CN102542556A - Method for automatically extracting ultrasonic breast tumor image - Google Patents

Method for automatically extracting ultrasonic breast tumor image Download PDF

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CN102542556A
CN102542556A CN2010106133884A CN201010613388A CN102542556A CN 102542556 A CN102542556 A CN 102542556A CN 2010106133884 A CN2010106133884 A CN 2010106133884A CN 201010613388 A CN201010613388 A CN 201010613388A CN 102542556 A CN102542556 A CN 102542556A
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沈民奋
张琼
郑柏泠
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Shantou University
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Abstract

The invention relates to the field of signal processing of biomedicine, in particular to a method for automatically extracting an ultrasonic breast tumor image. The method comprises the following steps of: 1, selecting a target image, namely marking a square frame by a user through a mouse so as to contain the tumor in the square frame; 2, automatically extracting an edge of the ultrasonic tumor image, namely suppressing noise of a cut image through a speckle reducing anisotropic diffusion (SRAD) algorithm; 3, obtaining an image I1 subjected to noise suppression through the step (2), and automatically segmenting the tumor image by utilizing an improved geometrical active contour model, namely 3a, pre-processing the image, 3b, optimizing the model, 3c, extracting the edge of the image, 3d, updating the model, and 3e, converging the image. A novel energy function is provided to improve the original model, so that the model is more suitable for lesion extraction of medical ultrasonic tumor image, and the accuracy and practicality of the algorithm are improved further.

Description

Ultrasonoscopy tumor of breast extraction method
Technical field
The invention belongs to the processing of biomedical signals field, be specifically related to tumour ultrasonoscopy extraction method.
Background technology
Breast cancer has become women's No.1 killer, and its morbidity number significantly rises with the speed of average annual 3%-5%, and increasingly serious trend is arranged.Research shows that if inspection in time in early days, cancer can be cured, and cure rate is up to more than 92%.It is thus clear that the early detection of tumor of breast is to the crucial effects that healed the sick.Based on the detection technique of ultrasonoscopy be with the fastest developing speed in the medical science, use one of tumor disease detection technique the most widely.Yet because the special mechanism of its imaging, ultrasonoscopy tumor of breast focus inspection problem also is not resolved so far, is one of current main research focus, is a classic problem yet.To this problem, the researchist has proposed a large amount of partitioning algorithms so far both at home and abroad, yet up to the present the shortcoming that these algorithms all exist and are of limited application, limitation is stronger does not also exist a kind of general focus detection method.
In recent years, in the image segmentation field, show actively based on the movable contour model method (like the snake model) at edge, all obtained in many aspects using widely.But they have following shortcoming usually:
(1) relatively more responsive to noise and clutter;
(2) in weak edge the border leakage phenomenon takes place easily;
(3) relatively harsher to the requirement of starting condition.
Chan and Vese are at article " Chan, T. F.; Vese, L. A.; Active contours without edges. Image Processing; IEEE Transactions; Vol.10, pp.266 – 277,2001 " in; proposed a kind of geometric active contour model-Chan-Vese model based on the zone of classics, it has greatly improved the above-mentioned shortcoming of Snake model.In this model, initial profile can be arranged on any position of target area, makes the practicality of model be further strengthened; Yet, for ultrasonoscopy, because it receives the serious interference of speckle noise in imaging process; Therefore, at the tumor boundaries near zone, speckle noise tends to form the bulk of some catastrophe points or sudden change; Thereby, caused the Chan-Vese model when extracting lesion boundary, to be easy to that speckle noise is regarded as the edge and extracted.In addition, the Chan-Vese model is to multiobject image Segmentation, and it is inaccurate to occur the location, edge easily.
Summary of the invention
The present invention seeks to defective, a kind of ultrasonoscopy tumor of breast extraction method is provided to above-mentioned cutting techniques existence.
Order of the present invention can realize not having artificial tumor of breast edge extracting of intervening; Can not only extract the tumor focus zone apace; And improve accuracy greatly; Thereby establish the good technical basis for the differentiation of tumor of breast and computer-aided diagnosis, help advancing the application of Medical Image Processing technology in the tumor of breast clinical diagnosis, the useful information of tumor region is provided for numerous doctors.
The present invention realizes through following technical scheme, specifically may further comprise the steps and carries out.
