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The variance entropy multi-level thresholding method

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Abstract

This paper proposes a new multi-level entropy-based image thresholding method. The key principle of the proposed method depends on the minimum of the variance entropy. The method is fully automated at all stages of implementation. It produces competitive segmentation results as compared to the generalized Otsu’s method, which is one of the most powerful multi-level thresholding techniques that requires human intervention. In addition, the method significantly outperforms the generalized Kapur’s method, which is one of the benchmarking entropy-based thresholding techniques. The method is successfully applied to several scenarios of trial histograms and real images, and its performance is checked using a variety of classification measures and quality metrics.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Natural Sciences, Mathematics and Technology Unit, Effat University-College of Engineering, Jeddah, Saudi Arabia

    Omar A. Kittaneh

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  1. Omar A. Kittaneh
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Correspondence to Omar A. Kittaneh.

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Kittaneh, O.A. The variance entropy multi-level thresholding method. Multimed Tools Appl 82, 43075–43087 (2023). https://doi.org/10.1007/s11042-023-15250-y

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