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A large-scale performance study of entropy-based image thresholding techniques using new SAD metric

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Abstract

The paper presents a novel method to measure the performance of entropy-based image thresholding techniques using a new Sum of Absolute value of Differences (SAD) metric in the absence of ground-truth images. The metric is further applied to estimate the parameters of generalized Renyi, Tsallis, Masi entropy measures and the optimal threshold automatically from the image histogram. This leads to a new entropy-based image thresholding algorithm with three variants—one for each generalized entropy. The SAD metric and proposed method are first validated using ground-truth images HYTA dataset. The SAD metric is compared with misclassification error metric, Jaccard and SSIM indices and is found to exhibit consistent behavior. It is further observed that the proposed new method with SAD metric produces same or less misclassification errors than the older algorithms. Inspired by the success of the results, a large-scale performance analysis of 8 image thresholding algorithms over diverse datasets containing 621 images is carried out. The investigation reveals that the variant of the new algorithm with Tsallis, Renyi and Masi entropies segment images better than others.

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Authors and Affiliations

  1. Department of Computer Science, South Asian University, New Delhi, 110021, India

    Hadi Mohammadi, Sargam Gupta & Shachi Sharma

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  1. Hadi Mohammadi
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  2. Sargam Gupta
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  3. Shachi Sharma
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Correspondence to Shachi Sharma.

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Mohammadi, H., Gupta, S. & Sharma, S. A large-scale performance study of entropy-based image thresholding techniques using new SAD metric. Pattern Anal Applic 26, 473–486 (2023). https://doi.org/10.1007/s10044-022-01121-z

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