CN106408566B

CN106408566B - A kind of fetal ultrasound image quality control method and system

Info

Publication number: CN106408566B
Application number: CN201610991842.7A
Authority: CN
Inventors: 倪东; 吴凌云; 李胜利; 郑介志; 汪天富
Original assignee: Shenzhen University
Current assignee: SHENZHEN WISONIC MEDICAL TECHNOLOGY Co.,Ltd.
Priority date: 2016-11-10
Filing date: 2016-11-10
Publication date: 2019-09-10
Anticipated expiration: 2036-11-10
Also published as: CN106408566A

Abstract

Fetal ultrasound image quality control method provided by the invention and system, the Fetus Abdominal scanning section gray level image obtained by input clinical ultrasound doctor, it extracts its local phase feature and obtains corresponding Local Symmetric phase and asymmetric phase diagram, RGB image is combined into together with former gray level image, utilize L-CNN model orientation to ROI, and judge whether the ROI in section meets the 1st index, then C-CNN model analyzes the ROI recognized, and judge whether SB meets the 2nd index, whether UV meets the 3rd standard, to realize the control of fetal ultrasound picture quality, fetal ultrasound image quality control method provided by the present application and system, it is controlled by the quantification quality to fetal ultrasound scanning section Automation grade point, the purpose of objective evaluation section quality may be implemented , more more economical than expert's manual operation effective.

Description

A kind of fetal ultrasound image quality control method and system

Technical field

The present invention relates to medical image technical fields, more particularly, to a kind of fetal ultrasound image quality control method and are System.

Background technique

The advantages that ultrasound is because of acquisition, at low cost, radiationless and Mobile portable in real time, is widely used in antenatal exaination.It is logical Often, the process of Prenatal ultrasonography for diagnosing includes: ultrasonic probe scanning, standard section obtains, biometric measures, gestational age weight is surveyed It is fixed.Biometric measurement is necessary to gestational age body weight determination, and Seeking Truth carries out on standard section again for biometric measurement, This just illustrates that only the measured value of standard section can accurately just estimate gestational age and weight, it is seen that the importance of standard section.

But not all doctor can obtain standard section.If Fig. 1 (a) is that the fetal abdominal circumference met the quality standard is cut Face, the length that grey coil measures abdominal circumference (AC) is 338.72mm；The fetus of same gestational age, it is not abundant enough to might have experience Doctor obtains the fetal abdominal circumference section that quality standard is not met shown in Fig. 1 (b), and the length that grey coil measures AC is 326.87mm；Two images only difference is that arrow meaning magenblase (SB), the full display of (a) magenblase and interior echoless, (b) there are 11.85mm for the abdominal circumference length that the not good enough display of magenblase, the abdominal circumference length for causing not good enough section to measure and standard section measure Error.

Existing research highlights the importance of the quality control of fetus biometric measurement in clinic.Generally by experienced Doctor (expert) scores to ultrasonic scan section according to agreement, realizes the purpose of quality control, but scored manually by expert Quality control has disadvantages that: be 1. more likely to the subjectivity of expert 2. heavy dependence expert experience 3. repetitive operation work, It takes time and effort, can not be realized in clinic.

Summary of the invention

Have in view of that, it is necessary to for defect existing for present technology, provide a kind of fetal ultrasound picture quality controlling party Method and system.

To achieve the above object, the present invention adopts the following technical solutions:

A kind of fetal ultrasound image quality control method, includes the following steps:

Step S110: the gray level image of Fetus Abdominal scanning section is obtained；

Step S120: extracting the local phase feature of the gray level image, obtains corresponding Local Symmetric phase diagram drawn game The asymmetric phase diagram in portion；

Step S130: the symmetrical phase figure and asymmetric phase diagram and the gray level image are combined into RGB image；

Step S140: using L-CNN model orientation to the ROI in the RGB image, and judge whether ROI meets the 1st Index；

Step S150: analyzing the ROI recognized using C-CNN model, and judges whether SB meets the 2nd and refer to Whether mark, UV meet the 3rd standard；

Step S160: fetal ultrasound image quality measurement knot is obtained according to the judging result in step S140 and step S150 Fruit；

Wherein, the 1st index is that the area of ROI accounts for 1/2 or more of scanning sector, and the 2nd index is filled for SB It is full of display, sharpness of border, interior echoless, the 3rd index is that UV is echo in hook solid shows, do not interrupt continuously, allowing.

