Fast MR brain image segmentation based on modified Shuffled Frog Leaping Algorithm

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• 作者：Anis Ladgham ; Fay?al Hamdaoui ; Anis Sakly…
• 关键词：SFLA optimization ; MSFLA ; Fitness function ; MR image segmentation
• 刊名：Signal, Image and Video Processing
• 出版年：2015
• 出版时间：July 2015
• 年：2015
• 卷：9
• 期：5
• 页码：1113-1120
• 全文大小：4,925 KB
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• 作者单位：Anis Ladgham (1)
  Fay?al Hamdaoui (1)
  Anis Sakly (2) (3)
  Abdellatif Mtibaa (1) (2)
  
  1. Laboratory EμE, Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia
  2. Department of electrical engineering, National Engineering School of Monastir, University of Monastir, Monastir, Tunisia
  3. Reseach Unit: Industrial Systems Study and Renewable Energy (ESIER), National Engineering School of Monastir (ENIM), University of Monastir, Monastir, Tunisia
  
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing
  Image Processing and Computer Vision
  Computer Imaging, Vision, Pattern Recognition and Graphics
  Multimedia Information Systems
  
• 出版者：Springer London
• ISSN：1863-1711

文摘

Due to the need of correct diseases analysis, MR image segmentation remains till now a challenging problem, especially in the presence of random noise. This paper proposes a new meta-heuristic algorithm for MR brain image segmentation, named Modified Shuffled Frog Leaping Algorithm (MSFLA), based on the technique of Shuffled Frog Leaping Algorithm (SFLA). In this new paradigm, there is no need to filter the original image. The new fitness function proposed in our algorithm helps to evaluate quickly the particle frogs in order to arrange them in descending order. The proposed approach has been compared with other meta-heuristics such as 3D-Otsu thresholding with SFLA and Genetic Algorithm (GA) and also with the algorithm of segmentation using the Rician Classifier (RiCE). Experimental results show that the proposed MSFLA is able to achieve better segmentation quality and execution time than the latest methods.