CN106504239A - A kind of method of liver area in extraction ultrasonoscopy - Google Patents
A kind of method of liver area in extraction ultrasonoscopy Download PDFInfo
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Abstract
The invention provides a kind of method for extracting liver area in ultrasonoscopy, it is characterised in that：Comprise the steps：Step 1：The even situation of uneven illumination in image is processed, makes the brightness of the liver area in image reach unanimity；Step 2：Reduce the noise in image using the FCM_I algorithms that with the addition of neighborhood relevance information and prior shape information and complete image segmentation；Step 3：According to FCM_I classification results and half-tone information, foreground area and the background area of image is obtained；Step 4：According to distribution and the shape of organ in liver, the image of complete liver area is obtained.The invention provides a kind of method for extracting liver area in ultrasonoscopy, improves the contact for being likely to occur liver area foreground pixel, and reduces the contact between the regional background, so that extracting than more complete liver area.
Description
Technical field
The present invention relates to the method for extracting organic region image in ultrasonoscopy, extracts ultrasound in particular to a kind of
The method of liver area in image.
Background technology
Ultrasonic examination be a kind of by applications of ultrasound in the technology of human detection, it utilize power of the human body to ultrasonic reflections
Imaging, measures the data such as the form and density of physiology or organizational structure, finds disease symptomses.As a kind of conventional physical examination
Means, ultrasound have many merits.What it utilized first is ultrasound wave, to human body fanout free region, is a kind of safe detection methods.
On the contrary using the inspection of various rays, there is larger injury to human body.Secondly, ultrasonographic low price,
The expense for once checking is usually dozens of yuan, and ordinary people can bear.And CT, the inspection fee of magnetic resonance are then more expensive.
In addition, ultrasound accuracy of detection in recent years is greatly improved, such as in terms of abdomen examination, ultrasound can be checked
Go out small liver cancer.But ultrasound also has the defect of its own, such as ultrasonoscopy to obscure, be easily affected by noise, cause to surpass
In sound, the positioning of focus is relatively difficult.Therefore ultrasound is frequently utilized for health check-up, examination at present, and CT, magnetic resonance are then used for true disease.
Liver has following characteristics in ultrasound：Internal uneven, cause liver to be difficult to extract completely；Outside and other regions
It is connected, causes other organ mistake additions are come in.In prior art, someone adopts Gaussian pyramid construction multi-resolution images
Picture, so that reduce the impact of speckle noise in ultrasound.Also someone improves the quality of ultrasonoscopy using anisotropic diffusion filtering,
Then using Chan-Vese active contour segmentation figure pictures, but for liver image is extracted, one kind has noise greatly, point
Difficulty is cut, the problems such as the image of acquisition is inaccurate.Chinese patent 201210131489.7 provide a kind of based on ultrasonoscopy and
The liver volume measuring method of threedimensional model, comprises the following steps：Three-dimensional hepatic model is set up using liver collection of illustrative plates；Obtain liver
The ultrasonoscopy of specified tangent plane, and carry out the edge wheel profile that image segmentation obtains liver in ultrasonoscopy；The ultrasound is schemed
As registering with the three-dimensional hepatic model；With in ultrasonoscopy, the edge wheel profile of liver is as reference picture, to three-dimensional liver mould
Type carries out elastic deformation；With the three-dimensional hepatic model after one group of parallel equally spaced plane cutting deformation, all sections are calculated
Area sum and the spacing of two neighboring section product, using result of calculation as liver volume, the method measurement side
Just quick, to human body without any infringement, can repeated measurement, but the noise problem being unable in effectively solving ultrasonoscopy.Chinese special
Profit 201510299989.5 provides a kind of method based on quick convex optimized algorithm registration three dimensional CT and ultrasonic liver image.Should
Following processes are included based on method of the quick convex optimized algorithm registration three dimensional CT with ultrasonic liver image：By ultrasound and CT images point
Resolution is adjusted to identical；To ultrasound and the rough registration based on rigid body translation of CT images；Extract the unification of multi-modality image registration
Characteristic information；Calculate under current non-rigid shape deformations field u (x), the gradient fields of the D (u) in data item and D (u) are to progressively convex optimization
Each step of method carries out model solution, obtains Deformation Field optimum correction value h (x), updates Deformation Field, until h (x) very littles；Root
According to the non-rigid shape deformations field for solving, ultrasonoscopy is converted, with CT image registrations；The prior art is by setting up rational mould
Type, designs fast, accurately three-D ultrasonic CT liver images registration Algorithm, but also fails to making an uproar in effectively solving ultrasonoscopy
Sound problem.