In some embodiments, in step S140, using L-CNN model orientation to the ROI in the RGB image, and sentence Whether disconnected ROI meets the 1st index, specifically include the following steps:

Step S141: the subgraph of each fetal abdominal circumference section is taken off；

Step S142: adjusting the size of the subgraph to 227*227, and input L-CNN, and obtaining window is the general of ROI Rate；

Step S143: the above-mentioned window probability obtained in whole section centered on each pixel according to this is used, ROI is obtained Corresponding probability graph；

Step S144: the probability graph is smooth by two-sided filter, probability is obtained most using non-maxima suppression High pixel is the best center point coordinate C that ROI is navigated to fixed size window_x,y；

Step S145: the ratio R that ROI area accounts for ultrasonic scan sector area is calculated_r/FOVObtain S_ROI, judge whether ROI accords with Close the 1st index.

In some embodiments, in step S141, the subgraph of each fetal abdominal circumference section is taken off, using fixed dimension The window of 320*280 takes off subgraph in ultrasonic scan region with the sliding of stride 10.

In some embodiments, the step S144 further includes following step:

With best center point coordinate C_x,yCentered on, and its be with stride in the regional scope of 10 pixels up and down 10 sliding windows obtain central point, width, the height of highest ROI probability, complete the automatic positioning of ROI in current test image, In, for the width of the window in the range of 210-400, window height is in the range of 190-340.

In some embodiments, wherein in step S160, obtained according to the judging result in step S140 and step S150 Fetal ultrasound picture quality control result, specifically include the following steps:

If ROI meets the 1st index, to the scoring S of ROI_ROIIt is 1, if it is not, being denoted as 0；

If SB meets the 2nd index, to the scoring S of SB_SBIt is 1, if it is not, being denoted as 0；

If UV meets the 3rd standard, to the scoring S of UV_UVIt is 1, if it is not, being denoted as 0；

The then assessment score S of fetal ultrasound picture quality_FAPFor S_FAP=S_ROI+S_SB+S_UV。

In some embodiments, wherein the width of the window is interval variation, window with 10 in the range of 210-400 Open height is interval variation with 10 in the range of 190-340.

In addition, present invention also provides a kind of fetal ultrasound picture quality controling systems, comprising:

Image collection module, for obtaining the gray level image of Fetus Abdominal scanning section；

Characteristic extracting module obtains corresponding Local Symmetric phase for extracting the local phase feature of the gray level image Bitmap and local asymmetry phase diagram；

Image co-registration module, for the symmetrical phase figure and asymmetric phase diagram to be combined into the gray level image RGB image；

L-CNN model orientation module for utilizing the ROI in RGB image described in L-CNN model orientation, and judges that ROI is It is no to meet the 1st index；

C-CNN model analysis module analyzes the ROI recognized using C-CNN model, and judges whether SB meets Whether the 2nd index, UV meet the 3rd standard；And

As a result output module, for according to the judgement in the L-CNN model orientation module and C-CNN model analysis module As a result fetai ultrasonogram image quality measurements are obtained；

In some embodiments, the L-CNN model orientation module includes:

Subgraph takes off unit, for taking off the subgraph of each fetal abdominal circumference section；

Image control unit for adjusting the size of the subgraph to 227*227, and inputs L-CNN, and obtaining window is The probability of ROI；

Window probability acquiring unit, for using the above-mentioned window obtained in whole section centered on each pixel according to this Probability obtains the corresponding probability graph of ROI；

Center point coordinate positioning unit, for the probability graph is smooth by two-sided filter, using non-maximum Inhibit to obtain the highest pixel of probability to be the best center point coordinate C for navigating to ROI with fixed size window_x,y；And

Judging unit accounts for the ratio R of ultrasonic scan sector area for calculating ROI area_r/FOVObtain S_ROI, judge that ROI is It is no to meet the 1st index.

