CN103720489B - Pathological tissues growth monitoring method and system - Google Patents

Pathological tissues growth monitoring method and system Download PDF

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CN103720489B
CN103720489B CN201310749638.0A CN201310749638A CN103720489B CN 103720489 B CN103720489 B CN 103720489B CN 201310749638 A CN201310749638 A CN 201310749638A CN 103720489 B CN103720489 B CN 103720489B
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pathological tissues
tissues
growth
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CN103720489A (en
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肖杨
张雪
郑海荣
牛丽丽
王丛知
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Shenzhen Institute of Advanced Technology of CAS
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Shenzhen Institute of Advanced Technology of CAS
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Abstract

The invention provides a kind of pathological tissues growth monitoring method and system, wherein method comprises step: the inducing shear ripple obtaining diseased tissue area deformation, obtains the elastic image of pathological tissues according to inducing shear ripple; Carry out pointwise coupling for the ROI region of elastic image and pixel in tracing area, build the elastic information image of ROI region; Determine the soft or hard attribute of pathological tissues in elastic information image; Infiltration degree tracking is carried out to elastic information image, determines invade tissues regional extent; The infiltration growth characteristics parameter of pathological tissues is obtained according to elastic information image, soft or hard attribute and invade tissues regional extent; The growth of infiltration growth characteristics parameter to described pathological tissues according to pathological tissues is monitored.Technical scheme of the present invention, the elastic information of sign pathological tissues that can be complete, good/Malignant mass nicety of grading is high, and tumor growth monitoring accuracy is high, for the pathological research of tumor and early diagnosis treatment provide important evidence.

Description

Pathological tissues growth monitoring method and system
Technical field
The present invention relates to technical field of medical image processing, particularly relate to a kind of pathological tissues growth monitoring method and system.
Background technology
Medicine clinical research shows, during cell generation pathological changes, the biomechanics characteristic generation significant change of himself and normal surrounding tissue, utilize the image information of display organization elastic parameter to carry out quantitative sign, assessment and monitoring to the growth characteristics of breast tumor, to the preoperative prediction of Breast-consering surgery, target volume define and benign tumors, pernicious differentiation etc. are all significant.
Such as, breast tumor growth characteristic based on elastic parameter is monitored, current research work mainly concentrates on and how to build on elastic image, lack the perspective study to elastic image information and clinical diagnoses, diagnostic result is often subject to diagnosis person's subjective impact, the elastic parameter of tumor growth is extracted and is not formed unified standard, and these all have impact on breast tumor growth feature evaluation and monitoring accuracy.
Tumor is a lot of tissue disease the most basic the most common sign, normal some features according to tumor clinically, as size, hardness and surface flatness thereof etc. are assessed neoplastic lesion situation, realize the growth monitoring of tumor, stiffness characteristics is as the common attribute of breast tumor, owing to lacking advanced instrument, all can only measure in the mode of palpation all the time.
In recent years, elastography obtains and develops widely, its information such as the skew by display organization, strain describes tissue elasticity degree, makes the extraction of tissue elasticity growth characteristics become possibility, is also at present clinically for measuring the main mode of tissue elasticity growth information.
Research for growth characteristics parameter tumor-infiltrated on two-dimension elastic image is scarcely out of swaddling-clothes, prior art mainly contains following several: 1. " Biomedical Supersonics " (ten thousand bright habit chief editors) volume two 480-483 page, Chinese Patent Application No. 201030119332.4 " liver cirrhosis detecting instrument ", " PLA's medical journal " the 36th volume o. 11th 1131-1133 pages in 2011 and " Chinese medicine image technology " the 28th volume the 3rd phase 529-533 page in 2012, in ultrasonic Transient elastography image, elastic characteristic is characterized with the elasticity average of area-of-interest, this characteristic parameter is mainly used in diagnosing liver fibrosis, the liver dispersivity pathological changes such as liver cirrhosis, 2. Guangxi Medical University's master thesis " the clinical value research of real-time ultrasound elastogram Diagnosis of Focal Liver Lesions " (author: Mei Chenling), " Chinese medicine image technology " the 26th volume the 9th phase 1682-1684 page, " modern biomedical progress " the 26th volume the 9th phase 492-494 pages in 2010 and " West China medical science " the 25th volume the 2nd phase 294-297 page in 2010 in 2010 in 2011, color distribution feature according to focus elastic image is marked, thus instruct the attribute of pathological changes to differentiate, five namely conventional clinically point-scores, 3. " Cancer Control " 17 volumes the 3rd phase 156-161 page in 2010, " clinical medicine " the 31st volume 10 phase 93-94 page in 2011, " contemporary medical science " the 18th volume the 1st phase 7-9 page in 2012, The 2nd Army Medical College 2011 master thesis " ultrasonic elastograph imaging is to the research of Hepatic Tumors differential diagnosis value " (author: Ji Jianfeng), " Chinese medicine image technology " the 18th volume the 7th phase 589-591 pages in 2009 and " world Chinese digests magazine " the 18th volume the 30th phase 3254-3258 page in 2010, by tumor region strain rate ratio on calculating elastic image, infer the hardness of tumor relative to normal surrounding tissue, 4. " Chinese ultrasound medicine magazine " the 25th volume the 4th phase 362-364 page in 2009, Fudan University 2010 master thesis " application of Real time Organization elastogram in distinguishing between benign and malignant breast lump " (author: palace rosy clouds), by tumor region area ratio on elastic image, infer the hardness of tumor relative to normal surrounding tissue, 5. " J Ultrasound Med " 2012 the 31st phase 281-287 page the lenth ratio of tumor region on tumor region and B-mode on elastic image, the elastic characteristic of assess lesion region and normal tissue regions are proposed.
