighted undirected graph G={V, E, the ψ of 8 neighborhood systems by the weighted value definition improved }, wherein | V |=n, | E |=m, connect node viAnd vjThe weighted value on limit is ψ (vi,vj), setting area minimum area is p；
Step S32: state initialization: set Sq-1It is the cut set after q-1 sub-region merges, makes q=1 make initial segmentation be Sq-1=S0, S0=(v1,v2,…,vn), namely in V, each element is a region, by prime area internal diversityRegion areaWherein i=1,2,3 ... n；
Step S33: sequence: weighted undirected graph G={V, E, ψ } in all of weighted value according to ascending order arrangement, obtain queue π=(O1,O2,…,Om)；
Step S34: if the queue in step S33 is not empty, makes the limit dequeue that weighted value is minimum, otherwise forward step S37 to；
Step S35: from Sq-1Structure Sq: setWithIt is S respectivelyq-1The two end node v on the limit of middle dequeueiAnd vjThe region at place, if:
And
Then by regionWithMerge and obtain Sq, and calculate the weighted value of new region and its neighborhood system；Otherwise nonjoinder, makes Sq=Sq-1；
Step S36: the weighted value according to new region, updates the internal diversity of new region, region difference and weighted value queue π, makes q=q+1, returns step S34；
Step S37: traversal SqEach region, (vi,vj) ∈ E, vi∈ C1, vj∈ C2, if | C1 | < p or | C2 | < p, then remerges C1 and C2；
Step S38: be same color by the pixel assignment belonging to the same area, it is simple to the observation analysis of human eye；
Step S39: the segmentation result of the minimal spanning tree algorithm that output improves herein.
Compared with prior art, the invention has the beneficial effects as follows be effectively improved in cell segmentation over-segmentation, less divided precision, there is application prospect widely.
Accompanying drawing explanation
Fig. 1 is the inventive method schematic flow sheet.
Fig. 2 is embodiment of the present invention Tiansi mask operator schematic diagram.
Fig. 3 is the mask operator schematic diagram of embodiment of the present invention similar round.
Fig. 4 is embodiment of the present invention vector Unite principle schematic diagram.
Fig. 5 is embodiment of the present invention watershed algorithm overdivided region schematic diagram.
Fig. 6 is embodiment of the present invention weighted undirected graph.
Detailed description of the invention
Below in conjunction with drawings and Examples, the present invention will be further described.
As it is shown in figure 1, present embodiments provide a kind of adhesion blood cell image dividing method based on the fractional order differential improved and graph theory, comprise the following steps:
Step S1: for the phenomenon that blood cell image is fuzzy and contrast is not high, being combined by the fractional order differential algorithm of the similar round mask operator of morphology denoising and improvement and blood cell image carries out pretreatment, the fractional order differential algorithm of described improvement pollutes in the dyeing filtering blood cell image and remains cell edges details preferably while grain noise；
Step S2: with watershed algorithm, pretreated image is carried out just segmentation, overdivided region is mapped as node；
Step S3: cell image step S2 obtained with graph theory minimum spanning tree (MST) algorithm improved is split again.
In the present embodiment, in described step S1, select the template of suitable size for the size of cell in cell image, it is proposed to a kind of Tiansi operator based on vector merge similar round improve mask template, specifically include following steps:
Step S11: the square Tiansi fractional order differential mask template of one 5x5 of structure, as in figure 2 it is shown, Tiansi mask operator is square structure, therefore can a on the position of 4 drift angles of outermost layer2Treating as is the starting point 4 vectors at template initial pointAnd take its half
Step S12: the upper left corner of the mask operator template obtained after described step S11 is processed is with the lower left cornerAccording to the principle that Fig. 4 vector merges, merge into level new vector to the left
Step S13: the combined vector obtained after described step S12 is processedBe added to the vector in original third layer centre position, just obtains the mask template of similar round, as shown in Figure 3.
In the present embodiment, described step S2 specifically includes following steps:
Step S21: assume that image obtains 6 overdivided regions as shown in Figure 5 after watershed algorithm is split: 1,2,3,4,5,6}, the limit collection of its mapped node is:
E={e1,e2,e3,e4,e5,e6,e7,e8,e9}={ (1,2), (1,5), (2,3), (2,5), (3,4), (3,5), (4,5), (4,6), (5,6) }；
The weighted value of described limit collection respectively 5,9,6,8,4,1,7,4,2；
Step S22: the weighted value on overdivided region and limit is mapped one by one and obtains initial weighted undirected graph, as shown in Figure 6.
In the present embodiment, described step S3 specifically includes following steps:
Step S31: map: the overdivided region of input picture is mapped as node, calculates weighted undirected graph G={V, E, the ψ of 8 neighborhood systems by the weighted value definition improved }, wherein | V |=n, | E |=m, connect node viAnd vjThe weighted value on limit is ψ (vi,vj), setting area minimum area is p；
Step S32: state initialization: set Sq-1It is the cut set after q-1 sub-region merges, makes q=1 make initial segmentation be Sq-1=S0, S0=(v1,v2,…,vn), namely in V, each element (node) is a region, by prime area internal diversityRegion areaWherein i=1,2,3 ... n；
Step S33: sequence: weighted undirected graph G={V, E, ψ } in all of weighted value according to ascending order arrangement, obtain queue π=(O1,O2,…,Om)；
Step S34: if the queue in step S33 is not empty, makes the limit dequeue that weighted value is minimum, otherwise forward step S37 to；
Step S35: from Sq-1Structure Sq: setWithIt is S respectivelyq-1The two end node v on the limit of middle dequeueiAnd vjThe region at place, if:
And
Then by regionWithMerge and obtain Sq, and calculate the weighted value of new region and its neighborhood system；Otherwise nonjoinder, makes Sq=Sq-1；
Step S36: the weighted value according to new region, updates the internal diversity of new region, region difference and weighted value queue π, makes q=q+1, returns step S34；
Step S37: traversal SqEach region, (vi,vj) ∈ E, vi∈ C1, vj∈ C2, if | C1 | < p or | C2 | < p, then remerges C1 and C2；
Step S38: be same color by the pixel assignment belonging to the same area, it is simple to the observation analysis of human eye；
Step S39: the segmentation result of the minimal spanning tree algorithm that output improves herein.
