基于活动轮廓模型的图像分割
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摘要
图像分割是对图像进行区域划分的处理过程,通常作为一种基础性操作为更高层的图像处理与计算机视觉操作提供准备性工作。近年来,基于活动轮廓模型的图像分割算法凭借其多样的形式、灵活的结构以及优越的性能受到了国内外学者的广泛关注,本文针对这一类图像分割算法进行了较为深入的研究。根据活动轮廓模型轮廓线的表示形式的不同,本文将研究内容分为以下三个部分:
首先,研究了基于参数活动轮廓模型的图像分割算法。参数活动轮廓模型采用点集或B-样条曲线来表示模型的轮廓线,具有运算效率高的特点。本文介绍了最为经典的参数活动轮廓模型——Snake模型,及其改进模型——气球Snake模型和GVF Snake模型。通过结合气球Snake模型和GVF Snake模型的优点,提出了GVF-Balloon Snake模型,实验表明该新模型在图像分割中既保持了GVF Snake模型的双向运动的特点,同时,在提取形状比较复杂的目标边界时又具有气球Snake模型优异的性能。
其次,研究了基于传统水平集活动轮廓模型的图像分割算法。传统水平集活动轮廓模型采用传统水平集函数的零水平集来表示模型的轮廓线,其中传统水平集函数被设置为关于其零水平集的符号距离函数。传统水平集活动轮廓模型具有自动处理轮廓线拓扑变化的优点。本文介绍了三种经典的传统水平集活动轮廓模型,即几何活动轮廓模型、测地活动轮廓模型和Chan-Vese模型。在此基础上,分别提出了有向测地活动轮廓模型和双重Chan-Vese模型,实验表明这两个改进模型均在一定程度上提高了原始的经典模型在图像分割中的性能。并且,本文还将基于形状的Chan-Vese模型成功地应用于圆形目标的分割,即圆检测。
最后,研究了基于二值水平集活动轮廓模型的图像分割算法。二值水平集活动轮廓模型采用二值水平集函数的分界线来表示模型的轮廓线,其中二值水平集函数被设置为仅取1和-1的二值函数。二值水平集活动轮廓模型既可以自动地处理轮廓线的拓扑变化,又具有较高的运算效率。本文介绍了Tai等人提出的基于区域的二值水平集活动轮廓模型。针对Tai等人所提出的模型丧失了曲线演化的渐进性这一缺点,本文提出了一种改进的基于区域的二值水平集活动轮廓模型,该改进模型的轮廓线能够以逐渐演化的方式向目标边界运动,从而保持了传统水平集活动轮廓模型轮廓线的运动方式。并且,在几何活动轮廓模型的框架下,提出一种新的二值水平集活动轮廓模型,该新模型将二值水平集方法的应用从基于区域的图像分割进一步推广到基于边界的图像分割。
Image segmentation is the process that divides the whole image region into several parts. As a fundamental operation, image segmentation provides the preparative works for the high-level operations in image processing and computer vision. Recently, the segmentation algorithms based on active contours have been widely paid attention by many internal and foreign researchers due to their variable form, flexible structure and excellent performance, this paper has performed in-depth researches on the kind of segmentation algorithms. According to the representation of active contours, the content of this paper is partitioned into the following three parts:
Firstly, this paper has studied the segmentation algorithms based on parametric active contours. Parametric active contours represent their contours by the point sets or B-splines, and they have the efficient performance on image segmentation. This paper has introduced the most classical parametric active contour, i.e., snake model, and its improved models, i.e., balloon snake model and GVF snake model. Through combining the good properties of balloon snake model and GVF snake model, this paper has proposed GVF-balloon snake model. The segmental experiments show that the proposed model not only preserves the GVF snake model’s property of bidirectional motion, but also has the excellent performance on extracting the objects with complex shapes like the balloon snake model.
Secondly, this paper has studied the segmentation algorithms based on traditional level set active contours. Traditional level set active contours represent their contours by the zero level sets of the level set functions, which is set to be signed distance functions. Traditional level set active contours have the ability of automatically handling the topology change. This paper has introduced three classical traditional level set active contours, i.e., geometric active contour, geodesic active contour and Chan-Vese model. Based on these works, we propose directional geodesic active contour and dual Chan-Vese model, respectively. The experiments conducted on image segmentation show that the two proposed models improve the performances of the original models. Moreover, this paper has applied the shape based Chan-Vese model to the extraction of circular objects, i.e., circle detection.
