Abstract
To improve two-dimensional (2D) Otsu thresholding’s performance in both computation speed and segmentation quality, an improved 2D Otsu algorithm is proposed for low Signal-to-noise Ratio (SNR) images. A new 2D histogram is defined based on median gray-scale and Gaussian average gray-scale. By meeting better to the assumption of that the object’s probability and the background’s probability sum up to 1, the new 2D histogram enhances the thresholding algorithm’s robustness to severe noise. Then a scheme of calculating the fitness function based on firefly optimization algorithm is employed to search for optimal thresholds. The proposed algorithm is applied to typical low SNR images–microscopic images of ocean plankton, and to Lenna test image. Experiment results show that with better thresholding quality, the running time of the proposed algorithm is reduced to 2.5% of the conventional 2D Otsu.
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Acknowledgments
This work was supported by the National Scientific Foundation of China (No. 61304108) and the Discipline Guidance Foundation of Harbin Institute of Technology (Weihai) (No. IDOA 1000290131).
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Li, L., Liu, J., Ling, M., Wang, Y., Xia, H. (2017). Improved Two-Dimensional Otsu Based on Firefly Optimization for Low Signal-to-Noise Ratio Images. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_66
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DOI: https://doi.org/10.1007/978-3-319-61824-1_66
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