1. choose target image: the user utilizes mouse to draw and gets a square frame, tumour is included within this square frame, and its purpose one is in order to cut out irrelevant information, to reduce and disturb; The 2nd, in order to improve the real-time of system.
2. tumour ultrasonoscopy edge extracts automatically: adopt SRAD anisotropy broadcast algorithm, carry out squelch to cutting out the image that obtains; The outstanding advantage applies of this algorithm exists: can effectively remove speckle noise and preserving edge information, can strengthen the step-like edge simultaneously; Therefore, spot (Speckle) interference of noise that adopts this algorithm to reduce as much as possible to be caused by ultrasonic imaging mechanism is for subsequent treatment provides good basis; The iterative equation of SRAD algorithm is as follows:
Figure 113794DEST_PATH_IMAGE001
Wherein, Be the image after cutting out;
Figure 161440DEST_PATH_IMAGE003
Be coefficient of diffusion;
Figure 121436DEST_PATH_IMAGE004
The supporting domain of presentation video, For
Figure 704919DEST_PATH_IMAGE006
The border, and
Figure 35538DEST_PATH_IMAGE007
For
Figure 997677DEST_PATH_IMAGE008
Outer normal vector;
is the output image after the iteration each time;
3. obtain through the image after the squelch by step (2) I1 ,Utilize improved geometric active contour model that tumor image is cut apart automatically:
The pre-service of 3a image: the expansion and the caustic solution that utilize geometric shape are to subimage
Figure 786828DEST_PATH_IMAGE010
Carry out pre-service, generate the initial active outline line automatically
Figure 475298DEST_PATH_IMAGE011
, i.e. zero level collection, and generation symbolic distance function S DF, i.e. level set function
Figure 994177DEST_PATH_IMAGE012
,The computing formula that expansion and caustic solution adopt is following:
Figure 177027DEST_PATH_IMAGE013
Wherein
Figure 566475DEST_PATH_IMAGE015
Expression has the structural element of definite shape and size; I1 doesImage after the squelch;
The 3b model optimization: in order to improve the huge problem of calculated amount that the original geometry active contour develops, system has adopted the arrowband algorithm, promptly only considers the near zone of zero level collection, only upgrades the SDF in this narrowband region at every turn, thereby improves the work efficiency of algorithm;
3c Edge extraction: extract problems such as inaccurate to the noise immunity difference of ultrasonoscopy and to multiple goal in order to improve traditional Chan-Vese model; The gradient information of systems incorporate image and area grayscale information propose a new energy term based on gradient:
Figure 988360DEST_PATH_IMAGE016
Figure 270175DEST_PATH_IMAGE017
Figure 343173DEST_PATH_IMAGE018
Figure 124179DEST_PATH_IMAGE019
Where,
Figure 282627DEST_PATH_IMAGE020
for the re-initialization of the level set function;
Figure 368133DEST_PATH_IMAGE021
and
Figure 311949DEST_PATH_IMAGE022
, respectively pixels inside the target area average and average gradient;
Figure 513123DEST_PATH_IMAGE023
and
Figure 204874DEST_PATH_IMAGE024
, respectively pixels outside the target area average and average gradient;
Figure 533218DEST_PATH_IMAGE025
and
Figure 580808DEST_PATH_IMAGE026
for the adjustable parameters; and there:
Figure 264468DEST_PATH_IMAGE027
Figure 882663DEST_PATH_IMAGE028
And
Figure 212068DEST_PATH_IMAGE030
The 3d model modification: when zero level collection curve near or when touching the border, arrowband, according to the evolution formula, upgrade level set function automatically, and recomputate new narrowband region;
Convergence of 3e model and criterion: when whether the inspection iteration restrained, if convergence does not then forward step 3b to, otherwise then calculating stopped, and zero level collection curve stops to develop, and the Rule of judgment of getting iteration convergence is:
Figure 505777DEST_PATH_IMAGE031
Wherein,
Figure 126114DEST_PATH_IMAGE032
is the level set function that the n time iteration obtains;
Figure 91534DEST_PATH_IMAGE033
is time step, and
Figure 864449DEST_PATH_IMAGE034
grid sum that satisfied in expression.
Advantage of the invention and effect
Advantage of the invention and effect
The present invention compared with prior art has the following advantages:
1, the present invention has adopted advanced geometric shape method auto-initiation level set function, can not only avoid artificial participation effectively, has improved the automatization level of this system.
2, the present invention utilizes the arrowband method to improve original geometric active contour model, has greatly reduced calculated amount, has improved the real-time performance of system.
