Dynamic Biomedical Image Segmentation Based on Wavelet Transform

2014 ◽  
Vol 571-572 ◽  
pp. 821-824 ◽  
Author(s):  
Zhan Ping Li ◽  
Mo Yuan Yang ◽  
Long Wang
Keyword(s):  
Image Segmentation ◽  
Wavelet Transform ◽  
Experimental Evaluation ◽  
Real Life ◽  
Feature Space ◽  
Segmentation Algorithm ◽  
Biomedical Image ◽  
Segmentation Quality ◽  
Speed Up ◽  
Image Segmentation Algorithm

In this paper, the image segmentation algorithm based on wavelet transform is presented. The proposed image segmentation algorithm performs the segmentation in the combined intensity-texture-position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. This segmentation algorithm is applied to reduced versions of the original images in order to speed-up the completion of the segmentation. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.

Dynamic Image Segmentation for Sport Graphics Based on Wavelet Transform

2014 ◽  
Vol 513-517 ◽  
pp. 3715-3718
Author(s):  
Wen Ge Zhao ◽  
Li Nan Wang
Keyword(s):  
Image Segmentation ◽  
Wavelet Transform ◽  
Experimental Evaluation ◽  
Real Life ◽  
Feature Space ◽  
Segmentation Algorithm ◽  
Dynamic Image ◽  
Segmentation Quality ◽  
Speed Up ◽  
Image Segmentation Algorithm

In this paper, a image segmentation algorithm based on wavelet transform are presented. The proposed image segmentation algorithm performs the segmentation in the combined intensity-texture-position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. This segmentation algorithm is applied to reduced versions of the original images in order to speed-up the completion of the segmentation. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.

Fuzzy C-Mean Clustering Image Segmentation Algorithm Research for Sport Graphics Based on Artificial Life

2012 ◽  
Vol 263-266 ◽  
pp. 2207-2210
Author(s):  
Xiao Liu ◽  
Lei Shi
Keyword(s):  
Image Segmentation ◽  
Artificial Life ◽  
Clustering Algorithm ◽  
Biological Process ◽  
Optimal Solution ◽  
Feature Space ◽  
Segmentation Quality ◽  
Fuzzy C Means Clustering ◽  
Theory Research ◽  
Image Segmentation Algorithm

In this paper, linking with the basic principle of FCM (Fuzzy c-means clustering) algorithm, on the basis of theory research, a method of the cluster analysis of FCM based on artificial life is proposed. The artificial life approach is promising in image processing because it is inherently parallel and coincides with the self-governing biological process. Firstly, the approximate optimal solution obtained by the FCM algorithm is taken as the original value, then combined with intensity-texture-position feature space in order to produce connected regions shown in the image, the final segmentation result is achieved at last. The experiment results prove that in the view of the sport image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

Fuzzy C-Mean Clustering Image Segmentation Algorithm Research for MRI Sport Graphics Based on Genetic Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 3711-3714 ◽  
Author(s):  
Tian Long Ma ◽  
Long Wang ◽  
Dong Wang
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Biological Process ◽  
Optimal Solution ◽  
Feature Space ◽  
Segmentation Quality ◽  
Fuzzy C Means Clustering ◽  
Theory Research ◽  
Image Segmentation Algorithm

In this paper, linking with the basic principle of FCM (Fuzzy c-means clustering) algorithm, on the basis of theory research, a method of the cluster analysis of FCM based on genetic algorithm is proposed. The genetic algorithm is promising in MRI (magnetic resonance image) processing because it is inherently parallel and coincides with the self-governing biological process. Firstly, the approximate optimal solution obtained by the FCM algorithm is taken as the original value, then combined with intensity-texture-position feature space in order to produce connected regions shown in the image, the final segmentation result is achieved at last. The experiment results prove that in the view of the MRI sport image segmentation, this algorithm provides fast segmentation with high perceptual segmentation quality.

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