Improving image segmentation by using energy function based on mixture of Gaussian pre-processing |
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| Affiliation: | 1. Department of Applied Mathematics, University of Tarbiat Modares, P.O. Box 14115-175, Tehran, Iran;2. Department of Computer Science, University of Tarbiat Modares, P.O. Box 14115-175, Tehran, Iran;1. Department of Mathematics, Shanghai Jiao Tong University, China;2. Department of Mathematics, Tongji University, China;3. Department of Mathematics, Hong Kong Baptist University, Hong Kong;1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116023, China;2. Operation Software and Simulation Institute, Dalian Navy Academy, Dalian 116018, China;1. National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China;2. School of Computer Science and Technology, Xidian University, Xi’an 710071, China;1. School of Telecommunication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, PR China;2. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an 710049, PR China;1. College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China;2. Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan |
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| Abstract: | In this paper, by proposing a two-stage segmentation method based on active contour model, we improve the procedure of former image segmentation methods. The first stage of our method is computing weights, means and variances of image by utilizing Mixture of Gaussian distribution which parameters are obtained from EM-algorithm. Once they are obtained, in the second stage, by incorporating level set method for minimizing energy function, the segmentation is achieved. We use an adaptive direction function to make the curve evolution robust against the curves initial position and a nonlinear adaptive velocity to speed up the process of curve evolution and also a probability-weighted edge and region indicator function to implement a robust segmentation for objects with weak boundaries. The paper consists of minimizing a functional containing a penalty term in an attempt to maintain the signed distance property in the entire domain and an external energy term such that it achieves a minimum when the zero level set of the function is located at desired position. |
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| Keywords: | Active contour Image segmentation Level set Gaussian mixture distribution EM-algorithm Pre-processing |
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