3, the present invention proposes a new energy function master pattern is improved, make that this model is more suitable for extracting in the focus of medical ultrasonic image tumour, further improved algorithm accuracy and practicality.
Accompanying drawing and explanation thereof
Fig. 1 is a process flow diagram of the present invention.
Fig. 2 is the present invention and the contrast and experiment figure of original geometry movable contour model algorithm on the noisy image of multiple goal;
Wherein, 2-a is former figure; 2-b is the experimental result of Li; 2-c is an experimental result of the present invention.
Fig. 3 is directed against the comparative test result figure of ultrasonoscopy tumor of breast extraction for the present invention and original geometry movable contour model algorithm;
Wherein, 3-a is former figure; 3-b is the experimental result of Li; 3-c is an experimental result of the present invention.
Fig. 4-a is the ultrasonoscopy that contains the stringiness tumor of breast that diagnostic ultrasonic equipment collects, the region that the tumour that the white rectangle square frame is got for the user draws is general; Fig. 4-b gets the subimage that the back forms for drawing; Fig. 4-c is the image through obtaining after the SRAD squelch, and by the automatic zero level collection curve that generates of this image; Fig. 4-d carries out in the automatic focus extraction the automatic evolutionary process of zero level collection curve for the improvement Chan-Vese model algorithm that adopts the present invention to propose; Fig. 4-e is that zero level collection curve evolvement finally stops, and the contour curve that obtains promptly extracts and obtained the tumor of breast edge; The final extraction that Fig. 4-f obtains for this diagnostic ultrasonic equipment utilization the present invention is displayed map as a result.
The present invention brings forward a new energy function, has realized the effective improvement to original Chan-Vese model, makes that the Chan-Vese model is more suitable for extracting in the focus of medical ultrasonic image tumour.Compare through simulation study and experiment, the present invention all obviously is superior to the Chan-Vese model on extraction performance and time loss, concrete visible lab diagram 2 and Fig. 3.Wherein, Fig. 2 is the present invention and the contrast and experiment figure of Chan-Vese model algorithm on the noisy image of multiple goal; Fig. 3 is directed against the comparative test result figure of ultrasonoscopy tumor of breast extraction for the present invention and Chan-Vese model algorithm.In addition, the present invention need not manual intervention in the leaching process of tumor focus, and automatization level is higher.Fig. 4 is loaded on the diagnostic ultrasonic equipment for the present invention, carries out the instance graph of clinical assistant diagnosis.In the selection of this instance parameter;
Figure 731965DEST_PATH_IMAGE036
;
Figure 940224DEST_PATH_IMAGE037
;
Figure 128498DEST_PATH_IMAGE038
=
Figure 13277DEST_PATH_IMAGE039
=0.4, ; The SRAD iterations is set to 5 times, and the iteration stopping number of times of algorithm is 50 times.
Concrete embodiment:
The present invention is primarily aimed at that extraction and the specialized designs of ultrasonoscopy tumor of breast implement.On the basis of the characteristics of fully having studied ultrasonic imaging mechanism and ultrasonoscopy; The present invention has used spot noise reduction anisotropy diffusion (SRAD) algorithm ultrasonoscopy has been carried out necessity ground noise remove, and has kept the information at tumor region edge effectively and strengthened edge contour; Following closely be, adopt improved geometric active contour model (Chan-Vese model) that the image after the denoising is handled, extract the focus zone of tumor of breast.Obviously, the entire process process need not manual intervention, and the present invention can automatically extract and obtain final result.
With reference to Fig. 1, the present invention is based on the medical ultrasonic image tumour extraction that improves movable contour model and comprise:
Step 1: cut out, obtain area-of-interest.
The present invention only needs the user probably to confirm the position of tumour, promptly can utilize mouse on the medical image that shows, to draw the approximate range of getting tumour and get final product, and system just can automatically propose important informations such as concrete shape and the size of tumour.
Step 2: squelch, it is readable to improve ultrasonoscopy, is convenient to subsequent treatment.
