Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/10/1261 |
| _version_ | 1797514685609672704 |
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| author | Ricardo Espinosa Raquel Bailón Pablo Laguna |
| author_facet | Ricardo Espinosa Raquel Bailón Pablo Laguna |
| author_sort | Ricardo Espinosa |
| collection | DOAJ |
| description | 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: <i>m</i> (length of square window), <i>r</i> (tolerance threshold), and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ρ</mi></semantics></math></inline-formula> (percentage of similarity). Three experiments were performed; the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters <i>m</i>, <i>r</i>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ρ</mi></semantics></math></inline-formula>. Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data; the recommended parameters for better performance are <i>m</i> = 3, <i>r</i> = 20, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ρ</mi></semantics></math></inline-formula> = 0.7. |
| first_indexed | 2024-03-10T06:35:06Z |
| format | Article |
| id | doaj.art-d475a6c161ff4b75af62245f8e16b874 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T06:35:06Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-d475a6c161ff4b75af62245f8e16b8742023-11-22T18:10:16ZengMDPI AGEntropy1099-43002021-09-012310126110.3390/e23101261Two-Dimensional EspEn: A New Approach to Analyze Image Texture by IrregularityRicardo Espinosa0Raquel Bailón1Pablo Laguna2Department of Biomedical Engineering, Universidad ECCI, Bogotá 111311, ColombiaBiomedical Signal Interpretation & Computational Simulation (BSICoS) Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, 50018 Zaragoza, SpainBiomedical Signal Interpretation & Computational Simulation (BSICoS) Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, 50018 Zaragoza, SpainImage 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: <i>m</i> (length of square window), <i>r</i> (tolerance threshold), and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ρ</mi></semantics></math></inline-formula> (percentage of similarity). Three experiments were performed; the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters <i>m</i>, <i>r</i>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ρ</mi></semantics></math></inline-formula>. Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data; the recommended parameters for better performance are <i>m</i> = 3, <i>r</i> = 20, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ρ</mi></semantics></math></inline-formula> = 0.7.https://www.mdpi.com/1099-4300/23/10/1261image processingtextureentropytwo-dimensional dataEspEnirregularity |
| spellingShingle | Ricardo Espinosa Raquel Bailón Pablo Laguna Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity Entropy image processing texture entropy two-dimensional data EspEn irregularity |
| title | Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity |
| title_full | Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity |
| title_fullStr | Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity |
| title_full_unstemmed | Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity |
| title_short | Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity |
| title_sort | two dimensional espen a new approach to analyze image texture by irregularity |
| topic | image processing texture entropy two-dimensional data EspEn irregularity |
| url | https://www.mdpi.com/1099-4300/23/10/1261 |
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