The foregoing is only presently preferred embodiments of the present invention, all equalizations done according to the present patent application the scope of the claims change and modify, and all should belong to the covering scope of the present invention.
Claims (4)
1. the adhesion blood cell image dividing method based on the fractional order differential improved and graph theory, it is characterised in that: comprise the following steps:
Step S1: for the phenomenon that blood cell image is fuzzy and contrast is not high, being combined by the fractional order differential algorithm of the similar round mask operator of morphology denoising and improvement and blood cell image carries out pretreatment, the fractional order differential algorithm of described improvement pollutes in the dyeing filtering blood cell image and remains cell edges details preferably while grain noise；
Step S2: with watershed algorithm, pretreated image is carried out just segmentation, overdivided region is mapped as node；
Step S3: cell image step S2 obtained with graph theory minimum spanning tree (MST) algorithm improved is split again.
2. a kind of adhesion blood cell image dividing method based on the fractional order differential improved and graph theory according to claim 1, it is characterized in that: in described step S1, propose a kind of Tiansi operator based on vector merge similar round improve mask template, specifically include following steps:
Step S11: the square Tiansi fractional order differential mask template of one 5x5 of structure, Tiansi mask operator is square structure, a on the position of 4 drift angles of outermost layer2Treating as is the starting point 4 vectors at template initial pointAnd take its half
Step S12: the upper left corner of the mask operator template obtained after described step S11 is processed is with the lower left cornerMerge into level new vector to the left
Step S13: the combined vector obtained after described step S12 is processedBe added to the vector in original third layer centre position, obtains the mask template of similar round.
3. a kind of adhesion blood cell image dividing method based on the fractional order differential improved and graph theory according to claim 1, it is characterised in that: described step S2 specifically includes following steps:
Step S21: assume that image obtains 6 overdivided regions after watershed algorithm is split: 1,2,3,4,5,6}, the limit collection of its mapped node is:
E={e1,e2,e3,e4,e5,e6,e7,e8,e9}={ (1,2), (1,5), (2,3), (2,5), (3,4), (3,5), (4,5), (4,6), (5,6) }；
The weighted value of described limit collection respectively 5,9,6,8,4,1,7,4,2；
Step S22: the weighted value on overdivided region and limit is mapped one by one and obtains initial weighted undirected graph.
4. a kind of adhesion blood cell image dividing method based on the fractional order differential improved and graph theory according to claim 1, it is characterised in that: described step S3 specifically includes following steps:
Step S31: map: the overdivided region of input picture is mapped as node, calculates weighted undirected graph G={V, E, the ψ of 8 neighborhood systems by the weighted value definition improved }, wherein | V |=n, | E |=m, connect node viAnd vjThe weighted value on limit is ψ (vi,vj), setting area minimum area is p；
Step S32: state initialization: set Sq-1It is the cut set after q-1 sub-region merges, makes q=1 make initial segmentation be Sq-1=S0, S0=(v1,v2,…,vn), namely in V, each element is a region, by prime area internal diversityRegion areaWherein i=1,2,3 ... n；
Step S33: sequence: weighted undirected graph G={V, E, ψ } in all of weighted value according to ascending order arrangement, obtain queue π=(O1,O2,…,Om)；
Step S34: if the queue in step S33 is not empty, makes the limit dequeue that weighted value is minimum, otherwise forward step S37 to；
Step S35: from Sq-1Structure Sq: setWithIt is S respectivelyq-1The two end node v on the limit of middle dequeueiAnd vjThe region at place, if:
And
Then by regionWithMerge and obtain Sq, and calculate the weighted value of new region and its neighborhood system；Otherwise nonjoinder, makes Sq=Sq-1；
Step S36: the weighted value according to new region, updates the internal diversity of new region, region difference and weighted value queue π, makes q=q+1, returns step S34；
Step S37: traversal SqEach region, (vi,vj) ∈ E, vi∈ C1, vj∈ C2, if | C1 | < p or | C2 | < p, then remerges C1 and C2；
Step S38: be same color by the pixel assignment belonging to the same area, it is simple to the observation analysis of human eye；
Step S39: the segmentation result of the minimal spanning tree algorithm that output improves herein.
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| CN107977978A (en) * | 2017-11-01 | 2018-05-01 | 浙江农林大学 | A kind of method and system that cytoadherence mode is determined based on elliptical shaft relation |
| CN109583479A (en) * | 2018-11-07 | 2019-04-05 | 北京市遥感信息研究所 | One kind being based on morphologic high spectrum image feature extracting method |
| CN112614104A (en) * | 2020-12-22 | 2021-04-06 | 湖南伊鸿健康科技有限公司 | Segmentation counting method for red blood cell overlapping and terminal |
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| CN112614104A (en) * | 2020-12-22 | 2021-04-06 | 湖南伊鸿健康科技有限公司 | Segmentation counting method for red blood cell overlapping and terminal |
| CN113139973A (en) * | 2021-04-01 | 2021-07-20 | 武汉市疾病预防控制中心 | Artificial intelligence-based plasmodium identification method and equipment |
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