Technical scheme in sum, the elastic characteristic that 1. method is extracted is quantitative; 2. the elastic characteristic that 3. 4. 5. method is extracted is qualitative or sxemiquantitative.Concrete, 1. for adopting the instantaneous elasticity detection technique of one-dimensional image technology, although can quantitative organize average elastic modulus value, two-dimension elastic distributed intelligence cannot be obtained, be generally only applicable to detect dispersivity pathological changes.2. be to elastic modelling quantity estimation a kind of method substantially the most the earliest, be also current clinical diagnostic applications elastic characteristic the most widely, but also there is following shortcoming: 1) due to tumor multiplicity, standards of grading are improved not and are not enough to be applicable to all tumor presence; 2) subjective assessment made elastic image by sonographer of scoring process, causes scoring process to be subject to the impact of doctor's subjective factors.3. be 4. 5. the feature of recent scholars for assessment of tumor elastic performance, compare the firmness change degree that 2. more can reflect focus objective quantitative, but still there is part to be modified: 1) feature cannot carry out the lateral comparison between different focus, there is certain overlap in the above-mentioned ratio interval obtained under some pathological changes, only relies on single ratio identification easily to cause mistaken diagnosis; 2) in focal area soft or hard region define use fixed threshold, due to elastogram shortcoming, define the actual corresponding adaptability to changes of threshold value in different images different, cause elasticity number quantization error; 3) when classifying to benign from malignant tumors, adopt single threshold value to image classification, the suitability is not strong, causes mistaken diagnosis, fails to pinpoint a disease in diagnosis.
In sum, apply the elastic characteristic value that above-mentioned various method obtains, the elastic information of sign tumor that cannot be complete, there is the problem that good Malignant mass nicety of grading is low, tumor growth monitoring accuracy is low.
Summary of the invention
Based on this, be necessary also to there is the elastic information of sign tumor that cannot be complete, problem that good Malignant mass nicety of grading is low for above-mentioned elastic characteristic value, a kind of pathological tissues growth monitoring method and system is provided.
A kind of pathological tissues growth monitoring method, comprises the steps:
Obtain the inducing shear ripple of diseased tissue area deformation, obtain the elastic image of pathological tissues according to described inducing shear ripple;
Carry out pointwise coupling for the ROI region of described elastic image and pixel in tracing area, build the elastic information image of ROI region;
Determine the soft or hard attribute of pathological tissues in described elastic information image;
Infiltration degree tracking is carried out to described elastic information image, determines invade tissues regional extent;
The infiltration growth characteristics parameter of pathological tissues is obtained according to described elastic information image, soft or hard attribute and invade tissues regional extent;
The growth of infiltration growth characteristics parameter to described pathological tissues according to described pathological tissues is monitored.
A kind of pathological tissues growth monitoring system, comprising:
Elastic image acquisition module, for obtaining the inducing shear ripple of diseased tissue area deformation, obtains the elastic image of pathological tissues according to described inducing shear ripple;
Elastic information picture creation module, for carrying out pointwise coupling for the ROI region of described elastic image and pixel in tracing area, builds the elastic information image of ROI region;
Organize soft or hard attribute determination module, for determining the soft or hard attribute of pathological tissues in described elastic information image;
Infiltration degree tracking module, for carrying out infiltration degree tracking to described elastic information image, determines invade tissues regional extent;
Elastic characteristic parameter extraction module, for obtaining the infiltration growth characteristics parameter of pathological tissues according to described elastic information image, soft or hard attribute and invade tissues regional extent;
Pathological tissues growth monitoring module, monitors for the growth of infiltration growth characteristics parameter to described pathological tissues according to described pathological tissues.
Above-mentioned pathological tissues growth monitoring method and system, merge acoustic radiation force elastography and image processing techniques acquisition two-dimension elastic modulus figure, utilize image processing techniques to carry out pretreatment to image and obtain tumor-infiltrated degree profile, realize auto Segmentation and the soft or hard region deviding of wetted area on two-dimension elastic image, then combining image process knowledge and clinical experience data, from image, identification ability is extracted high according to the growth characteristics of wetted area, the elasticity number argument sequence of strong robustness, adopt the Infiltrating of mechanics imaging means assess lesion tissue, the elastic information of complete sign pathological tissues, the good of pathological tissues can be instructed, pernicious differentiation, good/Malignant mass nicety of grading is high, pathological tissues growth monitoring accuracy is high, for the pathological research of tumor and early diagnosis treatment provide important evidence, for the early diagnosis of tumor and pathological study treatment provide new effective way.
Accompanying drawing explanation
Fig. 1 is the pathological tissues growth monitoring method flow diagram of an embodiment;
Fig. 2 is elastic information image reconstruction result schematic diagram;
Fig. 3 is elastic image soft or hard region deviding result schematic diagram;
Fig. 4 is tissue infiltration's degree frontier tracing result schematic diagram;
Fig. 5 is that schematic diagram is selected in invade tissues neighboring area;
Fig. 6 is the pathological tissues growth monitoring system structure schematic diagram of an embodiment;
Fig. 7 is the zone boundary tracking results schematic diagram of fibroadenoma (optimum);
Fig. 8 is IDC (pernicious) zone boundary tracking results schematic diagram.
Detailed description of the invention
Be described in detail below in conjunction with the detailed description of the invention of accompanying drawing to pathological tissues growth monitoring method and system of the present invention.
