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CN111127479A - Level set image segmentation method based on curve area - Google Patents

Level set image segmentation method based on curve area Download PDF

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CN111127479A
CN111127479A CN201911301075.2A CN201911301075A CN111127479A CN 111127479 A CN111127479 A CN 111127479A CN 201911301075 A CN201911301075 A CN 201911301075A CN 111127479 A CN111127479 A CN 111127479A
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贺建峰
陈路达
管观华
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Kunming University of Science and Technology
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    • G06T7/10—Segmentation; Edge detection
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    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
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    • G06T2207/30061—Lung

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Abstract

The invention relates to a level set image segmentation method based on curve area, and belongs to the technical field of image processing. The method avoids the problems of large calculation amount and low efficiency caused by the fact that only gray information of inner and outer areas of a curve is considered and edge information of the curve is not considered in the calculation motion process of a set curve in a gray uniform image due to a traditional level set segmentation model (CV model). According to the improved method provided by the invention, a curve area item which is fitted based on image edge information is added in the CV model, and the image with uniform gray scale is segmented. Experimental results show that the improved method can obviously improve the calculation and the movement speed of the curve in the CV model and improve the image segmentation efficiency.

Description

Level set image segmentation method based on curve area
Technical Field
The invention relates to a level set image segmentation method based on curve area, and belongs to the technical field of image processing.
Background
Image segmentation is to divide an image into meaningful parts according to a certain uniformity (or consistency) principle, so that each part meets a certain consistency requirement. The traditional level set segmentation CV model utilizes global image information to drive the curve evolution. The driving curve motion is mainly the energy difference between the inside and the outside of the curve, when the boundary of the target is detected, the curve only considers the gray information of the inside and the outside of the curve, but does not consider the edge information of the curve, so that the calculation amount is large, and the efficiency is low. In order to improve the accuracy of the CV model for detecting the target edge, the invention adds a curve area item based on edge information in the traditional CV model, can accelerate the motion speed of the curve and improve the calculation efficiency.
Disclosure of Invention
The invention aims to provide a level set image segmentation method based on curve area, which is used for accelerating the movement speed of a set curve in an image, improving the calculation efficiency and acquiring a segmented image of a region of interest.
The technical scheme of the invention is as follows: a level set image segmentation method based on curve area comprises the following specific steps:
step 1: and drawing a closed curve for any image to be segmented to divide the image into an inner area and an outer area.
Step 2: and calculating the area energy of the whole image region and the area energy inside the closed curve, wherein the area energy outside the closed curve is equal to the area energy of the whole image region minus the area energy inside the closed curve.
Step 3: an image curve area energy constraint term is calculated that is equal to the area energy outside the closed curve minus the area energy inside the closed curve.
Step 4: calculating an overall image energy general function which is equal to the sum of 4 terms; the first term is the length term of the closed curve, the second term is the curve area energy constraint term, and the third and fourth terms are the inner area energy term and the outer area energy term, respectively.
Step 5: and when the closed evolution curve is positioned at the boundary of the target to be segmented, the total energy general function of the image is minimum, and the segmented image is obtained.
The Step2 is specifically as follows:
(1): calculating the area energy S of the whole image regionu，
Figure BDA0002321801810000011
Wherein u is0Representing the gray value of any image to be segmented, wherein H (phi) is a Heaviside function, phi is a level set function, and phi is an edge detection function
Figure BDA0002321801810000012
▽ denotes the gradient operator, GσGaussian function representing window size σ
Figure BDA0002321801810000013
u is a constant and Ω is the image space u0One set of (a).
(2): calculating the area energy S inside the closed curveinside(C)，
Figure BDA0002321801810000021
(3): calculating the area energy S outside the closed curveoutside(C)，
Soutside＝Su-Sinside＝∫Ωg*u0*H(φ)-g*H(φ)dxdy。
The Step3 is specifically as follows:
calculating an image curve area energy constraint term Econv，
Figure BDA0002321801810000022
The Step4 is specifically as follows:
calculating the energy general function of the whole image:
