principle, and the shadow part generally occupy a large area of the image, a clearer .... Computer Vision and Pattern Recognition(CVPR), Minneapolis, pp.
Available online at www.sciencedirect.com
Physics Procedia 33 (2012) 1168 – 1175
2012 International Conference on Medical Physics and Biomedical Engineering
Sonar Image Segmentation based on an Improved Level Set Method Guangyu Liu and Hongyu Bian ,Hong Shi College of Underwater Acoustics Engineering,Harbin Engineering University,Harbin, China
Abstract Aiming at the problem that the existing image segmentation methods cannot be accurately applied in sonar image segmentation, an improved level set sonar image segmentation method was proposed in this paper. Firstly, this paper analyzed the different characteristics of sonar image from the optical image, and the biggest drawback among them is the existence of shadow interference, namely for a sonar image with shadow part, the traditional level set segmentation algorithm will often make the shadow as the segmentation target to be exported out because the feature of sonar target object is not significant enough; Secondly, to overcome the shadow negative effects in sonar image segmentation and achieve selective segmentation, this paper did sonar image preprocessing by morphological top-hat and bottom-hat transformation, then carried on level set method without re-initialization and constructed an improved level set sonar image segmentation system; finally, compared the improved level set method with the traditional level set method in the simulation experiment, and the results showed that the improved level set segmentation method is more adapted to sonar image with uneven background.
©2012 2011Published Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [name Committee. organizer] © by Elsevier B.V. Selection and/or peer review under responsibility of ICMPBE International Open access under CC BY-NC-ND license. Keywords: sonar image image segmentation; level set; morphological operations
1.Introduction Sonar image segmentation is the premise of late processing including target recognition, tracking, classification and so on, it plays an important role in sonar image processing, so it is urgent and valuable for the research of sonar image segmentation. Today many scholars did the research of sonar image segmentation and have achieved some successful sonar image segmentation methods such as Markov random fields [1], neural networks [2], spectral clustering [3] and so on. In recent years, with the successful application of partial differential equation in image processing, image segmentation method based on level set [4] was put forward, and because it can be superior in solving curve topology structure transformation problems to the parameterization methods, it has become important research direction in mechanics,
1875-3892 © 2012 Published by Elsevier B.V. Selection and/or peer review under responsibility of ICMPBE International Committee. Open access under CC BY-NC-ND license. doi:10.1016/j.phpro.2012.05.192
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computer graphics, image processing and target tracking fields [5]. Level set applications in image processing is mainly reflected in image segmentation. There have two kinds of image segmentation methods based on level set, one is the level set based on C-V model proposed by Chan and Vese [6], the other is level set without re-initialization proposed by Li [7]. The former is beneficial to segmentation for the image with fuzzy or discontinuous boundaries and noisy pollution, it is a global optimization model for image segmentation for it is not conducive to detail segmentation of the image because many local characteristics are ignored, and also its computational complexity is relatively high; the latter is improved by taking the local characteristics of the image into account, it amends the defects of the former essentially, and for it on longer need re-initialization, the computational complexity is greatly reduced, so it has higher research value. This paper mainly studied the level set segmentation without re-initialization, its performance is prominent in optical image segmentation, but its application research in sonar image segmentation is not enough yet, so in this paper, we discussed the application of level set in sonar image segmentation and proposed an improved level set image segmentation method. 2.Levet set without re-initialization The following will introduce the concept of active contour firstly. Active contour, also known as curve evolution, is a dynamic process of curve transformation over time. If the mathematical expression of twodimensional curve is C( p, t ) ( x( p, t ), y( p, t )) , showed in Fig. 1, the Partial Differential equation (PDE) evolution is: C ( p, t ) t
kN
Where k is curvature, describing the transformation rate of curve, N is normal vector. N( p )
C (0)
C (l )
C( p) T( p ) Figure 1. Definition of curve.
If (C( p, t ), t ) PDE evolution is
0 , that is dynamic curve C ( p, t ) is the zero level set of function
C t
t
( x, y, t ) , and its
0.
Namely:
t
At this time k can be expressed as:
C t
kN
( k
)
k
.
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k
(
)
div(
).
Then the final level set PDE evolution equation can be obtained as follows:
t
div (
)
Figure 2. The leve lset evolution
The evolution diagram of level set function is shown in Fig. 2, its implementation is relatively simple and easy to extend to higher dimensions, so it can basically solve some complex curve evolution problems, but on the other hand, it needs stopping evolution to re-initialize in the evolution process, in this paper, we use a level set evolution method based on energy penalty term without re-initialization [8], and the energy penalty term is defined as:
P( )
1 ( 2
1)2 dxdy.
Firstly, give the following definition
E( )
P( ) Em ( ).
Where Em ( ) is an energy driver of contour curve evolution. Secondly, Em ( ) can be expressed by area constraint term and length constraint term of the contour curve as follows:
Em ( )
Ag ( ) vLg ( ).
Then, equation (7) can be obtained further as follows:
E( )
P( ) [ Ag ( ) vLg ( )] 1 ( 2 v
g ( )
Where g is the image edges index, and the computation formula is:
1) 2 dxdy dxdy.
gH (
)dxdy
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g
1
1 G
I0
.
Finally, level set evolution equation is
t
[
div(
)] [ g ( ) v ( )div( g
)].