Pathological tissues growth monitoring method and system of the present invention, the various pathological tissues growth of monitoring can be applied to, such as, the extent of elasticity assessments such as such as liver, pulmonary, thyroid, breast tumor infiltrates the assessment of growth characteristics extent of elasticity, also may be used for lesion region extent of elasticity assessment on mammary gland and other position of human body.
Shown in Figure 1, Fig. 1 is the pathological tissues growth monitoring method flow diagram of an embodiment, mainly comprises the steps:
Step S10, obtains the inducing shear ripple of diseased tissue area deformation, obtains the elastic image of pathological tissues according to described inducing shear ripple.
In this step, can induce by utilizing the ultrasonic acoustic radiation field of force propagation of shearing wave in mechanics of biological tissue produced, obtaining the inducing shear ripple of diseased tissue area deformation, and then obtaining the elastic image of pathological tissues according to inducing shear ripple.
Consider that the shearing wave that the induction of the ultrasonic acoustic radiation field of force produces organizes the propagation (comprising fat, connective tissue etc. in as mammary gland) in different structure at complex biological, the impact of the non-ideal factors such as heterogeneity, velocity of sound decay, border can be introduced, simultaneously because the electronic noise of ultrasonic system and quantizing noise, tissue compression cause the reasons such as echo-signal axial deformation, the effect of signal Correlaton can be produced, make Displacement Estimation bring larger error.Therefore, need to provide one more accurate elastograph imaging method, to obtain high-quality breast tumor two-dimension elastic modulus scattergram.
In one embodiment, the elastic image process of the acquisition pathological tissues of step S10 specifically comprises the steps:
Step S101, the radiant force utilizing focused ultrasound beams to produce obtains breast tumor regional deformation inducing shear ripple.
Particularly, adopt the Ultrasonic Elasticity Imaging based on acoustic radiation force, the radiant force utilizing ultrasonic system to produce obtains the deformation induced shearing wave of diseased tissue area.
Step S102, follows the trail of and shears the propagation in crest face, and calculation side, to various point locations on propagation path and the relation curve of shearing wave crest time of advent, calculates shear wave velocity according to described relation curve.
Particularly, first the RF signal obtaining shearing wave is carried out Hilbert transform, becomes analytic signal:
r ^ ( n , s ) = r ( m , s ) * h ( m ) = 1 π ∫ - ∞ ∞ r ( τ , s ) m - τ dτ - - - ( 1 )
In formula (1), m is the sample point coordinate of RF signal, and s represents the time coordinate of sampled point; * represent convolution algorithm, h (m) is the FIR Hilbert transform factor.
Choose the long L of suitable observation window, computing cross-correlation carried out to above-mentioned analytic signal:
k ^ ( n , s ) = Σ 1 = - L 2 L 2 - 1 r ^ ( m + 1 , s ) r ^ * ( m + 1 + L , s + 1 ) - - - ( 2 )
In formula (2), k^ (n, s) is cross-correlation coefficient, and subscript * represents conjugate complex number.The ultrasound echo signal time shift Δ t caused when micro-displacement is less than sampling period t stime, the phase information that can be comprised by cross-correlation coefficient tries to achieve time shift:
In formula (3), n maxrepresent the coordinate of k^ (n, s) maximum, ∠ is angle computing, ∠ (a+jb)=arctan (b/a), and after obtaining time shift value, namely micro-displacement tries to achieve by ultrasonic velocity c:
d ( m , s ) = 1 2 cΔt - - - ( 4 )
In formula (4), d (m, s) represents micro-displacement.
Step S103, obtains Young's modulus of elasticity according to described shear wave velocity, obtains the high-resolution elastic image of pathological tissues according to described Young's modulus of elasticity.
Particularly, by following the trail of the propagation of shearing crest face (i.e. displacement maximum), calculate the relation curve of the time of advent of various point locations and shearing wave crest on lateral propagation path, Radon transform is utilized to find optimum linearity matching scheme, calculate shear wave velocity, substitute into formula (5) and calculate Young's modulus of elasticity:
E = 2 ( 1 + v ) ρ V s 2 - - - ( 5 )
While meeting real time implementation requirement, make jitter effect minimize and close to the lower limit of cram é r-rao, obtain the elastic image of the quality, high resolution of pathological tissues.
In the present embodiment, have employed the Ultrasonic Elasticity Imaging based on acoustic radiation force, utilize ultrasonic system to obtain the deformation induced shearing wave of diseased tissue area.Based on the non-homogeneous biological tissue elasticity formation method of acoustic radiation force, obtain the two-dimension elastic modulus figure of more accurate mammary gland tissue; That the RF signal data collected is divided into some segments according to depth direction relative to traditional cross correlation algorithm based on time-domain signal coupling, make computing cross-correlation by often a bit of with adjacent RF signal data, and then the displacement (being determined by the peak value of cross-correlation function) obtaining tissue is with the technological means of the degree of depth and the change of time.It is very little that the method can be adapted to the tissue biases amount that acoustic radiation force causes, and generally only has tens microns even to only have the situation of several microns.By improving the cross correlation algorithm of routine, thus more accurately can solve micro-displacement, the elastic image of quality, high resolution can be obtained.
Step S20, carries out pointwise coupling for the ROI region (Region Of Interest, area-of-interest) of described elastic image and pixel in tracing area, builds the elastic information image of ROI region.
In this step, due to the particularity of elasticity ultrasonoscopy, before tissue regions extent of elasticity being assessed according to elasticity ultrasonoscopy, need image in the pretreatment operation ensureing to rebuild elastic information image when not destroying amount of image information.
