Research on Soil Pore Segmentation of CT Images Based on MMLFR-UNet Hybrid Network

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Detalles Bibliográficos
Publicado en:	Agronomy vol. 15, no. 5 (2025), p. 1170
Autor principal:	Qin Changfeng
Otros Autores:	Zhang, Jie, Duan, Yu, Li, Chenyang, Dong Shanzhi, Mu, Feng, Chi Chengquan, Han, Ying
Publicado:	MDPI AG
Materias:	Hainan Island Image resolution Decoding Segmentation Task complexity Soil water Gas exchange Image processing Soil environment Deep learning Accuracy Soil sciences Moisture content Image reconstruction Fourier transforms Computed tomography Image segmentation Permeability Nutrient cycles Soil columns Algorithms Image acquisition Image quality Robustness (mathematics) Morphology
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Descripción
Resumen:	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.
ISSN:	2073-4395
DOI:	10.3390/agronomy15051170
Fuente:	Agriculture Science Database