Content of the invention
Cause for overcoming present in prior art liver area noise in ultrasonoscopy to be connected with other regions greatly
Difficulty extract complete problem, the invention provides a kind of method for extracting liver area in ultrasonoscopy.The technology of the present invention
Scheme is：In a kind of extraction ultrasonoscopy, the method for liver area, comprises the steps：
Step 1：Pretreatment, specifically, processes the even situation of uneven illumination in image, makes liver area in image
Brightness reaches unanimity；
Step 2：Using FCM_I algorithm segmentation figure pictures, specifically, using with the addition of neighborhood relevance information and prior shape
The FCM_I algorithms of information reduce the noise in image and complete image segmentation；
Step 3：The segmentation of foreground and background is obtained, specifically, according to FCM_I algorithm classifications result and half-tone information, is obtained
Obtain foreground area and the background area of image；
Step 4：The liver area in ultrasonoscopy is obtained, specifically, according to distribution and the shape of organ in liver, is obtained
The image of complete liver area.
In some embodiments, also include step 5：Further optimized using active contour algorithm and extract liver area
Edge.
In some embodiments, in the step 1, uneven illumination is processed even that method is：
Formula (1)：I (x, y)=I (x, y)-min (NI (x, y)),
Wherein, I (x, y) is a pixel of image, NI (x, y)It is the neighborhood of I (x, y), by the pretreated figure of formula (1)
As dimmed, but the brightness of entirety reaches unanimity.
In some embodiments, in the step 2, the method for segmentation figure picture is：Using FCM algorithms, by continuous iteration
The method of the minimization of object function is made to obtain the value of the degree of membership of pixel in ultrasound wave liver area image, then according to maximum person in servitude
Category degree principle is divided to pixel, realizes the segmentation of image, and by adding neighborhood relevance information and prior shape information
Reduce the noise in image.
In some embodiments, the object function of the FCM_I algorithms of neighborhood relevance information is with the addition of in the step 2
For：
Formula (2) FCM_I：
Wherein, NjRepresent the neighborhood of pixel j, NrThat represented is neighborhood NjThe number of middle pixel, i are pixel class subscripts, its
In, have C classification, j is pixel subscript, wherein, have N number of pixel, m be regulation fuzzy membership weighted index, dij=|
|xj-vi| | for j-th pixel xjWith ith cluster center viGray scale difference value, dir=| | xr-vi| | for pixel xrPoly- with i-th
Class center viGray scale difference value, uijIt is xjRelative to viFuzzy membership, αijIt is the parameter for adjusting control neighbourhood effect.
In some embodiments, the fuzzy membership is the probability that pixel belongs to certain classification, its value belong to [0,
1], for a pixel, its fuzzy membership sum for belonging to each classification is 1, i.e. formula (3)：
In some embodiments, extreme value of the formula (2) under the conditions of formula (3) is solved, specifically, using Lagrange multiplier
Method obtains fuzzy membership u to each variable derivationijAnd cluster centre viIterative formula：
Formula (4)：
Formula (5)：
By formula (4) and the iteration of formula (5), object function, i.e. formula (2) is made, is gradually tended to minimizing, is obtained each pixel
Degree of membership, so as to realize the segmentation of image.