Elastic image utilizes tissue ultrasonic imaging technique by probe apparatus to the strain organizing applying one small, collect measured body echo-signal fragment and it is analyzed, property estimate deformation extent and the tissue elasticity coefficient magnitude of tissue, then with GTG or coloud coding imaging.
In elastic image, the colouring information of characterizing tissues coefficient of elasticity size is superimposed upon on its B-mode image and shows, and in order to extent of elasticity is organized in assessment accurately, first will reconstruct elastic information image.The mode that the present invention utilizes pointwise to mate follows the tracks of ROI region pixel on elastic image, and reconstruct tissue elasticity frame, detailed process is as follows:
If elasticity vision-mix ROI region is I c, B-mode image ROI region is I b, after rebuilding, image ROI region is I r, according to elastogram principle, elastic information image can be set up by following formula:
I r(i,j)=I c(i,j)-I b(i,j) (6)
Wherein, (i, j) is pixel coordinate in ROI region, by rebuilding elastic information image, quantize to extract and analyze tissue elasticity characteristic sequence on the image after rebuilding, remove the interference of B-mode image, the extent of elasticity of follow-up invade tissues is assessed more reliable.Concrete effect can shown in reference diagram 2, Fig. 2 is elastic information image reconstruction result schematic diagram, for mammary gland tissue in figure, give mammary gland elastic image, B-mode image and elastic information image after rebuilding, figure a is original elastic image, figure b is the elastic information image rebuild, and figure c is B-mode image.
Step S30, determines the soft or hard attribute of pathological tissues in described elastic information image.
In this step, the soft or hard attribute realized in pathological tissues ROI region by elastic information image is defined.
Traditional pathological tissues infiltrates growth characteristics elastic parameter sequential extraction procedures technology, the definition of soft or hard Region dividing is generally realized by a fixed threshold, on elastic image, in different patient image, the actual corresponding adaptability to changes of same color is not necessarily identical, but the suitability of fixed threshold cutting techniques is not strong, easily causes elasticity number quantization error.In fact on tissue elasticity frame, the change of organization internal color does not have obvious boundary, and the inner soft or hard region of pathological tissues is the fuzzy set of boundary fuzzy, and traditional extractive technique, obviously can not bring high-precision soft or hard attribute to define.
For this reason, the present invention also provides a kind of soft or hard attribute confining method of more accurate pathological tissues.
In one embodiment, step S30 determines that the process of the soft or hard attribute of pathological tissues in described elastic information image can comprise as follows:
First the cluster centre of described elastic information image is calculated; Wherein, cluster centre comprises territory, hard area cluster centre and soft region clustering center.Then calculate the fuzzy similarity matrix of each element on described elastic information image and cluster centre square distance and, obtain the degree of membership that each element belongs to soft or hard interval.Again according to the soft or hard attribute of degree of membership determination ROI region tissue.Particularly, above-mentioned processing procedure is resolved as follows:
For elastic information image, if its gray level image is I, u ijrepresent that in image, a jth pixel belongs to the degree of membership of the i-th class, n is the sum of all pixels of image I, then cluster object function is as follows:
E ( U , V ) = Σ i = 1 2 Σ j = 1 n ( u ij ) m ( d ij ) 2 , ( u ij ∈ [ 0,1 ] , Σ i = 1 2 u ij = 1 ) - - - ( 7 )
Wherein, U is initial subordinated-degree matrix, and m is weighted index, and m ∈ [1 ,+∞), d ijfor each pixel is to center vector distance, V is cluster centre, V=(v 1, v 2) t, number of namely classifying is 2, i=1,2.
After degree of membership and cluster centre are determined, utilize Lagrange multiplier, order:
F = Σ i = 1 2 Σ j = 1 n ( u ij ) m ( d ij ) 2 + Σ j = 1 n ( Σ i = 1 2 u ij - 1 ) ∂ F ∂ x i ′ = 0 , ∂ F ∂ u ij = 0 - - - ( 8 )
Solving equation group (8) can obtain:
u ij = 1 Σ l = 1 2 ( d ij d lj ) 2 ( m - 1 ) , ( i = 1,2 ; j = 1,2 , . . . , n ) - - - ( 9 )
v i = Σ n j = 1 ( u ij ) m x j Σ j = 1 2 ( u ij ) m , ( i = 1,2 ) - - - ( 10 )
Subordinated-degree matrix U and cluster centre V obtains as follows:
1., under the prerequisite meeting degree of membership constraints, between 0-1, subordinated-degree matrix U is initialized.
2. (10) formula of applying solves 2 cluster centres (territory, hard area cluster centre, soft region clustering center).
3. E (U, V) is calculated according to (7).
Repeat said process, until the change of cluster centre V be less than certain threshold value or substantially constant time stop calculating, obtain best fuzzy classified matrix and cluster centre, thus realize ROI region and organize soft or hard attribute to define.
The confining method of the soft or hard attribute of above-mentioned pathological tissues, by the square distance of the fuzzy similarity matrix and cluster centre of asking for each element on elastic information image space and, obtain the interval degree of membership of element soft or hard, finally realize ROI region inner tissue soft or hard attribute to define, this confining method adopts Fuzzy clustering techniques automatically to determine to organize soft or hard attribute, compared with traditional method, the accuracy that soft or hard attribute defines is higher, and the suitability is stronger.It defines result can shown in reference diagram 3, and Fig. 3 is elastic image soft or hard region deviding result schematic diagram, and still for mammary gland tissue in figure, wherein black line is soft or hard regional edge boundary line.