The speckle noise of ultrasonoscopy has reduced ultrasonic image quality widely, has had a strong impact on the subsequent treatment of ultrasonoscopy, especially to the extraction and the identification of tumor focus.The present invention introduces SRAD anisotropy broadcast algorithm, can effectively remove speckle noise and preserving edge information, can strengthen the step-like edge simultaneously.Its iterative equation is as follows:
Figure 354577DEST_PATH_IMAGE041
Wherein coefficient of diffusion c (q) is:
Figure 397357DEST_PATH_IMAGE042
In the formula, q is the instantaneous coefficient operator of being calculated by local variance; Q0 (t) is the spot scale coefficient, is used to control level and smooth degree.Experiment shows that q0 (t) gets [0,1], and effect is good.Here, get q0 (t)=0.4.Instantaneous coefficient operator q is defined as:
Figure 203770DEST_PATH_IMAGE043
This operator has comprised gradient operator and Laplace operator, is used for detecting the edge of spot image.Obtain higher value with high-contrast profile place on the edge of, and obtain smaller value at homogeneous area.
Step 3: system extracts borderline tumor automatically, need not man-machine interactively.
If the image that obtains behind the noise suppression does I1, next, utilize improved geometric active contour model, realize that the edge of tumor of breast extracts automatically:
1) utilize expansion and corrosion in the geometric shape that the image I after the denoising is handled; Automatically generate initial active outline line
Figure 20416DEST_PATH_IMAGE011
; And then; Can calculate the symbolic distance function, just level set function
Figure 396909DEST_PATH_IMAGE012
.It is following with the definition of erosion operation to expand:
Figure 733343DEST_PATH_IMAGE013
Figure 959925DEST_PATH_IMAGE014
Wherein expression has the structural element of definite shape and size.
2) for improving real-time, system has adopted the arrowband algorithm that model is optimized, and only considers that promptly all pixels are that 4 pixels are with interior zone to zero level collection curve distance; In iterative computation after this, only upgrade the SDF in this narrowband region at every turn, thereby improve the work efficiency of algorithm.
3) native system makes full use of the gradient information and the area grayscale information of image, proposes a more efficiently energy function:
Figure 929204DEST_PATH_IMAGE016
Figure 369413DEST_PATH_IMAGE044
Figure 281743DEST_PATH_IMAGE045
Where,
Figure 27162DEST_PATH_IMAGE047
and
Figure 571145DEST_PATH_IMAGE048
respectively pixels inside the target area average and average gradient; and , respectively pixels outside the target area average and the average gradient. is illustrated in the Grad that point
Figure 653053DEST_PATH_IMAGE052
is located,
Figure 657918DEST_PATH_IMAGE020
be the level set function after reinitializing.
Utilize the variational method to this total energy function minimization, obtain level set function evolutionary process and be:
Figure 804937DEST_PATH_IMAGE053
Figure 74244DEST_PATH_IMAGE054
Wherein, Symbol is represented gradient, and symbol
Figure 299875DEST_PATH_IMAGE056
is expressed as Laplace operator.
For the numerical solution of level set function EVOLUTION EQUATION, adopt the implicit iterative solution.Can prove for the above-mentioned time-based level set function that obtains by the variation minimization
Figure 225161DEST_PATH_IMAGE020
EVOLUTION EQUATION; Its implied format iterative solution method is unconditional stability; Therefore; Time step
Figure 119299DEST_PATH_IMAGE033
can suitably strengthen; With the evolution of acceleration curve, the present invention gets
Figure 594142DEST_PATH_IMAGE040
.
4) according to the evolution formula, when zero level collection curve near or when touching the border, arrowband, system will upgrade level set function automatically and recomputate new narrowband region.
When 5) whether the inspection iteration restrains, if convergence does not then forward step 2 to), otherwise then calculating stops, zero level collection curve stops to develop, and at this moment, this curve will drop on the edge of tumour exactly, thereby obtain the extraction result of final tumour profile.For fear of unnecessary iterative computation, the Rule of judgment of getting iteration convergence is:
Figure 933726DEST_PATH_IMAGE031
）
Wherein, is the level set function that the n time iteration obtains, and
Figure 508243DEST_PATH_IMAGE034
grid sum that
Figure 454072DEST_PATH_IMAGE035
satisfied in expression.
Below verify the validity and the practicality of the inventive method through emulation experiment and concrete clinical practice.The method that is compared is the method that people such as Li propose, concrete list of references " T. F. Chan, L. A. Vese; Active contours without edges, IEEE Trans. Image Processing, 2001; vol.10, no.2, pp.266-277. "
Fig. 2 and Fig. 3 are respectively noisy multiple goal composograph and medical ultrasonic tumor of breast image, and the comparative result of experiment.As can be seen from the figure, the present invention can correctly split target, compares the scheduling algorithm with Li, has higher accuracy rate, and has good real time performance, and is as shown in table 1.
