Title : 
Improved image segmentation method based on optimized threshold using Genetic Algorithm
         
        
            Author : 
Zhao, Xin ; Lee, Myung-Eun ; Kim, Soo-Hyung
         
        
            Author_Institution : 
Chonnam Nat. Univ., Gwangju
         
        
        
            fDate : 
March 31 2008-April 4 2008
         
        
        
        
            Abstract : 
In image segmentation, threshold segmentation is becoming more and more widely used because of its simplicity and efficiency. In this paper, an improved image segmentation method based on optimized threshold using genetic algorithm is proposed. Compared with the traditional threshold segmentation methods, this method has advantages that it can nicely segment the thin and it can efficiently reduce calculation time and it has good capability and stabilization nature. The results show that using this proposed method can obtain satisfactory segmentation effect.
         
        
            Keywords : 
genetic algorithms; image segmentation; genetic algorithm; image segmentation; optimized threshold; threshold segmentation; Biological cells; Entropy; Genetic algorithms; Genetic mutations; Image segmentation; Iterative algorithms; Iterative methods; Optimization methods; Random number generation; Statistics;
         
        
        
        
            Conference_Titel : 
Computer Systems and Applications, 2008. AICCSA 2008. IEEE/ACS International Conference on
         
        
            Conference_Location : 
Doha
         
        
            Print_ISBN : 
978-1-4244-1967-8
         
        
            Electronic_ISBN : 
978-1-4244-1968-5
         
        
        
            DOI : 
10.1109/AICCSA.2008.4493645