It is to be understood that, above-mentioned for preferably to define mode, define for organizing soft or hard attribute and also can apply other two sorting algorithm, do not repeat them here.
Step S40, carries out infiltration degree tracking to described elastic information image, determines invade tissues regional extent.
In this step, obtain according to the bounds of the pathological tissues of elastic information image and infiltrate growth characteristics argument sequence, the extent of elasticity of analysis and evaluation pathological tissues, and be used to guide its growth monitoring, therefore, before extent of elasticity assessment, infiltration degree tracking is carried out to elastic information image, thus determine the bounds of pathological tissues.
In one embodiment, consider complexity and the scrambling of elastic information image, first the tracking of tissue infiltration's degree is completed under B-mode image, then under being mapped to elastic information image, realize tissue infiltration's degree image boundary on elastic image to follow the tracks of, concrete, the process of step S40 can comprise as follows:
First, obtain the B-mode image of elastic information image, and on B-mode image, tracking is carried out to the zone boundary of pathological tissues and obtain tissue infiltration's degree border; Then extract the marginal information of B-mode image and be fused on elastic information image, obtaining the invade tissues regional extent on elastic information image.
The infiltration degree tracking scheme of above-described embodiment, make use of B-mode undertissue and infiltrate the complicated irregular feature of edge shape, adopt the tissue infiltration's degree frontier tracing method based on Chan-Vese model, concrete grammar is described below:
If the elastic image I of B-mode 0coordinate set be Ω, ω be the subgraph be defined in Ω, then image division is inside (C) and outside (C) two regions by the border C of ω.
If c 1and c 2represent the average gray in these two regions respectively, level set function represent inside (C) and outside (C), then when following energy function time minimum, level set function zero level collection be the object boundary curve C of expectation:
In above formula, μ, v, λ 1, λ 2all constant coefficient, be Heaviside function, each step is developed c 1and c 2can by below two formulas calculate:
c 1 ( φ ) = ∫ Ω u 0 ( x , y ) H ( φ ) dxdy ∫ Ω H ( φ ) dxdy c 2 ( φ ) = ∫ Ω u 0 ( x , y ) ( 1 - H ( φ ) ) dxdy ∫ Ω ( 1 - H ( φ ) ) dxdy - - - ( 12 )
Setting function in time t develop, by formula (12) can derive about euler-Lagrange equation:
∂ φ ∂ t = δ ϵ ( φ ) [ μ div ( ▿ φ | ▿ φ | ) - v - λ 1 ( u 0 - c 1 ) 2 + λ 2 ( u 0 - c 2 ) 2 ] = 0 , in ( 0 , ∞ ) × Ω φ ( 0 , x , y ) = φ 0 ( x , y ) , inΩ δ ϵ ( φ ) | ▿ φ | ∂ φ ∂ n ‾ = 0 , on ∂ Ω - - - ( 13 )
In above formula, the approximate of Dirac function, represent the outer normal vector on border, δ 0(x, y) is the symbolic measurement obtained by initial boundary.
Solving equation (13) obtains object boundary curve C, thus complete tissue infiltration's degree frontier tracing, above-mentioned tracking mode, in conjunction with auto Segmentation technology from motion tracking invade tissues border, avoid the subjectivity of manual segmentation to affect, improve segmentation accuracy; Frontier tracing result can shown in reference diagram 4, Fig. 4 is tissue infiltration's degree frontier tracing result schematic diagram, still for mammary gland tissue in figure, figure a is tissue infiltration's degree frontier tracing result on B-mode image, figure b is tissue infiltration's degree frontier tracing result on the elastic information image after rebuilding, and figure c is elastic information gray level image undertissue infiltration degree frontier tracing result.
It is to be understood that above-mentioned is preferred infiltration degree tracking mode, tissue infiltration's degree is followed the tracks of to the image partition method also can applying other routine, do not repeat them here.
Step S50, obtains the infiltration growth characteristics parameter of pathological tissues according to described elastic information image, soft or hard attribute and invade tissues regional extent.
In this step, in the ROI region of Main Analysis pathological tissues, the elastic information in each region and carve information, obtain one group of tumor-infiltrated growth characteristics argument sequence, reflects the extent of elasticity situation of invade tissues and surrounding tissue thereof objectively.
In one embodiment, infiltrate growth characteristics parameter to comprise: wetted area hardness ratio, elastic characteristic ratio, elasticity average, elasticity variance and elasticity mark.
1) for diseased tissue area hardness ratio; Invade tissues zone hardness Ratio Features Inva hardbe expressed as:
Inva hard = N hard N inva - - - ( 14 )
In formula, N hardthat in wellability tissue regions, attribute is territory, hard area pixel number; N invafor pixel number in wellability tissue regions.Inva hardcharacterize the relative resilient degree of certain limit inner tissue, it shows more greatly tissue elasticity less (namely organizing harder), on the contrary then elasticity larger (namely organizing softer).
2) for diseased tissue area elasticity average; Invade tissues region elastic modelling quantity meansigma methods Inva avgbe expressed as:
Inva avg = 1 N inva Σ i = 1 N inva I i - - - ( 15 )
In formula, N invait is pixel number in invade tissues region; I iit is the gray value that on elastic image, pixel is corresponding.Inva avgfor characterizing the average elasticity degree of certain limit inner tissue.
