Identification of grain size in lanthanum-based materials using a computer program

Rizki Syahputra, Zulkarnain Zulkarnain, Rahmi Dewi, Vira Friska, Apriwandi Apriwandi, Agus Khusaeni

Abstract


Lanthanum-based materials are widely used in advanced ceramics, oxides, metallic composites, and thin films due to their superior electrical, magnetic, mechanical, and thermal properties, which are strongly governed by microstructural characteristics, particularly grain size. Accurate and reliable grain size identification is therefore essential for understanding structure–property relationships and optimizing material performance. This review systematically examines computer-based approaches for grain size identification in lanthanum-based materials using scanning electron microscopy (SEM) images. Conventional image processing techniques implemented in MATLAB, including thresholding, morphological operations, and signal-processing methods based on the Radon transform, are discussed alongside modern Python-based machine learning and deep learning frameworks such as U-Net and the Segment Anything Model (SAM). The comparative analysis highlights the strengths and limitations of each approach in handling complex microstructures, low-contrast grain boundaries, and overlapping grains. The reviewed studies demonstrate that while MATLAB-based methods remain effective for relatively simple microstructures, deep learning–based segmentation provides superior robustness and accuracy for complex lanthanum-based systems. Overall, this review emphasizes the growing importance of computer-assisted and deep learning–based methodologies as reliable tools for quantitative grain size characterization in advanced lanthanum-containing materials.

Keywords


Deep learning; MATLAB; Phyton; Radon transform; U-Net

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References


1. Bochenek, D., Brzezińska, D., & Kozielski, L. (2024). The influence of lanthanum admixture on microstructure and electrophysical properties. Mater., 17.

2. Cui, Y., Qu, D., & Guo, Y. (2021). Effect of La2O3 addition on the microstructural evolution and thermomechanical property of sintered low-grade. Ceram. Int., 47(3).

3. Zhu, W. Z., Kholkin, A., & Baptista, J. L. (2001). Effect of lanthanum-doping on the dielectric and piezoelectric properties of PZN-based MPB. J. Mater. Sci., 36(17).

4. Okos, A., Mocioiu, A. M., & Bogdănescu, C. (2025). Hydrothermal synthesis of lithium lanthanum titanate. Crystals, 15(3).

5. Özkan, D. Ç., Türk, A., & Celik, E. (2020). Synthesis and characterizations of sol–gel derived LaFeO3 perovskite powders. J. Mater. Sci.: Mater. Electron., 31(24).

6. Wang, S., Yang, J., & Hu, P. (2025). The stress concentration effect of La2O3 second-phase particles in molybdenum alloys with the dominant mechanisms. Int. J. Refractory Metals Hard Mater., 107617.

7. Kunčická, L. & Kocich, R. (2025). Characterizing the behavior and microstructure of Cu-La2O3 composite processed via equal channel. Metals, 15(4).

8. Yang, S., Zhu, J., & Wang, Y. (2025). SEM image segmentation method for copper microstructures based on enhanced U-Net modeling. Coatings, 15(8), 969.

9. Sylvester, Z., Stockli, D. F., & Bai, W. (2025). Segmenteverygrain: A Python module for segmentation of grains in images. J. Open Source Softw., 10(112).

10. Rathod, K., Choudhary, A. K., & Schneider, G. (2024). GeGra: Approaching a generic model for quantitative grain size analysis from materials microscopy data using deep learning. Mater. Charact., 217.

11. Kirillov, A., Mintun, E., & Girshick, R. (2023). Segment anything. Proc. IEEE/CVF Int. Conf. Comput. Vis., 4015.

12. Goetz, A., Durmaz, A. R., & Eberl, C. (2022). Addressing materials’ microstructure diversity using transfer learning. npj Comput. Mater., 8(1), 27.

13. Paruchuri, A., Thrasher, C., & Jayaraman, A. (2025). Machine learning for identifying grain boundaries in SEM images of nanoparticle superlattices. arXiv preprint arXiv:2501.04172.

14. Ismail, W., Belal, A., & El-Shaer, A. (2024). Investigating the physical and electrical properties of La2O3 via annealing of La(OH)3. Sci. Rep., 14(1).

15. Hameed, A., Asghar, A., & Anwar, H. (2024). A detailed investigation of rare earth lanthanum substitution effects on the structural, morphological, vibrational, optical, dielectric. Front. Chem., 12.

16. Bhardwaj, S., Kumar, S., & Thakur, N. (2025). Effect of lanthanum doping on structural and optical properties of K0. 5Bi0. 5TiO3 ceramics prepared by sol–gel technique. J. Sol-Gel Sci. Technol., 115(3).

17. Mair, D., Witz, G., & Schlunegger, F. (2024). Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large. Earth Surf. Process. Landf., 49(3), 1099.

18. Bordas, A., Zhang, J., & Nino, J. C. (2022). Application of deep learning workflow for autonomous grain size analysis. Molecules, 27(15), 4826.

19. Mélanie, V., Armelle, J., & François, M. (2024). Calibration‐free image analysis method for grain‐size distribution of small natural sand samples. Earth Surf. Process. Landf., 49(10), 3189–3199.

20. Wang, B., & Saniie, J. (2019). Multilayer perceptron neural networks for grain size estimation and classification. 2019 IEEE Int. Ultrason. Symp., 1643–1646.

21. Stringer, C. & Pachitariu, M. (2025). Cellpose3: One-click image restoration for improved cellular segmentation. Nat. Methods, 22(3), 592–599.

22. Perera, R., Guzzetti, D., & Agrawal, V. (2021). Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain. Comput. Mater. Sci., 196, 110524.




DOI: http://dx.doi.org/10.31258/jkfi.23.1.1-8

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