Two kinds of methods of table 1 are cut apart the comparison of required time.
Fig. 2 Fig. 3
The Li method 11.54 12.33
The present invention 0.97 1.25
Fig. 4 is loaded on the diagnostic ultrasonic equipment for the present invention, carries out the instance graph of clinical assistant diagnosis.The result of clinical testing shows that the present invention has very high practicality, and the important information of tumour can be provided for the clinician practically.

Claims (1)

1. tumor of breast ultrasonoscopy extraction method, carry out according to the following steps:
(1) choose target image: the user utilizes mouse to cut out to include the block scheme picture of pending tumor of breast;
(2) tumour ultrasonoscopy edge extracts automatically: adopt SRAD anisotropy broadcast algorithm, carry out squelch to cutting out the image that obtains, the iterative equation of SRAD algorithm is following:
Figure 503706DEST_PATH_IMAGE001
Wherein,
Figure 978549DEST_PATH_IMAGE002
Be the image after cutting out;
Figure 52553DEST_PATH_IMAGE003
Be coefficient of diffusion;
Figure 109502DEST_PATH_IMAGE004
The supporting domain of presentation video,
Figure 627071DEST_PATH_IMAGE005
For
Figure 900796DEST_PATH_IMAGE006
The border, and
Figure 279955DEST_PATH_IMAGE007
For
Figure 689946DEST_PATH_IMAGE008
Outer normal vector;
Figure 378416DEST_PATH_IMAGE009
is the output image after the iteration each time;
(3) obtain through the image after the squelch by step (2) I1 ,Utilize improved geometric active contour model that tumor image is cut apart automatically:
The pre-service of 3a image: the expansion and the caustic solution that utilize geometric shape are to subimage
Figure 640901DEST_PATH_IMAGE010
Carry out pre-service, generate the initial active outline line automatically
Figure 56708DEST_PATH_IMAGE011
, i.e. zero level collection, and generation symbolic distance function S DF, i.e. level set function
Figure 275200DEST_PATH_IMAGE012
,The computing formula that expansion and caustic solution adopt is following:
Figure 947621DEST_PATH_IMAGE013
Figure 868041DEST_PATH_IMAGE014
Wherein
Figure 103850DEST_PATH_IMAGE015
Expression has the structural element of definite shape and size; I1 doesImage after the squelch;
3b model optimization: adopted the arrowband algorithm, promptly only considered the near zone of zero level collection, upgraded the SDF in this narrowband region again, thereby improved the work efficiency of algorithm;
3c Edge extraction: the gradient information of combining image and area grayscale information; Adopt minimization of energy function to calculate, make zero level collection curve develop along the borderline tumor direction:
Figure 269438DEST_PATH_IMAGE017
Figure 686961DEST_PATH_IMAGE019
Figure 880045DEST_PATH_IMAGE020
Where,
Figure 392804DEST_PATH_IMAGE021
for the re-initialization of the level set function; and
Figure 101314DEST_PATH_IMAGE023
, respectively pixels inside the target area average and average gradient;
Figure 398172DEST_PATH_IMAGE024
and
Figure 583297DEST_PATH_IMAGE025
are outside of the target area of the pixel average and the average gradient,
Figure 716338DEST_PATH_IMAGE026
and
Figure 81329DEST_PATH_IMAGE027
for the adjustable parameters; and there:
Figure 796475DEST_PATH_IMAGE028
Figure 339452DEST_PATH_IMAGE029
Figure 943478DEST_PATH_IMAGE030
And
Figure 862892DEST_PATH_IMAGE031
The 3d model modification: when zero level collection curve near or when touching the border, arrowband, according to the evolution formula, upgrade level set function automatically, and recomputate new narrowband region;
Convergence of 3e model and criterion :When whether the inspection iteration restrained, if convergence does not then forward step 3b to, otherwise then calculating stopped, and zero level collection curve stops to develop, and the Rule of judgment of getting iteration convergence is:
）
Wherein,
Figure 419128DEST_PATH_IMAGE033
is the level set function that the n time iteration obtains;
Figure 339811DEST_PATH_IMAGE034
is time step, and
Figure 797337DEST_PATH_IMAGE035
grid sum that
Figure 657714DEST_PATH_IMAGE036
satisfied in expression.
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