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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-07

AUTHORS

Anis Ladgham, Fayçal Hamdaoui, Anis Sakly, Abdellatif Mtibaa

ABSTRACT

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. More... »

PAGES

1113-1120

References to SciGraph publications

  • 2009-04. A novel hybrid multi-objective shuffled frog-leaping algorithm for a bi-criteria permutation flow shop scheduling problem in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2008. Fully Bayesian Joint Model for MR Brain Scan Tissue and Structure Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2008
  • 2004. Automatic Segmentation of Neonatal Brain MRI in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2004
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11760-013-0546-y

    DOI

    http://dx.doi.org/10.1007/s11760-013-0546-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1000285001


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