In some embodiments, in using the FCM_I algorithms that with the addition of neighborhood relevance information, add prior shape
Information is limited, and specifically, the contour area for limiting liver is Rg, restriction background area is Rb, it is further provided with transitional region Ri,
Wherein, Rg=Ix＞ p1, Rb=Ix＜ p2, in formula, IxFor image, transition region RiFor IxIn except RgAnd RbRegion.
In some embodiments, using formula (6)：
Add prior shape information, wherein, α1、α2And α3It is three different parameters, and α1＜ α2＜ α3.
Compared with prior art, the invention has the beneficial effects as follows：Algorithm proposed by the present invention is obtained preferably in an experiment
Extraction effect.The method is improve and is likely to occur liver area prospect using the prior information of liver shape in ultrasonoscopy
The contact of pixel, and the contact between the regional background is reduced, so that the region that extracts is more complete.In addition, algorithm
Have also combined the pretreatment of brightness irregularities background, and the operation of complete foreground area is extracted from multiple clusters, so as to
Obtain than more complete liver area.
Description of the drawings
Fig. 1 is the method and step flow chart of liver area in a kind of extraction ultrasonoscopy that the present invention is provided；
Fig. 2 is the ultrasonic liver dirty district area image without pretreatment；
Fig. 3 is the ultrasonic liver dirty district area image through pretreatment；
Fig. 4 is liver area, background area and the transitional region that prior information is obtained；
Fig. 5 is poly- by FCM algorithms respectively in the method for liver area in a kind of extraction ultrasonoscopy that the present invention is provided
The acquired example images of class result；
Fig. 6 is to pass through FCM_S algorithms in a kind of extraction ultrasonoscopy that the present invention is provided in the method for liver area respectively
The acquired example images of cluster result；
Fig. 7 is to pass through FCM_I algorithms in a kind of extraction ultrasonoscopy that the present invention is provided in the method for liver area respectively
The acquired example images of cluster result.
Specific embodiment
Below in conjunction with drawings and Examples, the present invention will be described in further detail.It should be appreciated that described herein
Specific embodiment only in order to explain the present invention, is not intended to limit the present invention.
The side of liver area in a kind of extraction ultrasonoscopy that Fig. 1 to Fig. 7 show schematically show according to present disclosure
Method.
As shown in figure 1, in a kind of extraction ultrasonoscopy of present disclosure liver area method, using FCM algorithms, profit
Contact with priori control tactics region.But FCM is only a clustering algorithm, it is therefore desirable to coordinate other pretreatment
The task of extraction liver area can be completed with post processing.The following is liver area extraction scheme proposed by the present invention.
Step 1：Pretreatment, specifically, processes the even situation of uneven illumination in image, makes liver area in image
Brightness reaches unanimity.Not only noise is big for ultrasonoscopy, and is affected by brightness irregularities, as shown in Fig. 2 background luminance is dark
Region is easy to be mistaken for background.It is even that method is to pass through that the present invention processes uneven illumination：Formula (1)：I (x, y)=I (x, y)-
min(NI (x, y)) place buries the even situation of uneven illumination in image, wherein, I (x, y) is a pixel of image, NI (x, y)It is I
The neighborhood of (x, y), the image after pre- place buries are as shown in Figure 3, although liver area is dimmed, but the brightness of entirety tends to
Unanimously, more using the extraction of liver area.
Step 2：Using FCM_I algorithm segmentation figure pictures, specifically, using with the addition of neighborhood relevance information and prior shape
The FCM algorithms of information reduce the noise in image and complete image segmentation.In this embodiment of the present invention, divide in step 2
Actual every the method for image it is：Using FCM algorithms, the method for the minimization of object function is made to obtain ultrasound wave liver by continuous iteration
In dirty district area image, the value of the degree of membership of pixel, then divides to pixel according to maximum membership grade principle, realizes image
Segmentation, and reduce the noise in image by adding neighborhood relevance information and prior shape information.
