Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer Images
Entropy algorithms are widely applied in signal analysis to quantify the irregularity of data. In the realm of two-dimensional data, their two-dimensional forms play a crucial role in analyzing images. Previous works have demonstrated the effectiveness of one-dimensional increment entropy in detecti...
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MDPI AG
2025-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/1/80 |
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| author | Muqaddas Abid Muhammad Suzuri Hitam Rozniza Ali Hamed Azami Anne Humeau-Heurtier |
| author_facet | Muqaddas Abid Muhammad Suzuri Hitam Rozniza Ali Hamed Azami Anne Humeau-Heurtier |
| author_sort | Muqaddas Abid |
| collection | DOAJ |
| description | Entropy algorithms are widely applied in signal analysis to quantify the irregularity of data. In the realm of two-dimensional data, their two-dimensional forms play a crucial role in analyzing images. Previous works have demonstrated the effectiveness of one-dimensional increment entropy in detecting abrupt changes in signals. Leveraging these advantages, we introduce a novel concept, two-dimensional increment entropy (IncrEn2D), tailored for analyzing image textures. In our proposed method, increments are translated into two-letter words, encoding both the size (magnitude) and direction (sign) of the increments calculated from an image. We validate the effectiveness of this new entropy measure by applying it to MIX<sub>2<i>D</i></sub>(<i>p</i>) processes and synthetic textures. Experimental validation spans diverse datasets, including the Kylberg dataset for real textures and medical images featuring colon cancer characteristics. To further validate our results, we employ a support vector machine model, utilizing multiscale entropy values as feature inputs. A comparative analysis with well-known bidimensional sample entropy (SampEn<sub>2<i>D</i></sub>) and bidimensional dispersion entropy (DispEn<sub>2<i>D</i></sub>) reveals that IncrEn<sub>2<i>D</i></sub> achieves an average classification accuracy surpassing that of other methods. In summary, IncrEn<sub>2<i>D</i></sub> emerges as an innovative and potent tool for image analysis and texture characterization, offering superior performance compared to existing bidimensional entropy measures. |
| format | Article |
| id | doaj-art-d23c34cfd6dc41adbce2e372144d77e0 |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Entropy |
| spelling | doaj-art-d23c34cfd6dc41adbce2e372144d77e02025-01-24T13:31:55ZengMDPI AGEntropy1099-43002025-01-012718010.3390/e27010080Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer ImagesMuqaddas Abid0Muhammad Suzuri Hitam1Rozniza Ali2Hamed Azami3Anne Humeau-Heurtier4Faculty of Computer Science and Mathematics, Universiti Malaysia Terengganu, Kuala Terengganu 21030, MalaysiaFaculty of Computer Science and Mathematics, Universiti Malaysia Terengganu, Kuala Terengganu 21030, MalaysiaFaculty of Computer Science and Mathematics, Universiti Malaysia Terengganu, Kuala Terengganu 21030, MalaysiaCentre for Addiction and Mental Health, University of Toronto, Toronto, ON M6J 1H1, CanadaLARIS, SFR MATHSTIC, Univ Angers, F-49000 Angers, FranceEntropy algorithms are widely applied in signal analysis to quantify the irregularity of data. In the realm of two-dimensional data, their two-dimensional forms play a crucial role in analyzing images. Previous works have demonstrated the effectiveness of one-dimensional increment entropy in detecting abrupt changes in signals. Leveraging these advantages, we introduce a novel concept, two-dimensional increment entropy (IncrEn2D), tailored for analyzing image textures. In our proposed method, increments are translated into two-letter words, encoding both the size (magnitude) and direction (sign) of the increments calculated from an image. We validate the effectiveness of this new entropy measure by applying it to MIX<sub>2<i>D</i></sub>(<i>p</i>) processes and synthetic textures. Experimental validation spans diverse datasets, including the Kylberg dataset for real textures and medical images featuring colon cancer characteristics. To further validate our results, we employ a support vector machine model, utilizing multiscale entropy values as feature inputs. A comparative analysis with well-known bidimensional sample entropy (SampEn<sub>2<i>D</i></sub>) and bidimensional dispersion entropy (DispEn<sub>2<i>D</i></sub>) reveals that IncrEn<sub>2<i>D</i></sub> achieves an average classification accuracy surpassing that of other methods. In summary, IncrEn<sub>2<i>D</i></sub> emerges as an innovative and potent tool for image analysis and texture characterization, offering superior performance compared to existing bidimensional entropy measures.https://www.mdpi.com/1099-4300/27/1/80biomedical imagingmultiscale increment entropytexture analysistexture irregularitytwo-dimensional increment entropy |
| spellingShingle | Muqaddas Abid Muhammad Suzuri Hitam Rozniza Ali Hamed Azami Anne Humeau-Heurtier Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer Images Entropy biomedical imaging multiscale increment entropy texture analysis texture irregularity two-dimensional increment entropy |
| title | Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer Images |
| title_full | Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer Images |
| title_fullStr | Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer Images |
| title_full_unstemmed | Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer Images |
| title_short | Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer Images |
| title_sort | bidimensional increment entropy for texture analysis theoretical validation and application to colon cancer images |
| topic | biomedical imaging multiscale increment entropy texture analysis texture irregularity two-dimensional increment entropy |
| url | https://www.mdpi.com/1099-4300/27/1/80 |
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