3) for diseased tissue area elasticity variance; The elastic modelling quantity variance Inva in invade tissues region stdbe expressed as:
Inva std = 1 N inva - 1 Σ i = 1 N inva ( I i - Inva avg ) 2 - - - ( 16 )
In formula (16), N invait is pixel number in invade tissues region; I iit is the gray value that on elastic image, pixel is corresponding; Inva avgit is average elasticity average in invade tissues region; Inva stdfor characterizing the uniformity of certain limit inner tissue extent of elasticity.
4) for pathological tissues territory elasticity ratio; The elasticity ratio I nva in invade tissues region eratiobe expressed as:
Inva eratio = Inva avg ROI savg - - - ( 17 )
In formula (17), Inva avgit is the elasticity average in invade tissues region; ROI savgit is the elasticity average of area-of-interest.Inva eratiocharacterize the relative resilient intensity of variation of invade tissues and its periphery certain limit inner tissue.
5) for pathological tissues elasticity mark; The main elasticity point system (5 point-score) according to extensive employing clinically at present of elasticity mark, analyze the hardness ratio distribution of in wetted area and neighboring area, with modeling pattern, standards of grading are quantified as discriminant parameter Esc, calculation procedure is as follows:
1. invade tissues zone hardness ratio Inva is asked for hard;
2. according to invade tissues region contour, if be invade tissues neighboring area apart from region, this profile 30mm place, asking in this region is hardness ratio Peri in invade tissues neighboring area hard; As shown in Figure 6, Fig. 5 is that schematic diagram is selected in invade tissues neighboring area to neighboring area profile, and wherein, the region between solid white line and grey filled lines is invade tissues neighboring area, can be used for calculating elastic grading parameters.
3. elasticity Rating Model is as follows:
Table 1 elasticity Rating Model
Elasticity scoring characterizes wetted area elasticity Relative distribution situation, with color variability display organization firmness change information in elastic image, colouring information from red to indigo plant, correspondingly be the DE of lump by soft to firmly.Binding of pathological is gained knowledge, and generally, optimum focus is labeled as indigo plant, green, and intralesional elasticity distribution is comparatively even, less with the elastic contrast of surrounding tissue; Pernicious focus shows as red, yellow, and the elasticity distribution of intralesional is uneven (as blood vessel, calcification etc.), larger with the elastic contrast of surrounding tissue.Aforesaid way creates the profit growth characteristics argument sequence of pathological tissues, can avoid the limitation of single parameter, make the analysis of growth characteristics more comprehensive.
Step S60, the growth of infiltration growth characteristics parameter to described pathological tissues according to described pathological tissues is monitored.
In this step, monitor based on the growth of infiltration growth characteristics parameter to pathological tissues, can according to the infiltration growth characteristics parameter be made up of hardness ratio, elasticity average, elasticity variance, flexible ratio and elasticity mark of said extracted and quantification, this infiltrates growth characteristics argument sequence and reflects the extent of elasticity describing invade tissues from different perspectives.And then can judge the growth conditions of pathological tissues by strong, objective according to this identification ability, repeatable strong characteristic sequence, for analyzing the elastic performance of invade tissues, thus realize the monitoring that pathological tissues grows.
Shown in Figure 7, Fig. 7 is the pathological tissues growth monitoring system structure schematic diagram of an embodiment, comprising:
Elastic image acquisition module 10, for obtaining the inducing shear ripple of diseased tissue area deformation, obtains the elastic image of pathological tissues according to described inducing shear ripple;
Elastic information picture creation module 20, for carrying out pointwise coupling for the ROI region of described elastic image and pixel in tracing area, builds the elastic information image of ROI region;
Organize soft or hard attribute determination module 30, for determining the soft or hard attribute of pathological tissues in described elastic information image;
Infiltration degree tracking module 40, for carrying out infiltration degree tracking to described elastic information image, determines invade tissues regional extent;
Elastic characteristic parameter extraction module 50, for obtaining the infiltration growth characteristics parameter of pathological tissues according to described elastic information image, soft or hard attribute and invade tissues regional extent;
Pathological tissues growth monitoring module 60, monitors for the growth of infiltration growth characteristics parameter to described pathological tissues according to described pathological tissues.
In one embodiment, described elastic image acquisition module 10 is further used for:
The radiant force utilizing focused ultrasound beams to produce obtains breast tumor regional deformation inducing shear ripple; Follow the trail of and shear the propagation in crest face, calculation side, to various point locations on propagation path and the relation curve of shearing wave crest time of advent, calculates shear wave velocity according to described relation curve; Obtain Young's modulus of elasticity according to described shear wave velocity, obtain the high-resolution elastic image of pathological tissues according to described Young's modulus of elasticity.
In one embodiment, described soft or hard attribute determination module 30 of organizing is further used for:
Calculate the cluster centre of described elastic information image; Wherein, cluster centre comprises territory, hard area cluster centre and soft region clustering center; Calculate the fuzzy similarity matrix of each element on described elastic information image and cluster centre square distance and, obtain the degree of membership that each element belongs to soft or hard interval; According to the soft or hard attribute of degree of membership determination ROI region tissue.
In one embodiment, described infiltration degree tracking module 40 is further used for:
Obtain the B-mode image of elastic information image; B-mode image carries out tracking to the zone boundary of pathological tissues and obtains tissue infiltration's degree border; Extract the marginal information of B-mode image and be fused on elastic information image, obtaining the invade tissues regional extent on elastic information image.
In one embodiment, described infiltration growth characteristics parameter comprises: wetted area hardness ratio, elastic characteristic ratio, elasticity average, elasticity variance and elasticity mark.