Traditional, if the object function of image I, FCM is：Formula (7)：FCM：Wherein, i is
Pixel class subscript, wherein, has C classification, and j is pixel subscript, wherein, has N number of pixel, and m is to adjust fuzzy membership
Weighted index, dij=| | xj-vi| | for j-th pixel xjWith ith cluster center viGray scale difference value, uijIt is xjRelative to
viFuzzy membership.
Fuzzy membership is the probability that pixel belongs to certain classification, and its value belongs to [0,1], and for a pixel, which belongs to
In each classification fuzzy membership sum be 1, i.e.,
Formula (3)：
Extreme value of the solution formula (7) under the conditions of formula (3), specifically, using method of Lagrange multipliers to each variable derivation,
Obtain fuzzy membership uijAnd cluster centre viIterative formula：
Formula (8)：
Formula (9)：
By formula (8) and the iteration of formula (9), object function, i.e. formula (7) is made, is gradually tended to minimizing, is obtained each pixel
Degree of membership, so as to realize the segmentation of image.
The greatest problem of original FCM algorithms is that object function comprising any spatial information, does not cause affected by noise
Very big.Only using the information of pixel itself, this problem that brings is that anti-noise ability is poor, due to noise usually self-existent,
Distinguish substantially with surrounding pixel, therefore noise is easily independently formed a region by algorithm, so that originally complete region
In be filled with hole.Another problem of original FCM is when the irregular colour in a region is even, and it is divided into several areas easily
Domain, increases the difficulty of later stage process.Then, make an inventive point of the present invention, in this embodiment of the present invention, in FCM
Neighborhood relevance information is with the addition of on algorithm, and the object function of the FCM algorithms for obtaining with the addition of neighborhood relevance information is：
Formula (2) FCM_I：
Wherein, NjRepresent the neighborhood of pixel j, NrThat represented is neighborhood NjThe number of middle pixel, i are pixel class subscripts, its
In, have C classification, j is pixel subscript, wherein, have N number of pixel, m be regulation fuzzy membership weighted index, dij=|
|xj-vi| | for j-th pixel xjWith ith cluster center viGray scale difference value, dir=| | xr-vi| | for pixel xrPoly- with i-th
Class center viGray scale difference value, uijIt is xjRelative to viFuzzy membership, αijIt is the parameter for adjusting control neighbourhood effect.
Fuzzy membership is the probability that pixel belongs to certain classification, and its value belongs to [0,1], and for a pixel, which belongs to
In each classification fuzzy membership sum be 1, i.e.,
Formula (3)：
Extreme value of the solution formula (2) under the conditions of formula (3), specifically, using method of Lagrange multipliers to each variable derivation,
Obtain fuzzy membership uijAnd cluster centre viIterative formula：
Formula (3)：
Formula (4)：
By formula (3) and the iteration of formula (4), object function, i.e. formula (2) is made, is gradually tended to minimizing, is obtained each pixel
Degree of membership, so as to realize the segmentation of image.
The FCM_I algorithms that with the addition of neighborhood relevance can be effectively increased anti-noise ability, but the method interpolation is complete
The consistent item of office, not whole region be required for, therefore make another inventive point of the present invention, in this embodiment of the present invention
In, add prior shape information in FCM_I algorithms, that is, add limiting, specifically, contact in region, and the connection outside region
It is not consistent.The restriction of interpolation determined according to different objects, the wheel that be limited to a liver of the present invention in liver is extracted
Wide region Rg, and it is R to limit background areab, another different in view of the liver area in every piece image, in liver area
Transition region R be also add outside domain and background areai, wherein, Rg=Ix＞ p1, Rb=Ix＜ p2, in formula, IxFor image, transition region Ri
For IxIn except RgAnd RbRegion.In order to determine liver area, doctor is allowed to sketch out the liver area in several ultrasonoscopys
Come, and be superimposed ballot.The many regions of gained vote are considered as liver area, and it is background to win the vote less or do not have the region that wins the vote, its
His is transitional region.
