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Simulated Annealing (SA), Tabu Search (TS), Genetic Algorithm (GA), and Ant Colony System (ACS) are four of the main algorithms for solving challenging problems of intelligent systems. In this paper, these four techniques and three novel hybrid combinations of them are proposed to mammogram segmentation. The novel hybrid algorithms consist of a Sequential TS-ACS, a Hybrid ACS/TS, and a Sequential...
Magnetic Resonance Imaging (MRI) is one of the best technologies currently being used for diagnosing brain tumor. Brain tumor is diagnosed at advanced stages with the help of the MRI image. Segmentation is an important process to extract suspicious region from complex medical images. Automatic detection of brain tumor through MRI can provide the valuable outlook and accuracy of earlier brain tumor...
In this paper, mathematical modeling of digital watermarking is proposed to approximate the image based on the generalized Gaussian distribution. Using maximum a posteriori probability based image segmentation and fuzzy c means image segmentation, the cover image is segmented into several homogeneous areas. In EM segmentation, every region in the image is represented by a generalized Gaussian distribution...
Combining the gray histogram of images, using the maximal cross-entropy function as the fitness function of Adaptive Genetic Algorithm, adopting Adaptive Genetic Algorithm to search the optimal threshold function, a multi-target image segmentation algorithm is put forward based on the improved PCNN model.It can effectively complete image segmentation, and the results are superior to the Ostu multi-threshold...
In this paper, linking with the basic principle of FCM algorithm, on the basis of theory research, a method of the cluster analysis that FCM and the genetic algorithm are combined together is proposed. Firstly, the approximate optimal solution obtained by the genetic algorithm is taken as the original value of the FCM algorithm, then carrying on the local search to obtain the global optimal solution,...
Image segmentation plays an important and basic role in image processing and pattern recognition. Its purpose is to separate areas that do not superpose each other and to obtain the interested target. During the past few years many algorithms for image segmentation have been proposed. The popular technique is the threshold segmentation because of its simplicity and efficiency. Genetic algorithm is...
In this paper, we describe a segmentation method for brain MR images using an ant colony optimization (ACO) algorithm. This is a relatively new meta-heuristic algorithm and a successful paradigm of all the algorithms which take advantage of the insectpsilas behavior. It has been applied to solve many optimization problems with good discretion, parallel, robustness and positive feedback. As an advanced...
Edge detection of images is a classical problem in computer vision and image processing. The key of edge detection is the choice of threshold; the choice of threshold directly determines the results of edge detection. How to automatically determine an optimal threshold is one of difficult points of edge detection. In this paper, Sobel edge detection operator and its improved algorithm are first discussed...
An improved genetic K-means clustering algorithm is proposed and is applied to image segmentation. According to the characteristics of the image, the feature vector of the pixel is properly chosen and the weight factors of the feature vector are adjusted, which enhances the segmentation precision. The selection of conventional genetic algorithm and the modification of mutation operations improve the...
The Ant colony optimization (ACO) algorithm is relatively a new meta-heuristic algorithm and a successful paradigm of all the algorithms which take advantage of the insectpsilas behavior. It has been applied to solve many optimization problems with good discretion, parallel, robustness and positive feedback. As an advanced optimization algorithm, only recently, researchers began to apply ACO to image...
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