Lastly, this paper has studied the segmentation algorithms based on binary level set active contours. Binary level set active contours represent their contours by the interface of the binary level set functions, which just take -1 and 1. Binary level set active contours not only have the ability of automatically handling the topology change, but also have high computational efficiency. This paper has introduced the region based binary level set active contour proposed by Tai et al. Aim at the shortcoming that the model proposed by Tai et al lost the gradual property of curve evolution, this paper proposed an improved region based binary level set active contour, the contour of improved model can conform to the object in the gradual evolution form, thus it preserves the motion form of the contour of traditional level set active contours. And, under the framework of geometric active contour, this paper proposed a novel binary level set active contour, which extend the application of the binary level set method from the region based image segmentation to the boundary based image segmentation.
首先,研究了基于参数活动轮廓模型的图像分割算法。参数活动轮廓模型采用点集或B-样条曲线来表示模型的轮廓线,具有运算效率高的特点。本文介绍了最为经典的参数活动轮廓模型——Snake模型,及其改进模型——气球Snake模型和GVF Snake模型。通过结合气球Snake模型和GVF Snake模型的优点,提出了GVF-Balloon Snake模型,实验表明该新模型在图像分割中既保持了GVF Snake模型的双向运动的特点,同时,在提取形状比较复杂的目标边界时又具有气球Snake模型优异的性能。
其次,研究了基于传统水平集活动轮廓模型的图像分割算法。传统水平集活动轮廓模型采用传统水平集函数的零水平集来表示模型的轮廓线,其中传统水平集函数被设置为关于其零水平集的符号距离函数。传统水平集活动轮廓模型具有自动处理轮廓线拓扑变化的优点。本文介绍了三种经典的传统水平集活动轮廓模型,即几何活动轮廓模型、测地活动轮廓模型和Chan-Vese模型。在此基础上,分别提出了有向测地活动轮廓模型和双重Chan-Vese模型,实验表明这两个改进模型均在一定程度上提高了原始的经典模型在图像分割中的性能。并且,本文还将基于形状的Chan-Vese模型成功地应用于圆形目标的分割,即圆检测。
最后,研究了基于二值水平集活动轮廓模型的图像分割算法。二值水平集活动轮廓模型采用二值水平集函数的分界线来表示模型的轮廓线,其中二值水平集函数被设置为仅取1和-1的二值函数。二值水平集活动轮廓模型既可以自动地处理轮廓线的拓扑变化,又具有较高的运算效率。本文介绍了Tai等人提出的基于区域的二值水平集活动轮廓模型。针对Tai等人所提出的模型丧失了曲线演化的渐进性这一缺点,本文提出了一种改进的基于区域的二值水平集活动轮廓模型,该改进模型的轮廓线能够以逐渐演化的方式向目标边界运动,从而保持了传统水平集活动轮廓模型轮廓线的运动方式。并且,在几何活动轮廓模型的框架下,提出一种新的二值水平集活动轮廓模型,该新模型将二值水平集方法的应用从基于区域的图像分割进一步推广到基于边界的图像分割。
Image segmentation is the process that divides the whole image region into several parts. As a fundamental operation, image segmentation provides the preparative works for the high-level operations in image processing and computer vision. Recently, the segmentation algorithms based on active contours have been widely paid attention by many internal and foreign researchers due to their variable form, flexible structure and excellent performance, this paper has performed in-depth researches on the kind of segmentation algorithms. According to the representation of active contours, the content of this paper is partitioned into the following three parts:
Firstly, this paper has studied the segmentation algorithms based on parametric active contours. Parametric active contours represent their contours by the point sets or B-splines, and they have the efficient performance on image segmentation. This paper has introduced the most classical parametric active contour, i.e., snake model, and its improved models, i.e., balloon snake model and GVF snake model. Through combining the good properties of balloon snake model and GVF snake model, this paper has proposed GVF-balloon snake model. The segmental experiments show that the proposed model not only preserves the GVF snake model’s property of bidirectional motion, but also has the excellent performance on extracting the objects with complex shapes like the balloon snake model.
Secondly, this paper has studied the segmentation algorithms based on traditional level set active contours. Traditional level set active contours represent their contours by the zero level sets of the level set functions, which is set to be signed distance functions. Traditional level set active contours have the ability of automatically handling the topology change. This paper has introduced three classical traditional level set active contours, i.e., geometric active contour, geodesic active contour and Chan-Vese model. Based on these works, we propose directional geodesic active contour and dual Chan-Vese model, respectively. The experiments conducted on image segmentation show that the two proposed models improve the performances of the original models. Moreover, this paper has applied the shape based Chan-Vese model to the extraction of circular objects, i.e., circle detection.