Figure BDA0002321801810000023
Figure BDA0002321801810000024
wherein, the first term of the equation is the length term of the curve C, mu is more than or equal to 0 and is the coefficient of the length term, the second term of the equation is the area term of the curve C with the edge information fused, u is the area term of the curve C0Representing the gray value of any image to be segmented, v is more than or equal to 0 and is the coefficient of area term, lambda1,λ2A coefficient of energy terms in inner and outer regions, c1,c2Respectively representing the mean grey value inside the evolution curve and the mean grey value outside the evolution curve, deltaεAnd (phi) represents the Dirac function.
The invention has the beneficial effects that: the method avoids the situations of large calculation amount and low efficiency caused by the fact that only gray information of inner and outer areas of a curve is considered and edge information of the curve is not considered in the calculation motion process of a set curve in an image due to the traditional level set segmentation CV model. The improved method provided by the invention can obviously improve the calculation and the movement speed of the curve in the CV model and improve the image segmentation efficiency.
Drawings
FIG. 1 is a flow chart of the steps of the present invention;
fig. 2 is a segmented contrast image of a lung CT scan in an embodiment of the present invention, in which (a) is an original image, (b) is an image segmentation of a CV model, and (c) is an image segmentation of an improved CV model.
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
Example 1: as shown in fig. 1, a method for segmenting a level set image based on a curve area includes the following specific steps:
step 1: and drawing a closed curve for any image to be segmented to divide the image into an inner area and an outer area.
Step 2: and calculating the area energy of the whole image region and the area energy inside the closed curve, wherein the area energy outside the closed curve is equal to the area energy of the whole image region minus the area energy inside the closed curve.
Step 3: an image curve area energy constraint term is calculated that is equal to the area energy outside the closed curve minus the area energy inside the closed curve.
Step 4: calculating an overall image energy general function which is equal to the sum of 4 terms; the first term is the length term of the closed curve, the second term is the curve area energy constraint term, and the third and fourth terms are the inner area energy term and the outer area energy term, respectively.
Step 5: and when the closed evolution curve is positioned at the boundary of the target to be segmented, the total energy general function of the image is minimum, and the segmented image is obtained.
In step S2, the process is described by taking the initial calculation parameter Δ t (S) of the lung CT scan image in fig. 2 as 0.1, which is specifically as follows:
(1): calculating the area energy of the whole image region: suArea energy of whole image area
Figure BDA0002321801810000031
Wherein u is0＝[0,195]Representing the gray value of any image to be segmented (in this case, the lung CT scan image), H (phi) is the Heaviside function, phi is the level set function, and the value of the Heaviside function is 0.3183 in this case, and the edge detection function
Figure BDA0002321801810000032
▽ denotes the gradient operator, GσGauss function representing window size sigma
Figure BDA0002321801810000033
The window size is then 5 x 5, Gσ1.5, the value of the gaussian function is Gσ＝[0.0144,0.0853]U is a constant 650.25 and Ω is the image space u0One set of (a).
(2): calculate the area energy inside the closed curve:
Figure BDA0002321801810000034
(3): calculate the area energy outside the closed curve: soutside(C)Is the area energy outside the closed curve,
Soutside＝Su-Sinside＝∫Ωg*u0*H(φ)-g*H(φ)dxdy＝[-0.32,7.78]
the step S3 is specifically as follows:
calculating an image curve area energy constraint term:
Figure BDA0002321801810000035
the step S4 is specifically as follows:
calculating the energy general function of the whole image:
Figure BDA0002321801810000036
Figure BDA0002321801810000037
wherein, the first term of the equation is the length term of the curve C, the coefficient of which the length term is mu ≧ 0 is 650.25, the second term of the equation is the area term fused with the edge information curve C, the coefficient of which the area term is v ≧ 0 is 1, λ1,λ2The coefficient of the energy term of the inner area and the outer area is more than or equal to 0 and is respectively equal to 1 and c1,c2Equal to 19.1266 and 39.0575, respectively, representing the mean gray value inside the evolution curve and the mean gray value outside the evolution curve, δε(φ)＝[0.0187,0.3183]Representing the Dirac function.
The original image in fig. 2 was iteratively divided by the above method, and the results are shown in table 1:
Figure BDA0002321801810000041
TABLE 1
Table 1 shows the image segmentation iteration number and the running time experiment result of fig. 2, and it can be concluded that the improved CV model far exceeds the conventional CV model in terms of running efficiency, but after the CV model is sufficiently iterated, the evolution curve fails to reach the outer contour edge of the target.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit and scope of the present invention.