As preferred, region RgThe contact of interior emphasis prospect, needs to strengthen impact of the foreground area to surrounding, that is, works as vi
For prospect center when, using less α values, increase the proportion of expression formula (2) latter half.On the other hand, region RgInterior minimizing
The contact of background, that is, work as viFor background center when, using larger α values, reduce the proportion of expression formula (2) latter half.Due to
Expression formula iteration tends to minimum, therefore, using α values defined herein, can make RgIn extraction liver area more complete.
Fig. 4 shows the extraction process of liver prior information.Wherein (a) is the liver area of manual extraction, (b) is (a)
The result of binaryzation, is (c) by the result after the figure superposition in (b).Figure in Fig. 4 (c) is Ix.
As preferred, using formula (6)：
Add prior shape information, wherein, α1、α2And α3It is three different parameters, and α1＜ α2＜ α3.
In step 2 of the present invention, due to the addition of prior information and neighborhood relevance information, compare the liver area of acquisition
Completely.
Step 3：Obtain the substantially segmentation of foreground and background.Specifically, according to classification results and the gray scale of FCM_I algorithms
Information, the foreground and background region for obtaining.
Step 4：Obtain the liver area in ultrasonoscopy.Specifically, in abdominal ultrasound images, liver is typically maximum
Organ, using this priori, according to distribution and the shape of organ in liver, obtain the image of complete liver area.
As preferred, also include step 5：Further optimize the edge for extracting liver area using active contour algorithm.
In order to verify algorithm proposed by the present invention, the method for the present invention is tested on the ultrasound image.Such as Fig. 5 extremely
Shown in Fig. 7, image has larger noise, and occurs in that the situation of brightness irregularities.Algorithm and original FCM and FCM_
S is compared.Clusters number 3, α=1 in FCM_S, it is 5 × 5 that the present invention proposes the pretreatment Size of Neighborhood of algorithm, parameter alpha1
=4, α2=1, α3=0.1.Fig. 5 to Fig. 7 is the example of three kinds of FCM algorithm cluster results, and wherein Fig. 5 is the result of original FCM,
Fig. 6 is the result of FCM_S, and Fig. 7 is the result of FCM_I algorithms, can be seen that from Fig. 5 to Fig. 7 the results contrast of FCM is broken, and
The result of FCM_S then complete some, the result of FCM_I algorithms proposed by the present invention is best.It can be seen that constraint proposed by the present invention
Method can improve the classifying quality of FCM.
In order to more accurately be compared data, by experiment used in ultrasonoscopy carried out artificial segmentation, extract
Right-on hepatic portion is used as reference object.Then liver area is extracted using above-mentioned algorithm, and with correct with reference to knot
Fruit is compared, and obtains and accuracy P=TP/ (TP+FP), recall rate R=TP/AP, and wherein TP is the correct pixel count of classification, and FP is
The pixel count of classification error, AP are the sums of liver area pixel.Accuracy P of FCM_I algorithms and call together as can be seen from Table 1
Rate R of returning increases compared with FCM and FCM_I algorithms.
The Performance comparision of 1 three kinds of FCM of table
In a kind of extraction ultrasonoscopy of present disclosure, the method for liver area is obtained in an experiment
Really.The method is improve and is likely to occur liver area foreground pixel using the prior information of liver shape in ultrasonoscopy
Contact, and the contact between the regional background is reduced, so that the region that extracts is more complete.In addition, algorithm herein in connection with
The pretreatment of brightness irregularities backgrounds, and the operation of complete foreground area is extracted from multiple clusters, so as to obtain
Than more complete liver area.