Lastly, this paper has studied the segmentation algorithms based on binary level set active contours. Binary level set active contours represent their contours by the interface of the binary level set functions, which just take -1 and 1. Binary level set active contours not only have the ability of automatically handling the topology change, but also have high computational efficiency. This paper has introduced the region based binary level set active contour proposed by Tai et al. Aim at the shortcoming that the model proposed by Tai et al lost the gradual property of curve evolution, this paper proposed an improved region based binary level set active contour, the contour of improved model can conform to the object in the gradual evolution form, thus it preserves the motion form of the contour of traditional level set active contours. And, under the framework of geometric active contour, this paper proposed a novel binary level set active contour, which extend the application of the binary level set method from the region based image segmentation to the boundary based image segmentation.
引文
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110 J. Lie, M. Lysaker, X.-C. Tai. A Variant of the Level Set Method and Applications to Image Segmentation. Mathematics of Computation. 2006, 75(255): 1155~1174
111 X.-C. Tai, O. Christiansen, P. Lin, I. Skj?laaen. Image Segmentation UsingSome Piecewise Constant Level Set Methods with MBO Type of Project. International Journal of Computer Vision. 2007, 73(1): 61~76
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113 S. P. Awate, T. Tasdizen, R. T. Whitaker. Unsupervised Texture Segmentation with Nonparametric Neighborhood Statistics. Proceedings of the Ninth European Conference on Computer Vision. 2006, 2: 494~507
114 C. Xu, D. L. Pham, J. L. Prince. Medical Image Segmentation Using Deformable Models. Handbook of Medical Imaging-Vol. 2: Medical Image Processing and Analysis. SPIE Press. 2000, 2: 129~174
115 周昌雄. 基于活动轮廓模型的图像分割方法研究. 南京航空航天大学博士论文. 2005
116 N. Ray, S. T. Acton, Motion Gradient Vector Flow: An External Force for Tracking Rolling Leukocytes with Shape and Size Constrained Active Contours. IEEE Transactions on Medical Imaging. 2004, 23(12): 1466~1478
117 N. Paragios, O. Mellina-Gottardo, V. Ramesh. Gradient Vector Flow Fast Geometric Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2004, 26(3): 402~407
118 G. Sapiro. Geometric Partial Differential Equations and Image Analysis. Cambridge University Press, Cambridge. 2001
119 J. A. Sethian. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision and Materials Sciences. Cambridge, U.K.: Cambridge University Press. 1966
120 D. Adalsteinsson, J. A. Sethian. A Fast Level Set Method for Propagating Interfaces. Journal of Computational Physics. 1995, 118(2): 269~277
121 J. A. Sethian. A Marching Level Set Method for Monotonically Advancing Fronts. Proceedings of the National Academy of Sciences. 1996, 93(4): 1591~1595
122 H. W. Park, T. Schoepflin, Y. Kim. Active Contour Model with Gradient Directional Information: Directional Snake. Transactions on Circuits and Systems for Video Technology. 2001, 11(2): 252~256
123 M. Jacob, T. Blu, M. Unser. Efficient Energies and Algorithms for Parametric Snakes. IEEE Transactions on Image Processing. 2004, 13(9): 1231~1244
124 R. Kimmel, A. M. Bruckstein. Regularized Laplacian Zero Crossings as Optimal Edge Integrators. International Journal of Computer Vision. 2003, 53(3): 225~243
125 S. R. Gunn, M. S. Nixon. A Robust Snake Implementation: A Dual Active Contour. Transactions on Pattern Analysis and Machine Intelligence. 1997, 19(1): 63~68
126 T. Chan, W. Zhu. Level Set Based Shape Prior Segmentation. Proceedings of the 10th International Conference on Computer Vision. 2005, 1164~1170
127 M. T. El-Melegy, N. H. Al-Ashwal, A. A. Farag. Variational-Based Method to Extract Parametric Shapes from Images. Proceedings of the 10th International Conference on Computer Vision. 2005, 1786~1791
128 P.-L. Lions, B. Mercier. Splitting Algorithms for the Sum of Two Monotone Operators. SIAM Journal on Numerical Analysis. 1979, 16(6): 964~79
129 G. Marchuk. Splitting and Alternating Direction Methods. Handbook of Numerical Analysis. Vol. I, Finite Difference Methods (Part 1). 1990, 197~462