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

Gorde:
Argitaratua izan da:Agronomy vol. 15, no. 5 (2025), p. 1170
Egile nagusia: Qin Changfeng
Beste egile batzuk: Zhang, Jie, Duan, Yu, Li, Chenyang, Dong Shanzhi, Mu, Feng, Chi Chengquan, Han, Ying
Argitaratua:
MDPI AG
Gaiak:
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
Sarrera elektronikoa:Citation/Abstract
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Laburpena: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
Baliabidea:Agriculture Science Database