Multi-level image thresholding based on Hybrid Differential Evolution algorithm. Application on medical images

M. Ali P. Siarry 1 M. Pant 2
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
Abstract : Image thresholding is definitely one of themost popular segmentation approaches for extracting objects from the background, or for discriminating objects from objects that have distinct gray-levels. It is typically simple and computationally efficient. It is based on the assumption that the objects can be distinguished by their gray levels. The optimal threshold is the one that can separate different objects fromeach other or from the background to such an extent that a decision can bemadewithout further processing [8, 13]. The automatic fitting of this threshold is one of the main challenges of image segmentation. Sezgin and Sankur [18] have presented a survey of a variety of thresholding techniques. There are a lot of approaches classifying thresholdingmethods. Authors in [18] labeled the method according to the information they exploit, such as histogram shape, space measurement clustering, entropy, object attributes, spatial information and local gray-level surface. Another classification approach consists in dividing these techniques into parametric and non-parametric techniques. The parametric thresholding methods exploit the first-order statistical characterization of the image to be segmented. Weszka et al. [16] proposed a parametric method where the gray-level distribution of each class is assumed to be a Gaussian distribution. An attempt to find an estimate of the parameters of the distribution that best fit the given histogram data is made by using the least-squares estimation method. Typically, it leads to a nonlinear optimization problem, its solution is computationally expensive and time consuming. Over the years, many researchers have proposed several algorithms to solve the objective function of Gaussian curve fitting for multi-level
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Submitted on : Thursday, January 11, 2018 - 11:47:33 PM
Last modification on : Thursday, March 14, 2019 - 6:45:37 PM


  • HAL Id : hal-01682077, version 1



M. Ali, P. Siarry, M. Pant. Multi-level image thresholding based on Hybrid Differential Evolution algorithm. Application on medical images. A. Nakib and E-G. Talbi. Metaheuristics for Medicine and Biology, Studies in Computational Intelligence series, Springer, pp.23-36, 2017. ⟨hal-01682077⟩



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