In some embodiments, the center point coordinate positioning unit is also used to best center point coordinate C_x,yCentered on, And its up and down in the regional scope of 10 pixels, with stride for 10 sliding windows, the center of highest ROI probability is obtained Point, width, height complete the automatic positioning of ROI in current test image, wherein range of the width of the window in 210-400 Interior, window height is in the range of 190-340.

In some embodiments, the result output module is used for according to the L-CNN model orientation module and C-CNN mould Judging result in type analysis module obtains fetai ultrasonogram image quality measurements, comprising: if ROI meets the 1st index, To the scoring S of ROI_ROIIt is 1, if it is not, being denoted as 0；

The present invention by adopting the above technical scheme the advantages of be:

Fetal ultrasound image quality control method provided by the invention and system are obtained by input clinical ultrasound doctor Fetus Abdominal scanning section gray level image extracts its local phase feature and obtains corresponding Local Symmetric phase and asymmetric phase Figure, is combined into RGB image together with former gray level image, is navigated to ROI in the way of multi-resolution scanning by L-CNN model, And judge whether the ROI in section meets the 1st index, then C-CNN model analyzes the ROI recognized, and judges Whether SB meets the 2nd index, and whether UV meets the 3rd standard, wherein the area that the 1st index is ROI accounts for scanning fan 1/2 or more of area, the 2nd index are that SB fills display, sharpness of border, interior echoless, and it is curved that the 3rd index, which is UV, It is hook-shaped to show, do not interrupt continuously, allowing interior echo, to realize the control of fetal ultrasound picture quality, tire provided by the present application Youngster's ultrasonograph quality control method and system pass through the quantification quality control to fetal ultrasound scanning section Automation grade point System, may be implemented the purpose of objective evaluation section quality, more more economical than expert's manual operation, efficient；Secondly, can be used in It is easy to use in tele-medicine；Further more, can be used for the training and study of new hand doctor, by assessing the section of their acquisitions, The ability that they obtain standard section is continuously improved.

Detailed description of the invention

What (a) was indicated in Fig. 1 is the fetal abdominal circumference section met the quality standard；

What (b) was indicated in Fig. 1 is the fetal abdominal circumference section for not meeting quality standard；

Fig. 2 is the step flow chart of fetal ultrasound image quality control method provided in an embodiment of the present invention；

(a) Fetus Abdominal standard section in Fig. 3, (b) and (c) indicates bright symmetrical and asymmetric phase property, (d) with (e) dark symmetrical and asymmetric phase property is indicated；

Fig. 4 provides for the application using the ROI in RGB image described in L-CNN model orientation, and judges whether ROI meets The step flow chart of 1st index；

Fig. 5 is expressed as four kinds of classifications of ROI；

Fig. 6 is expressed as the structural schematic diagram of fetal ultrasound picture quality controling system provided by the present application；

Fig. 7 indicates the structural schematic diagram of the L-CNN model orientation module；

Fig. 8 is FUIQA system provided by the present application and expert E1, E2, and the consistent Fetus Abdominal ultrasound of E3 quality evaluation is swept Look into section display diagram.

Fig. 9 illustrates FUIQA system and expert E1, E2, the inconsistent Fetus Abdominal ultrasonic scan section of E3 assessment result Schematic diagram.

Specific embodiment

In order to make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing and specific implementation Example, the present invention will be described in further detail.It should be appreciated that specific embodiment described herein is only to explain this hair It is bright, it is not intended to limit the present invention.

