Research on Soil Pore Segmentation of CT Images Based on MMLFR-UNet Hybrid Network
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| Publicado en: | Agronomy vol. 15, no. 5 (2025), p. 1170 |
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| Otros Autores: | , , , , , , |
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Qin Changfeng |u School of Information Science and Technology, Hainan Normal University, Haikou 571158, China; qcf1428@gmail.com (C.Q.); z15882515884@gmail.com (J.Z.); lhua7430@gmail.com (Y.D.); lcy5653@163.com (C.L.); dongshanzhi204730@gmail.com (S.D.); mf17339680919@163.com (F.M.) | |
| 245 | 1 | |a Research on Soil Pore Segmentation of CT Images Based on MMLFR-UNet Hybrid Network | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Accurate segmentation of soil pore structure is crucial for studying soil water migration, nutrient cycling, and gas exchange. However, the low-contrast and high-noise CT images in complex soil environments cause the traditional segmentation methods to have obvious deficiencies in accuracy and robustness. This paper proposes a hybrid model combining a Multi-Modal Low-Frequency Reconstruction algorithm (MMLFR) and UNet (MMLFR-UNet). MMLFR enhances the key feature expression by extracting the image low-frequency signals and suppressing the noise interference through the multi-scale spectral decomposition, whereas UNet excels in the segmentation detail restoration and complexity boundary processing by virtue of its coding-decoding structure and the hopping connection mechanism. In this paper, an undisturbed soil column was collected in Hainan Province, China, which was classified as Ferralsols (FAO/UNESCO), and CT scans were utilized to acquire high-resolution images and generate high-quality datasets suitable for deep learning through preprocessing operations such as fixed-layer sampling, cropping, and enhancement. The results show that MMLFR-UNet outperforms UNet and traditional methods (e.g., Otsu and Fuzzy C-Means (FCM)) in terms of Intersection over Union (IoU), Dice Similarity Coefficients (DSC), Pixel Accuracy (PA), and boundary similarity. Notably, this model exhibits exceptional robustness and precision in segmentation tasks involving complex pore structures and low-contrast images. | |
| 651 | 4 | |a Hainan Island | |
| 653 | |a Image resolution | ||
| 653 | |a Decoding | ||
| 653 | |a Segmentation | ||
| 653 | |a Task complexity | ||
| 653 | |a Soil water | ||
| 653 | |a Gas exchange | ||
| 653 | |a Image processing | ||
| 653 | |a Soil environment | ||
| 653 | |a Deep learning | ||
| 653 | |a Accuracy | ||
| 653 | |a Soil sciences | ||
| 653 | |a Moisture content | ||
| 653 | |a Image reconstruction | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Computed tomography | ||
| 653 | |a Image segmentation | ||
| 653 | |a Permeability | ||
| 653 | |a Nutrient cycles | ||
| 653 | |a Soil columns | ||
| 653 | |a Algorithms | ||
| 653 | |a Image acquisition | ||
| 653 | |a Image quality | ||
| 653 | |a Robustness (mathematics) | ||
| 653 | |a Morphology | ||
| 700 | 1 | |a Zhang, Jie |u School of Information Science and Technology, Hainan Normal University, Haikou 571158, China; qcf1428@gmail.com (C.Q.); z15882515884@gmail.com (J.Z.); lhua7430@gmail.com (Y.D.); lcy5653@163.com (C.L.); dongshanzhi204730@gmail.com (S.D.); mf17339680919@163.com (F.M.) | |
| 700 | 1 | |a Duan, Yu |u School of Information Science and Technology, Hainan Normal University, Haikou 571158, China; qcf1428@gmail.com (C.Q.); z15882515884@gmail.com (J.Z.); lhua7430@gmail.com (Y.D.); lcy5653@163.com (C.L.); dongshanzhi204730@gmail.com (S.D.); mf17339680919@163.com (F.M.) | |
| 700 | 1 | |a Li, Chenyang |u School of Information Science and Technology, Hainan Normal University, Haikou 571158, China; qcf1428@gmail.com (C.Q.); z15882515884@gmail.com (J.Z.); lhua7430@gmail.com (Y.D.); lcy5653@163.com (C.L.); dongshanzhi204730@gmail.com (S.D.); mf17339680919@163.com (F.M.) | |
| 700 | 1 | |a Dong Shanzhi |u School of Information Science and Technology, Hainan Normal University, Haikou 571158, China; qcf1428@gmail.com (C.Q.); z15882515884@gmail.com (J.Z.); lhua7430@gmail.com (Y.D.); lcy5653@163.com (C.L.); dongshanzhi204730@gmail.com (S.D.); mf17339680919@163.com (F.M.) | |
| 700 | 1 | |a Mu, Feng |u School of Information Science and Technology, Hainan Normal University, Haikou 571158, China; qcf1428@gmail.com (C.Q.); z15882515884@gmail.com (J.Z.); lhua7430@gmail.com (Y.D.); lcy5653@163.com (C.L.); dongshanzhi204730@gmail.com (S.D.); mf17339680919@163.com (F.M.) | |
| 700 | 1 | |a Chi Chengquan |u School of Information Science and Technology, Hainan Normal University, Haikou 571158, China; qcf1428@gmail.com (C.Q.); z15882515884@gmail.com (J.Z.); lhua7430@gmail.com (Y.D.); lcy5653@163.com (C.L.); dongshanzhi204730@gmail.com (S.D.); mf17339680919@163.com (F.M.) | |
| 700 | 1 | |a Han, Ying |u College of Geography and Environmental Science, Hainan Normal University, Haikou 571158, China | |
| 773 | 0 | |t Agronomy |g vol. 15, no. 5 (2025), p. 1170 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
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