ArticleEntropy (Basel, Switzerland)2021
Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Harmonic Aggregation Entropy: A Highly Discriminative Harmonic Feature Estimator for Time Series.Entropy (Basel, Switzerland) · 2025Article
- Image statistics substantiate Gaudí's naturalistic design principles.Scientific reports · 2025Article
- Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer Images.Entropy (Basel, Switzerland) · 2025Article
- EspEn Graph for the Spatial Analysis of Entropy in Images.Entropy (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
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Abstract
Image processing has played a relevant role in various industries, where the main challenge is to extract specific features from images. Specifically, texture characterizes the phenomenon of the occurrence of a pattern along the spatial distribution, taking into account the intensities of the pixels for which it has been applied in classification and segmentation tasks. Therefore, several feature extraction methods have been proposed in recent decades, but few of them rely on entropy, which is a measure of uncertainty. Moreover, entropy algorithms have been little explored in bidimensional data. Nevertheless, there is a growing interest in developing algorithms to solve current limits, since Shannon Entropy does not consider spatial information, and SampEn2D generates unreliable values in small sizes. We introduce a proposed algorithm, EspEn (Espinosa Entropy), to measure the irregularity present in two-dimensional data, where the calculation requires setting the parameters as follows:
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