In application documents, relational terms such as first and second and the like are used merely to an entity or operation It is distinguished with another entity or operation, without necessarily requiring or implying between these entities or operation, there are any this Actual relationship or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to nonexcludability It include so that the process, method, article or equipment for including a series of elements not only includes those elements, but also to wrap Include other elements that are not explicitly listed, or further include for this process, method, article or equipment intrinsic want Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including described want There is also other identical elements in the process, method, article or equipment of element.

Referring to Fig. 2, a kind of fetal ultrasound image quality control method provided in an embodiment of the present invention, including following steps It is rapid:

In the present embodiment, input picture provided by the present application is the Fetus Abdominal scanning section that clinical ultrasound doctor obtains Gray level image, it will be understood that in practical applications, can be extended to other ultrasonic scan sections of fetus.

Referring to Fig. 3, the bright and dark phase for illustrating the extraction of a Fetus Abdominal standard section is symmetrical with asymmetric spy Sign figure, wherein (a) Fetus Abdominal standard section in Fig. 3, (b) and (c) indicates bright symmetrical and asymmetric phase property, (d) with (e) dark symmetrical and asymmetric phase property is indicated.

It is appreciated that natural image is the color image in tri- channels RGB, and ultrasound image is single pass grayscale image Picture, in order to analyze ultrasound image using DCNN model, it will usually which replicating single-channel data to increase is 3 channels, and Local Phase Position feature is effectively applied to many ultrasound images processing problems, and in order to increase gray level image information, we are by Local Symmetric Optional channel with asymmetric phase property as original image inputs, and is combined into RGB image with the gray level image.

Step S140: using the ROI (abdomen area) in RGB image described in L-CNN model orientation, and whether judge ROI Meet the 1st index；Wherein, the 1st index is that the area of ROI accounts for 1/2 or more of scanning sector；

Table 1 is please referred to, for the fetal abdominal circumference section quality control protocol executed in the application.

Index	Anatomical structure	Standard
			1st index	Abdomen area (ROI)	Area accounts for 1/2 or more of scanning sector
2nd index	Magenblase (SB)	Full display, sharpness of border, interior echoless
			3rd index	Umbilical vein (UV)	Hook solid shows, does not interrupt continuously, allowing interior echo

Depth convolutional network (DCNN) model is a kind of depth learning technology of hot topic, has been successfully applied to medical image The various problems of analysis.Typical DCNN structure is by several groups of convolutional layers, maximum value pond layer and the last full articulamentum group closely followed At.Convolutional layer extracts feature using upper one layer of local receptor field as input, and retains global spatial structural form, this Shen The L-CNN that please be proposed is based on the basis of DCNN.

Table 1 is please referred to, is L-CNN model structure.In the present embodiment, for preferably transfer learning natural image data Trained parameter, we by the convolutional layer of L-CNN network and the structure setting of pond layer at as AlexNet model, and Subsequent 3 full articulamentums are finely tuned on ultrasonic training data, it is single that this 3 full articulamentums separately include 1024,256,2 nerves Member, that is to say, that during L-CNN training, the parameter of convolutional layer is as at the beginning of trained AlexNet model parameter Beginningization, and then random initializtion is Gaussian Profile to the parameter of full articulamentum.Meanwhile in order to improve generalization ability, dropout plan Slightly and ReLu is used for training process.Learning rate is initially 0.001, and is gradually reduced 10 times when network stops and restraining.

The structure of 1 L-CNN model of table

Referring to Fig. 4, it is provided by the present application, in step S140, using in RGB image described in L-CNN model orientation ROI, and judge whether ROI meets the 1st index, specifically include the following steps:

Preferably for each fetal abdominal circumference section, in the region of ultrasonic scan, with fixed dimension 320*280's Window takes off subgraph with the sliding of stride 10.It is appreciated that can also be using other windows for different fetal abdominal circumference sections Mouth and stride take off subgraph.