Pathological tissues growth monitoring system of the present invention and pathological tissues growth monitoring method one_to_one corresponding of the present invention, the technical characteristic of setting forth in the embodiment of above-mentioned pathological tissues growth monitoring method and beneficial effect thereof are all applicable in the embodiment of pathological tissues growth monitoring system, do not repeat them here, hereby state.
Each embodiment in sum, the pathological tissues growth monitoring method and system that the present invention proposes:
(1) based on the two-dimension elastic image acquiring method of the non-homogeneous biological tissue of acoustic radiation force: by the offset characteristic of biological tissue under the effect of analysis acoustic radiation force, the formation mechenism of research ultrasound detection echo-signal, sets up the echo signal model of carrying displacement of tissue change information (shearing velocity of wave propagation).Shear velocity of wave propagation according to calculating and obtain elastic parameter, the final high-quality two-dimension elastic modulus figure obtaining pathological tissues.
(2) based on the extracting method of the pathological tissues lubricant nature growth characteristics argument sequence of elastic image: by analyzing the elastic information of invade tissues on color elastic image, combining image process and empirical data, from image, extract that some identification abilitys are high, strong robustness, the elasticity number parameter not relying on acquisition system and operator's maneuver, morphological characteristic and textural characteristics, the extent of elasticity of comprehensive, scientifical, the invade tissues that describes in a systematic way.
(3) in conjunction with the tumor growth method for monitoring and analyzing of acoustic radiation force technology, image procossing, mode identification technology: the present invention carries out accurate quantitative Analysis and analysis to the extent of elasticity feature of tissue biological's mechanical characteristics on elastic image, clinician is helped to analyze tumor-infiltrated growth characteristics, thus realize the growth monitoring of pathological tissues tumor, can be used for assist physician clinical diagnosis.
In order to verify technical scheme feasibility of the present invention and effectiveness, set forth technical scheme of the present invention below in conjunction with an application example, the ultrasonic breast tumor image adopting certain hospital's Ultrasonography to be gathered by HITACHI Vision8500 Ultrasound Instrument has carried out (comprising conventional B ultrasonic image and the elastic modelling quantity image of focus) quantification and the extraction of elastic characteristic sequence.Double width shows two-dimentional B ultrasonic image and elastic image simultaneously, keeps the stable of probe as far as possible, maintain 2 seconds during operation, the static B ultrasonic image of Cryopreservation and elastic image.
The technical scheme utilizing the present invention to propose is monitored tumor growth, shown in figure 7, Fig. 8 and table 2, sets forth fibroadenoma (optimum) and the tracking of IDC (pernicious) zone boundary and infiltrate growth characteristics argument sequence and extract result schematic diagram, wherein, Fig. 7 a is the B ultrasonic image of mammary gland fibroadenoma, Fig. 7 b is lesion segmentation result schematic diagram, and Fig. 7 c is springform spirogram; Fig. 8 a is the B ultrasonic image of infiltration ductal carcinomas of breast, and Fig. 8 b is lesion segmentation result schematic diagram, and Fig. 8 c is elastic modelling quantity gray-scale map.
As can be seen from above-mentioned accompanying drawing and table 2, fibroadenoma (optimum) hardness rate is less, and intralesional elasticity distribution is comparatively even, less with the elastic contrast of surrounding tissue; IDC (pernicious) hardness rate is comparatively large, and the elasticity distribution of intralesional is uneven, larger with the elastic contrast of surrounding tissue.From clinical trial results, the tumor growth monitoring system based on ultrasonic elastograph imaging that the present invention proposes, can be used for tumor growth monitoring, instruct good, the pernicious differentiation of tumor, for the pathological research of invade tissues and the early diagnosis treatment of malignant tumor provide new foundation.
Table 2 elastic characteristic sequence:
Technical scheme of the present invention, may be used for the assessment of various human soma (such as liver, pulmonary, thyroid etc.) extent of elasticity: the elastic characteristic sequence that invention proposes not only may be used for breast tumor and infiltrates the assessment of growth characteristics extent of elasticity, also may be used for lesion region extent of elasticity assessment on mammary gland and other position of human body.
In addition, not only can be applied to springform spirogram, two-dimension displacement figure and Two-dimensional strain figure can also be applied to: the border, focal area traced into is fused to two-dimension displacement figure and springform spirogram, also can extract a series of tumor-infiltrated growth characteristics parameter, realize the monitoring of tumor growth characteristic.
Finally, technical scheme of the present invention, be adapted to the growth monitoring of breast tumor especially, also can be applied to the growth monitoring (such as liver, pulmonary, thyroid etc.) of other human body organ-tissue: a series of growth characteristics parameters also can extracting other site tissue region simultaneously, realize the monitoring of growth.
Technical scheme of the present invention, merge acoustic radiation force elastography and image processing techniques, utilize ultrasonic acoustic radiation force system, deeply dissect the offset characteristic of biological tissue under acoustic radiation force effect, obtaining elastic parameter by calculating shearing velocity of wave propagation, obtaining the two-dimension elastic modulus figure of mammary gland tissue; For the two-dimension elastic modulus figure got, utilize image processing techniques to carry out pretreatment to image, the tumor-infiltrated degree profile of automatic acquisition, realize auto Segmentation and the soft or hard region deviding of wetted area on two-dimension elastic image; Then the clinical experience of combining image process knowledge and doctor, analyzes the growth characteristics of wetted area, extracts that some identification abilitys are high, the elasticity number argument sequence of strong robustness from image, comprehensive, scientifical, systematically assess tumor-infiltrated growth characteristics.In addition, can also combine with clinical practice, tested by clinical sample, inquire into the feasibility and effectiveness that adopt mechanics imaging means assessment (mammary gland) tumor-infiltrated situation, solve the actual difficult point in clinical practice, for the early diagnosis of (mammary gland) tumor and pathological study treatment provide new effective way.
