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Volume 7 Issue 1
Jan.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(1): 200-213
Ashish Kumar Bhandari, Arunangshu Ghosh and Immadisetty Vinod Kumar, "A Local Contrast Fusion Based 3D Otsu Algorithm for Multilevel Image Segmentation," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 200-213, Jan. 2020. doi: 10.1109/JAS.2019.1911843
Citation: Ashish Kumar Bhandari, Arunangshu Ghosh and Immadisetty Vinod Kumar, "A Local Contrast Fusion Based 3D Otsu Algorithm for Multilevel Image Segmentation," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 200-213, Jan. 2020. doi: 10.1109/JAS.2019.1911843
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A Local Contrast Fusion Based 3D Otsu Algorithm for Multilevel Image Segmentation

doi: 10.1109/JAS.2019.1911843
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    Abstract
  • To overcome the shortcomings of 1D and 2D Otsu’s thresholding techniques, the 3D Otsu method has been developed. Among all Otsu’s methods, 3D Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image; it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional 1D Otsu, 2D Otsu and 3D Otsu methods, as evident from the objective and subjective evaluations.

     

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