Further, the step S144 further includes following step:

It is appreciated that S141~step S145 can complete to scheme using RGB described in L-CNN model orientation through the above steps ROI as in, and judge whether ROI meets the 1st index.

Step S150: analyzing the ROI recognized using C-CNN model, and judges whether SB meets the 2nd and refer to Whether mark, UV meet the 3rd standard, and the 2nd index is that SB fills display, sharpness of border, interior echoless, and described 3rd Index is that UV is echo in hook solid shows, do not interrupt continuously, allowing.

It is appreciated that the purpose of C-CNN model is to realize whether two important anatomy structures of SB and UV meet matter inside ROI The assessment of amount standard.Clinically, be anatomical structure is divided into standard, it is not good enough, there is no three kinds of judge forms, it is not good enough to represent the solution Cut open structure exist but it is nonstandard.In fact, the section of only standard state is just recognized for biometric measurement, it is not good enough There are conspicuousness errors in biometric measurement with standard section for the section of state.

It is appreciated that in this application, only anatomical structure standard when, the scoring of anatomical structure is just 1 in corresponding table 1； It is not good enough be not present in the case where, the scoring of the anatomical structure is 0.Meanwhile whether we meet the feelings of standard according to SB and UV All ROI are divided into 4 kinds of classifications by condition, and the quality evaluation problem of C-CNN is transformed into 4 classification problems, as shown in figure 5, indicating For four kinds of classifications of ROI.

It is assessed it is appreciated that the 2nd and the 3rd in table 1 is integrated and is transformed into the modes of 4 classification, is to utilize C- CNN learns the feature of abdomen area entirety, and is more than and learns single anatomical structure.Simultaneously as three indexs are phases in table 1 Mutual correlation, L-CNN model parameter transfer learning is assisted the classification of 4 classifications by we to C-CNN model.The instruction of C-CNN Practice details and structure is similar to L-CNN, in addition to the neural unit number of last 3 full articulamentums is respectively 2048,256,4.C- After CNN model training is completed, it is input to C-CNN after the ROI being automatically positioned of L-CNN in test image is taken off, obtains Fig. 5 The shown other probability of 4 type, maximum probability is classification belonging to the ROI, completes the 2nd and the 3rd index in table 1 oneself Dynamic assessment.

It is appreciated that according to table 1, the 1st is the area ratio for assessing abdomen area, thus by L-CNN precise positioning to After ROI, the ratio R that ROI area accounts for ultrasonic scan sector area is calculated_r/FOVObtain S_ROI, see formula (1).It is later that ROI is defeated Enter C-CNN, obtain the classification of ROI, according to the corresponding scoring for respectively obtaining UV and SB of Fig. 5, i.e. S_SBAnd S_UV.And then obtain one The quality evaluation score S of ultrasonic scan section_FAP, see formula (2).

S_FAP=S_ROI+S_SB+S_UV (2)

Although frame has general it is appreciated that we are currently only used for the quality of assessment Fetus Abdominal scanning section Property, the quality evaluation of other sections of fetal ultrasound scanning can also be extended to.

Referring to Fig. 6, fetal ultrasound picture quality controling system provided by the present application, comprising: image collection module 110, For obtaining the gray level image of Fetus Abdominal scanning section；

Characteristic extracting module 120 obtains corresponding Local Symmetric for extracting the local phase feature of the gray level image Phase diagram and local asymmetry phase diagram；Image co-registration module 130, for by the symmetrical phase figure and asymmetric phase diagram with The gray level image is combined into RGB image；L-CNN model orientation module 140, for being schemed using RGB described in L-CNN model orientation ROI as in, and judge whether ROI meets the 1st index；C-CNN model analysis module 150, using C-CNN model to identification To ROI analyzed, and judge whether SB meets the 2nd index, whether UV meets the 3rd standard；And result output module 160, for obtaining fetal ultrasound according to the judging result in the L-CNN model orientation module and C-CNN model analysis module Image quality measurement result；Wherein, the 1st index is that the area of ROI accounts for 1/2 or more of scanning sector, and described 2nd refers to It is designated as SB and fills display, sharpness of border, interior echoless, the 3rd index is that UV is that hook solid shows, continuously do not interrupt, permits Perhaps interior echo.