3.Sonar iamge segmentation method baes on the improved level set 3.1.Design Principle Optical image usually have clear edges and sharp regional characteristics, while sonar image often have low resolution, incomplete and fuzzy boundary, existence of shadows and other features because of its imaging mechanism, this will make some accurate optical image segmentation methods cannot get good results for sonar image segmentation. On the one hand, the environment noise will cause the acoustic signal waveform distortion and loss of signal information, and the receiver sound waves is often weakened and incomplete due to the complexity of sound propagation medium, so sonar image do not have precise, accurate, clear and obvious boundary feature as optical image has, and the boundary of target objects often appear irregular, uncertain, leading the non-feasibility of many optical image segmentation methods based on edge. On the other hand, there is usually a shadow in an obtained sonar image because of the imaging principle, and the shadow part generally occupy a large area of the image, a clearer boundary and more obvious regional characteristics relative to target objects, this will bring inevitable difficulties to sonar image segmentation, the existing segmentation methods will easily take shadow part as the target object, leading to a segmentation mistake that exporting shadow part as the segmentation result, therefore, how to obtain a more accurate segmentation method is still a research focus in sonar image segmentation field. The existing common shadow removal methods is the preprocessing method based on histogram equalization or gray threshold transform, but the result is unsatisfied, and moreover, the original image information may be broken at the same time when removing the shadow part [9]. To avoid the adverse impact of image shadow, this paper did sonar image preprocessing by morphological operations in view of the application characteristics of morphological operations such as enhancement and compensation for image [10]. 3.2.Design Steps Firstly, uneven background brightness can be compensated through morphological open operation, and the entire image background estimate can be obtained by doing open operation for the image of appropriate structure elements. Open operation is recorded as I b , then the open operation of structure element b(i , j ) to image I (i, j ) is defined as:
I b (I
b) b
Where the symbol and are respectively expressed as the dilation and erosion operations of structural element b(i , j ) to image I (i, j ) , and the formula is as follows:
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(I
b)(i, j) max{I (i i , j
j ) b(i , j ) | (i , j ) Db }
Where Db is the domain of structural element b(i , j ) . Open operation can remove details brighter than structural element, keep grey value and large bright area in the image basically unchanged, and get a uniform background estimate. Then the image with uniform background can be generated by subtracting the estimated background, and the shadow effect can be eliminated, this process is called morphological top-hat transformation, and the formula is as follows:
g
I (I b)
Where g is the output image. The shadow part of image can be generally covered and integrated with the original background through top-hat transformation, so the target area instead of shadow part can be extracted in sonar image segmentation system. On the other hand, sonar image often have low contrast, serious noise pollution, fuzzy edge due to the imaging principle, the limit of actual experimental instruments and the restriction of underwater environment condition. Therefore, this paper takes top-hat transformation and bottom-hat transformation together to do sonar image enhancement, and the bottom hat operation is defined as:
g
I (I b)
Where I b is close operation, the formula of close operation of structure element b(i , j ) to image I (i, j ) is as follows:
I b (I
b) b
Close operation can remove the smaller dark details than structural element, keep grey value and large dark area in the image basically unchanged. After the implementation of the preprocessing based on morphological operations, the image shadow has been removed and the edge has been enhanced, then level set without re-initialization will be carried on and the improved level set sonar image segmentation method will be finally obtained. 4.Experiment In the following simulation experiment, we selected a representative sonar image of underwater ship showed in Fig. 4, it is can be seen that the most obvious characteristic of this image is the shadow part which occupies large area of the image, then carried on image segmentation by traditional level set method and improved level set method to verify the effectiveness and feasibility of the proposed method. The experimental results are shown in Fig. 5 and Fig. 6.
Guangyu Liu et al. / Physics Procedia 33 (2012) 1168 – 1175 Figure 3. The originalsonar image of underwater ship.
Fig. 5 is the segmentation result by the traditional level set method, it can be seen that the traditional level set method made shadow to be exported out at the same time with ship as one part of the foreground object. Apparently it is a segmentation mistake, so the existence of shadow in a sonar image brings a lot of negative effects and increases the segmentation difficulty. Fig. 6 is the segmentation result by the improved level set method proposed in this paper. It can be seen that our method is obviously superior to the traditional level set segmentation method. The proposed level set method combining the morphological preprocessing has only segmented the target foreground without the shadow part and its accuracy is improved significantly. Therefore, compared with the traditional level set segmentation method, the proposed algorithm in this paper is more suitable for sonar image segmentation, and the segmentation performance is much more improved. Final contour, 400 iterations
20
40
60
80
100
120 20
40
60
80
100
120
Figure 4. The segmentation result by the traditional level set method. Final contour, 400 iterations
20
40
60
80
100
120 20
40
60
80
100
120
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Figure 5. The segmentation result by the improved level set method.
5.Conclusion The current widely used level set segmentation method for optical image can not accurately segment sonar image because of extremely different characteristics of sonar image. This paper proposed a new method to improve the traditional level set segmentation method. The biggest problem of traditional segmentation method for sonar image is the negative impact of shadow part in the image, to solve this problem, this paper carried on morphological preprocessing to sonar image before segmentation and constructed a sonar image segmentation system based on the improved level set without re-initialization method. The simulation experimental results demonstrate the feasibility and effectiveness of the proposed method. Acknowledgment The project is sponsored by Natural Science Foundation of China (No. 50909025 and No. 60772128). This paper is funded by the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation. References [1]
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