Claims (4)

1.一种基于曲线面积的水平集图像分割方法，其特征在于：1. a level set image segmentation method based on curve area, is characterized in that: Step1：对任意一幅待分割的图像，画一条闭合曲线将图像划分为内部区域和外部区域；Step1: For any image to be segmented, draw a closed curve to divide the image into an inner area and an outer area; Step2：计算整幅图像区域的面积能量和闭合曲线内部的面积能量，闭合曲线外部的面积能量等于整幅图像区域的面积能量减去闭合曲线内部的面积能量；Step2: Calculate the area energy of the entire image area and the area energy inside the closed curve. The area energy outside the closed curve is equal to the area energy of the entire image area minus the area energy inside the closed curve; Step3：计算图像曲线面积能量约束项，其等于闭合曲线外部的面积能量减去闭合曲线内部的面积能量；Step3: Calculate the area energy constraint term of the image curve, which is equal to the area energy outside the closed curve minus the area energy inside the closed curve; Step4：计算整幅图像能量泛函数，其等于4项之和；第一项为闭合曲线的长度项，第二项为曲线面积能量约束项，第三项和第四项分别为内部面积能量项和外部面积能量项；Step4: Calculate the energy functional function of the whole image, which is equal to the sum of 4 terms; the first term is the length term of the closed curve, the second term is the energy constraint term of the curve area, the third term and the fourth term are the internal area energy terms respectively and the external area energy term; Step5：当闭合演化曲线位于要分割目标的边界时，图像总能量泛函数最小，得到分割图像。Step5: When the closed evolution curve is located at the boundary of the target to be segmented, the total energy functional function of the image is the smallest, and the segmented image is obtained. 2.根据权利要求1所述的基于曲线面积的水平集图像分割方法，其特征在于所述Step2具体为：2. the level set image segmentation method based on curve area according to claim 1 is characterized in that described Step2 is specifically: (1)：计算整幅图像区域的面积能量Su，
Figure FDA0002321801800000011
其中，u0表示任意一幅待分割图像灰度值，H(φ)为Heaviside函数，φ为水平集函数，边缘检测函数
Figure FDA0002321801800000012
▽表示梯度算子，Gσ表示窗口大小为σ的高斯函数
Figure FDA0002321801800000013
u为常数，Ω是图像空间u0中的一个集合；(1): Calculate the area energy Su of the entire image area,
Figure FDA0002321801800000011
Among them, u 0 represents the gray value of any image to be segmented, H(φ) is the Heaviside function, φ is the level set function, and the edge detection function
Figure FDA0002321801800000012
▽ represents the gradient operator, G σ represents the Gaussian function with window size σ
Figure FDA0002321801800000013
u is a constant, Ω is a set in the image space u 0 ; (2)：计算闭合曲线内部的面积能量Sinside(C)，
Figure FDA0002321801800000014
(2): Calculate the area energy S inside(C) inside the closed curve,
Figure FDA0002321801800000014
 (3)：计算闭合曲线外部的面积能量Soutside(C)，(3): Calculate the area energy S outside(C) outside the closed curve, Soutside＝Su-Sinside＝∫Ωg*u0*H(φ)-g*H(φ)dxdy。S outside =S u -S inside =∫ Ω g*u 0 *H(φ)-g*H(φ)dxdy. 3.根据权利要求1所述的基于曲线面积的水平集图像分割方法，其特征在于所述Step3具体为：3. the level set image segmentation method based on curve area according to claim 1, is characterized in that described Step3 is specifically: 计算图像曲线面积能量约束项Econv，
Figure FDA0002321801800000015
Calculate the image curve area energy constraint term E conv ,
Figure FDA0002321801800000015
 4.根据权利要求1所述的基于曲线面积的水平集图像分割方法，其特征在于所述Step4具体为：4. the level set image segmentation method based on curve area according to claim 1, is characterized in that described Step4 is specifically: 计算整幅图像能量泛函数：
Figure FDA0002321801800000016
Compute the entire image energy functional:
Figure FDA0002321801800000016
Figure FDA0002321801800000021
Figure FDA0002321801800000021
 其中，方程的第一项为曲线C的长度项，μ≥0为长度项的系数，方程的第二项为融合了边缘信息曲线C的面积项，u0表示任意一幅待分割图像灰度值，v≥0为面积项的系数，λ1,λ2≥0为内外区域能量项的系数，c1,c2分别表示演化曲线内部的平均灰度值和演化曲线外部的平均灰度值，δε(φ)表示Dirac函数。Among them, the first term of the equation is the length term of the curve C, μ≥0 is the coefficient of the length term, the second term of the equation is the area term of the curve C fused with the edge information, u 0 represents any gray level of the image to be segmented value, v≥0 is the coefficient of the area term, λ 1 , λ 2 ≥ 0 are the coefficients of the energy term of the inner and outer regions, c 1 , c 2 represent the average gray value inside the evolution curve and the average gray value outside the evolution curve, respectively , δ ε (φ) represents the Dirac function.
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