Wherein, referring to Fig. 7, the L-CNN model orientation module 140 includes: that subgraph takes off unit 141, for detaining Take the subgraph of each fetal abdominal circumference section；Image control unit 142 is used to adjust the size of the subgraph to 227* 227, and L-CNN is inputted, obtain the probability that window is ROI；Window probability acquiring unit 143 be used for using it is above-mentioned obtain according to this it is whole The window probability in section centered on each pixel is opened, the corresponding probability graph of ROI is obtained；Center point coordinate positioning unit 144 For the probability graph is smooth by two-sided filter, obtaining the highest pixel of probability using non-maxima suppression is The best center point coordinate C of ROI is navigated to fixed size window_x,y；And judging unit 145 accounts for ultrasound for calculating ROI area The ratio R of scanning sector area_r/FOVObtain S_ROI, judge whether ROI meets the 1st index.

Preferably, the center point coordinate positioning unit 144 is also used to best center point coordinate C_x,yCentered on, and its Up and down in the regional scope of 10 pixels, with stride for 10 sliding windows, obtain the central point of highest ROI probability, width, Height completes the automatic positioning of ROI in current test image, wherein the width of the window is in the range of 210-400, window Open height is in the range of 190-340.

Preferably, the result output module 160 is used for according to the L-CNN model orientation module and C-CNN model point Analysis module in judging result obtain fetai ultrasonogram image quality measurements, if including ROI meet the 1st index, to ROI Scoring S_ROIIt is 1, if it is not, being denoted as 0；

Technical solution provided by the invention is described in detail below in conjunction with specific embodiment.

We study all Fetus Abdominal ultrasonic scans section used and both are from Shenzhen healthcare hospital for women & children, acquisition time From in September, 2012 in November, 2013.According to the antenatal exaination agreement of standard, ultrasound cross-section ties up ultrasonic spy by traditional portable 2 Head obtains pregnant woman's scanning in 16-40 weeks pregnant age.The instrument of all acquiring ultrasound images is Siemens Acuson Sequoia 512 ultrasound scanners, but include different parameter settings.In our study, 492 sets of ultrasonic scans of acquisition in 2012 Video is used as training dataset, and 219 sets of ultrasonic scan videos of acquisition in 2013 are used as test data set.

We concentrate the training for being extracted 8072 scanning sections for L-CNN model from 492 sets of training datas, and each Section take off its abdomen area generate a ROI positive sample, by section with the Duplication of positive sample in 40% work below For negative sample, negative sample is taken off immediately in background area.Training of the positive sample of L-CNN model training as C-CNN model Sample, corresponding classification 1,2,3,4 have 1256,3827,151,2838 samples respectively, for the balance of 4 class sample datas, we Angular range inward turning by the sample of classification 1,3,4 in (- 25,25), which transfers, increases its sample size.Therefore, final C-CNN model 4 trained classification sample sizes are respectively 3717,3827,3907,3964, and this 15415 samples are all used as L- in total The positive sample of CNN model training.All training datas are marked has the Ultrasonography postgraduate of 2 years rich experiences complete by one At, while by the examination of our clinical guidance committee and revising the accuracy to ensure data.In training L-CNN and C-CNN During model, training sample is also divided into training set according to the ratio of 9:1 for we and verifying collects, and detailed details is shown in Table 3。

Table 3.L-CNN and C-CNN training sample number is shown

Ultrasonic scan section for control (FUIQA) system testing of fetal ultrasound picture quality has 2606, comes from 66 219 sets of ultrasonic scan videos of a pregnant woman's acquisition.According to the criterion in table 1, each ultrasound cross-section is all by 3 working experiences 3 Year or more Ultrasonography attending physician mark the position of ROI region, and judged to whether UV and SB complies with standard.This Shen Please in use E1 respectively, E2, E3 represent 3 as doctor, and 3 doctors are complete independently labels.