Described above illustrates and describes the preferred embodiments of the present invention, as previously mentioned, it should be understood that not office of the invention
It is limited to presently disclosed form, is not to be taken as the exclusion to other embodiment, and can be used for various other combinations, modification
And environment, and can be entered by the technology or knowledge of above-mentioned teaching or association area in invention contemplated scope of the present invention
Row is changed.And change that those skilled in the art are carried out and change be without departing from the spirit and scope of the present invention, then all should be in the present invention
In the protection domain of claims.
Claims (9)
1. a kind of extract ultrasonoscopy in liver area method, it is characterised in that：Comprise the steps：
Step 1：Pretreatment, specifically, processes the even situation of uneven illumination in image, makes the brightness of the liver area in image
Reach unanimity；
Step 2：Using FCM_I algorithm segmentation figure pictures, specifically, using with the addition of neighborhood relevance information and prior shape information
FCM_I algorithms reduce image in noise and complete image segmentation；
Step 3：The segmentation of foreground and background is obtained, specifically, according to FCM_I algorithm classifications result and half-tone information, is schemed
The foreground area of picture and background area；
Step 4：The liver area in ultrasonoscopy is obtained, specifically, according to distribution and the shape of organ in liver, is obtained complete
The image of liver area.
2. according to claim 1 extract ultrasonoscopy in liver area method, it is characterised in that：Also include step 5：
Further optimize the edge for extracting liver area using active contour algorithm.
3. according to claim 1 extract ultrasonoscopy in liver area method, it is characterised in that：In the step 1,
Process uneven illumination even that method is：
Formula (1)：I (x, y)=I (x, y)-min (NI (x, y)),
Wherein, I (x, y) is a pixel of image, NI (x, y)It is the neighborhood of I (x, y), is become by the pretreated image of formula (1)
Secretly, but the brightness of entirety reaches unanimity.
4. according to arbitrary described method for extracting liver area in ultrasonoscopy in claims 1 to 3, it is characterised in that：Institute
The method for stating segmentation figure picture in step 2 is：Using FCM algorithms, obtain the method for the minimization of object function by continuous iteration
In ultrasound wave liver area image, the value of the degree of membership of pixel, then divides to pixel according to maximum membership grade principle, real
The segmentation of existing image, and reduce the noise in image by adding neighborhood relevance information and prior shape information.
5. according to claim 4 extract ultrasonoscopy in liver area method, it is characterised in that：In the step 2
The object function that with the addition of the FCM_I algorithms of neighborhood relevance information is：
Formula (2) FCM_I：
Wherein, NjRepresent the neighborhood of pixel j, NrThat represented is neighborhood NjThe number of middle pixel, i are pixel class subscripts, wherein,
Total C classification, j are pixel subscripts, wherein, have N number of pixel, m be adjust fuzzy membership weighted index, dij=| |
xj-vi| | for j-th pixel xjWith ith cluster center viGray scale difference value, dir=| | xr-vi| | for pixel xrPoly- with i-th
Class center υiGray scale difference value, uijIt is xjRelative to υiFuzzy membership, αijIt is the parameter for adjusting control neighbourhood effect.
6. according to claim 5 extract ultrasonoscopy in liver area method, it is characterised in that：The fuzzy membership
Degree is the probability that pixel belongs to certain classification, and its value belongs to [0,1], and for a pixel, which belongs to the fuzzy of each classification
Degree of membership sum is 1, i.e. formula (3)：
7. according to claim 6 extract ultrasonoscopy in liver area method, it is characterised in that：Solution formula (2) exists
Extreme value under the conditions of formula (3), specifically, using method of Lagrange multipliers to each variable derivation, obtains fuzzy membership uijAnd
Cluster centre υiIterative formula：
Formula (4)：
Formula (5)：
By formula (4) and the iteration of formula (5), object function, i.e. formula (2) is made, is gradually tended to minimizing, is obtained the person in servitude of each pixel
Category degree, so that realize the segmentation of image.
