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Multilevel threshold selection for image segmentation using soft computing techniques

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Abstract

Multilevel thresholding is the method applied to segment the given image into unique sub-regions when the gray value distribution of the pixels is not distinct. The segmentation results are affected by factors such as number of threshold and threshold values. Hence, this paper proposes different methods for determining optimal thresholds using optimization techniques namely GA, PSO and hybrid model. Parallel algorithms are also proposed and implemented for these methods to reduce the execution time. From the experimental results, it is inferred that proposed methods take less time for determining the optimal thresholds when compared with existing methods such as Otsu and Kapur methods.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, 620 015, India

    C. Mala & M. Sridevi

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  1. C. Mala
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  2. M. Sridevi
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Correspondence to C. Mala.

Additional information

Communicated by A. Jara, M. R. Ogiela, I. You and F.-Y. Leu.

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Mala, C., Sridevi, M. Multilevel threshold selection for image segmentation using soft computing techniques. Soft Comput 20, 1793–1810 (2016). https://doi.org/10.1007/s00500-015-1677-6

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  • DOI: https://doi.org/10.1007/s00500-015-1677-6

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