By FUIQA system, in ROI, SB and UV, individually the label of each assessment result respectively with 3 experts is opposed for we Than taking accuracy rate (Acc), sensibility (Sen) and specificity (Spec) quantitatively to show the performance of L-CNN and C-CNN, such as table respectively Shown in 4.

Table 4. is directed to single anatomical structure ROI, SB, UV, and quantitative pair of FUIQA system and 3 expert E1, E2, E3 scoring Than

Find out from above-mentioned experimental section, the performance of the FUIQA system of proposition provided by the present application, Fig. 8, which is also shown, to be based on The FUIQA system of DCNN different parameters is arranged the robustness of image, meanwhile, by (b) in Fig. 8, (d) can be seen that with (h) FUIQA system also can correctly control picture quality in the case where ultrasound image is seriously affected by sound shadow.Diversified trained number According to study, the FUIQA system based on DCNN can be considered different images parameter setting, the variation of fetus position, sound shadow and A variety of situations such as the influence of noise, relatively traditional Image Processing and Pattern Recognition method occupy very big advantage.

Fig. 9 illustrates FUIQA system and expert E1, E2, the inconsistent Fetus Abdominal ultrasonic scan section of E3 assessment result. Wherein, the position ROI that the automatic positioning of our FUIQA systems and expert mark in (a) figure in Fig. 9 is very close, but obtains not Consistent score.The R of E1, E2, E3 and FUIQA system_r/FOVRespectively 0.497,0.513,0.476 and 0.523, even if four is non- Very close to, but ROI scores are calculated according to formula (4).(b) by sound shadow, 3 experts still may recognize that the interlude of UV in figure The crotch-shaped of UV, and our FUIQA systems judge that UV is nonstandard because that can not identify UV interlude, obtain with 3 experts not Consistent scoring.

Certainly fetal ultrasound image quality control method and method of the invention can also have a variety of transformation and remodeling, not It is confined to the specific structure of above embodiment.In short, protection scope of the present invention should include those skills common for this field It obviously converts or substitutes and retrofit for art personnel.

Claims

1. a kind of fetal ultrasound image quality control method, which is characterized in that include the following steps:

Step S120: extracting the local phase feature of the gray level image, obtains corresponding Local Symmetric phase diagram and part is non- Symmetrical phase figure；

Step S140: using the ROI in RGB image described in L-CNN model orientation, and judge whether ROI meets the 1st index；

Step S150: analyzing the ROI recognized using C-CNN model, and judges whether SB meets the 2nd index, UV Whether 3rd standard is met；

Step S160: fetai ultrasonogram image quality measurements are obtained according to the judging result in step S140 and step S150；

Wherein, the area that the 1st index is ROI accounts for 1/2 or more of scanning sector, and the 2nd index is SB full aobvious Show, sharpness of border, interior echoless, the 3rd index is that UV is echo in hook solid shows, do not interrupt continuously, allowing；

The L-CNN is convolutional neural networks used for positioning, and the C-CNN is the convolutional neural networks for classification, described RO I is abdomen area, and the SB is magenblase, and the UV is umbilical vein.

2. fetal ultrasound image quality control method according to claim 1, which is characterized in that in step S140, utilize ROI in RGB image described in L-CNN model orientation, and judge whether ROI meets the 1st index, specifically include the following steps:

Step S142: adjusting the size of the subgraph to 227*227, and input L-CNN, obtains the probability that window is ROI；

Step S143: the window probability in whole section centered on each pixel is obtained, the corresponding probability graph of ROI is obtained；

Step S144: the probability graph is smooth by two-sided filter, it is highest that probability is obtained using non-maxima suppression The coordinate of pixel is the best center point coordinate C that ROI is navigated to fixed size window_x,y；

Step S145: the ratio R that ROI area accounts for ultrasonic scan sector area is calculated_r/FOVObtain the scoring S to ROI_ROI, judgement Whether ROI meets the 1st index.