8. according to claim 7 extract ultrasonoscopy in liver area method, it is characterised in that：Using with the addition of
In the FCM_I algorithms of neighborhood relevance information, add prior shape information and limit, specifically, the contour area for limiting liver is
Rg, restriction background area is Rb, it is further provided with transitional region Ri, wherein, Rg=Ix＞ p1, Rb=Ix＜ p2, in formula, IxFor figure
Picture, transition region RiFor IxIn except RgAnd RbRegion.
9. according to claim 8 extract ultrasonoscopy in liver area method, it is characterised in that：Using formula (6)：
Add prior shape information, wherein, α1、α2And α3It is three different parameters, and α1＜ α2＜ α3.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108932449A (en) * | 2017-05-27 | 2018-12-04 | 广州智慧城市发展研究院 | A kind of bar code binarization method and system based on cluster |
| CN110738214A (en) * | 2019-09-26 | 2020-01-31 | 天津大学 | Fuzzy C-means clustering light interference fringe pattern binarization method |
| CN112037302A (en) * | 2020-08-31 | 2020-12-04 | 南通大学 | Diffusion light tomography intelligent modeling method based on big data |
| CN112561860A (en) * | 2020-11-23 | 2021-03-26 | 重庆邮电大学 | BCA-UNet liver segmentation method based on prior shape constraint |
| CN114677421A (en) * | 2022-04-12 | 2022-06-28 | 卡本(深圳)医疗器械有限公司 | Method for estimating rigid/non-rigid registration of 2d organ |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102800089A (en) * | 2012-06-28 | 2012-11-28 | 华中科技大学 | Main carotid artery blood vessel extraction and thickness measuring method based on neck ultrasound images |
| CN103366379A (en) * | 2013-07-29 | 2013-10-23 | 江苏中惠医疗科技股份有限公司 | Level set medical image segmentation method based on heredity kernel fuzzy clustering |
-
2016
- 2016-10-25 CN CN201610934000.8A patent/CN106504239B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102800089A (en) * | 2012-06-28 | 2012-11-28 | 华中科技大学 | Main carotid artery blood vessel extraction and thickness measuring method based on neck ultrasound images |
| CN103366379A (en) * | 2013-07-29 | 2013-10-23 | 江苏中惠医疗科技股份有限公司 | Level set medical image segmentation method based on heredity kernel fuzzy clustering |
Non-Patent Citations (10)
| Title |
|---|
| MOHAMED N. AHMED ET AL.: "A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data", 《IEEE TRANSACTIONS ON MEDICAL IMAGING》 * |
| NISHANT JAIN ET AL.: "IFCM Based Segmentation Method for Liver Ultrasound Images", 《SYSTEMS-LEVEL QUALITY IMPROVEMENT》 * |
| XUECHEN LI ET AL.: "Liver Segmentation from CT Image Using Fuzzy Clustering and Level Set", 《JOURNAL OF SIGNAL AND INFORMATION PROCESSING》 * |
| ZHANG XIAOFENG ET AL.: "Medical image segmentation using improved FCM", 《SCIENCE CHINA》 * |
| 张小峰: "基于模糊聚类算法的医学图像分割技术研究", 《中国博士学位论文全文数据库》 * |
| 张茜: "CT图像中肝脏分割方法研究", 《中国优秀硕士学位论文全文数据库 医药卫生科技辑》 * |
| 曾凡玉 等: "一种结合邻域信息的FCM图像分割新算法", 《重庆文理学院学报》 * |
| 罗清 等: "基于先验形状信息核图割模型的肝脏分割方法", 《计算机工程与设计》 * |
| 赵伟 等: "基于顶帽变换和模糊C均值聚类的图像分割方法", 《计算机技术与发展》 * |
| 金凯成 等: "基于模糊聚类和水平集的肝脏肿瘤分割研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
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