The above embodiment only have expressed several embodiment of the present invention, and it describes comparatively concrete and detailed, but therefore can not be interpreted as the restriction to the scope of the claims of the present invention.It should be pointed out that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.

Claims (10)

1. a pathological tissues growth monitoring method, is characterized in that, comprises the steps:
Obtain the inducing shear ripple of diseased tissue area deformation, obtain the elastic image of pathological tissues according to described inducing shear ripple;
Carry out pointwise coupling for the ROI region of described elastic image and pixel in tracing area, build the elastic information image of ROI region;
Determine the soft or hard attribute of pathological tissues in described elastic information image;
Infiltration degree tracking is carried out to described elastic information image, determines invade tissues regional extent;
The infiltration growth characteristics parameter of pathological tissues is obtained according to described elastic information image, soft or hard attribute and invade tissues regional extent;
The growth of infiltration growth characteristics parameter to described pathological tissues according to described pathological tissues is monitored.
2. pathological tissues growth monitoring method according to claim 1, is characterized in that, the inducing shear ripple of described acquisition diseased tissue area deformation, and the step obtaining the elastic image of pathological tissues according to described inducing shear ripple comprises:
The radiant force utilizing focused ultrasound beams to produce obtains breast tumor regional deformation inducing shear ripple;
Follow the trail of and shear the propagation in crest face, calculation side, to various point locations on propagation path and the relation curve of shearing wave crest time of advent, calculates shear wave velocity according to described relation curve;
Obtain Young's modulus of elasticity according to described shear wave velocity, obtain the high-resolution elastic image of pathological tissues according to described Young's modulus of elasticity.
3. pathological tissues growth monitoring method according to claim 1, is characterized in that, describedly determines that the step of the soft or hard attribute of pathological tissues in described elastic information image comprises:
Calculate the cluster centre of described elastic information image; Wherein, cluster centre comprises territory, hard area cluster centre and soft region clustering center;
Calculate the fuzzy similarity matrix of each element on described elastic information image and cluster centre square distance and, obtain the degree of membership that each element belongs to soft or hard interval;
According to the soft or hard attribute of degree of membership determination ROI region tissue.
4. pathological tissues growth monitoring method according to claim 1, is characterized in that, describedly carries out infiltration degree tracking to described elastic information image, determines that the step of invade tissues regional extent comprises:
Obtain the B-mode image of elastic information image;
B-mode image carries out tracking to the zone boundary of pathological tissues and obtains tissue infiltration's degree border;
Extract the marginal information of B-mode image and be fused on elastic information image, obtaining the invade tissues regional extent on elastic information image.
5. pathological tissues growth monitoring method according to claim 1, is characterized in that, described infiltration growth characteristics parameter comprises: wetted area hardness ratio, elastic characteristic ratio, elasticity average, elasticity variance and elasticity mark.
6. a pathological tissues growth monitoring system, is characterized in that, comprising:
Elastic image acquisition module, for obtaining the inducing shear ripple of diseased tissue area deformation, obtains the elastic image of pathological tissues according to described inducing shear ripple;
Elastic information picture creation module, for carrying out pointwise coupling for the ROI region of described elastic image and pixel in tracing area, builds the elastic information image of ROI region;
Organize soft or hard attribute determination module, for determining the soft or hard attribute of pathological tissues in described elastic information image;
Infiltration degree tracking module, for carrying out infiltration degree tracking to described elastic information image, determines invade tissues regional extent;
Elastic characteristic parameter extraction module, for obtaining the infiltration growth characteristics parameter of pathological tissues according to described elastic information image, soft or hard attribute and invade tissues regional extent;
Pathological tissues growth monitoring module, monitors for the growth of infiltration growth characteristics parameter to described pathological tissues according to described pathological tissues.
7. pathological tissues growth monitoring system according to claim 6, is characterized in that, described elastic image acquisition module is further used for:
The radiant force utilizing focused ultrasound beams to produce obtains breast tumor regional deformation inducing shear ripple; Follow the trail of and shear the propagation in crest face, calculation side, to various point locations on propagation path and the relation curve of shearing wave crest time of advent, calculates shear wave velocity according to described relation curve; Obtain Young's modulus of elasticity according to described shear wave velocity, obtain the high-resolution elastic image of pathological tissues according to described Young's modulus of elasticity.
8. pathological tissues growth monitoring system according to claim 6, is characterized in that, described soft or hard attribute determination module of organizing is further used for:
Calculate the cluster centre of described elastic information image; Wherein, cluster centre comprises territory, hard area cluster centre and soft region clustering center; Calculate the fuzzy similarity matrix of each element on described elastic information image and cluster centre square distance and, obtain the degree of membership that each element belongs to soft or hard interval; According to the soft or hard attribute of degree of membership determination ROI region tissue.
9. pathological tissues growth monitoring system according to claim 6, is characterized in that, described infiltration degree tracking module is further used for:
Obtain the B-mode image of elastic information image; B-mode image carries out tracking to the zone boundary of pathological tissues and obtains tissue infiltration's degree border; Extract the marginal information of B-mode image and be fused on elastic information image, obtaining the invade tissues regional extent on elastic information image.
10. pathological tissues growth monitoring system according to claim 6, is characterized in that, described infiltration growth characteristics parameter comprises: wetted area hardness ratio, elastic characteristic ratio, elasticity average, elasticity variance and elasticity mark.
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