Kittaneh, Omar2023-06-042023-06-0420231573-7721http://hdl.handle.net/20.500.14131/886This paper presents a novel image thresholding method based on multi-level entropy. The approach relies on minimizing variance entropy and is fully automated, requiring no human intervention. The method achieves segmentation results that are comparable to those obtained by the highly regarded generalized Otsu's method. Additionally, the proposed method outperforms the benchmarking generalized Kapur's method. The effectiveness of the approach is demonstrated through numerous tests on both simulated and actual images, and its performance is evaluated using various quality metrics and classification measures.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.en-USMTH