3. fetal ultrasound image quality control method according to claim 2, which is characterized in that in step S141, take off The subgraph of each fetal abdominal circumference section uses the window of fixed dimension 320*280 with the sliding of stride 10, in ultrasonic scan area Subgraph is taken off in domain.

4. fetal ultrasound image quality control method according to claim 2, which is characterized in that the step S144 is also wrapped Include following step:

With best center point coordinate C_x,yCentered on, and its be 10 sliding with stride up and down in the regional scope of 10 pixels Dynamic window, obtains central point, width, the height of the window of highest ROI probability, completes the automatic positioning of ROI in current test image, Wherein, the width of the window is obtained in the range of 210-400, and window height is in the range of 190-340.

5. fetal ultrasound image quality control method according to claim 2, which is characterized in that wherein, in step S160, Fetai ultrasonogram image quality measurements are obtained according to the judging result in step S140 and step S150, specifically include following steps It is rapid:

6. fetal ultrasound image quality control method according to claim 4, which is characterized in that wherein, the window Width is interval variation with 10 in the range of 210-400, and window height is interval variation with 10 in the range of 190-340.

7. a kind of fetal ultrasound picture quality controling system characterized by comprising

Characteristic extracting module obtains corresponding Local Symmetric phase diagram for extracting the local phase feature of the gray level image With local asymmetry phase diagram；

Image co-registration module, for the symmetrical phase figure and asymmetric phase diagram and the gray level image to be combined into RGB figure Picture；

Whether L-CNN model orientation module for utilizing L-CNN model orientation to the ROI in the RGB image, and judges ROI Meet the 1st index；

C-CNN model analysis module analyzes the ROI recognized using C-CNN model, and judges whether SB meets the 2nd Whether item index, UV meet the 3rd standard；And

As a result output module, for according to the judging result in the L-CNN model orientation module and C-CNN model analysis module Obtain fetai ultrasonogram image quality measurements；

The L-CNN convolutional neural networks used for positioning, convolutional neural networks of the C-CNN for classification, the ROI are Abdomen area, the SB are magenblase, and the UV is umbilical vein.

8. fetal ultrasound picture quality controling system according to claim 7, which is characterized in that the L-CNN model is fixed Position module include:

Image control unit for adjusting the size of the subgraph to 227*227, and inputs L-CNN, and obtaining window is ROI Probability；

Window probability acquiring unit, for using, above-mentioned subgraph takes off unit and above-mentioned image control unit obtains whole section Window probability centered on interior each pixel obtains the corresponding probability graph of ROI；

Center point coordinate positioning unit, for the probability graph is smooth by two-sided filter, using non-maxima suppression The coordinate for obtaining the highest pixel of probability is the best center point coordinate C that ROI is navigated to fixed size window_x,y；And

Judging unit accounts for the ratio R of ultrasonic scan sector area for calculating ROI area_r/FOVObtain the scoring S to ROI_ROI, sentence Whether disconnected ROI meets the 1st index.

9. fetal ultrasound picture quality controling system according to claim 8, which is characterized in that the center point coordinate is fixed Bit location is also used to best center point coordinate C_x,yCentered on, and its up and down in the regional scope of 10 pixels, with step Width is 10 sliding windows, obtains central point, width, the height of the window of highest ROI probability, completes ROI in current test image Automatic positioning, wherein for the width of the window in the range of 210-400, window height is in the range of 190-340.

10. fetal ultrasound picture quality controling system according to claim 7, which is characterized in that the result exports mould Block is used to obtain fetai ultrasonogram according to the judging result in the L-CNN model orientation module and C-CNN model analysis module Image quality measurements, if including ROI meet the 1st index, to the scoring S of ROI_ROIIt is 1, if it is not, being denoted as 0；