Deep self-organizing map neural networks improve the segmentation for inadequate plantar pressure imaging data set

Wang, D., Li, Z., Dey, N., Slowik, A., Sherratt, R. S. ORCID: https://orcid.org/0000-0001-7899-4445 and Shi, F. (2024) Deep self-organizing map neural networks improve the segmentation for inadequate plantar pressure imaging data set. Network: Computation in Neural Systems. ISSN 1361-6536 doi: 10.1080/0954898X.2024.2413849

Abstract/Summary

This study introduces a deep self-organizing map neural network based on level-set (LS-SOM) for the customization of a shoe-last defined from plantar pressure imaging data. To alleviate the over-segmentation problem of images, which refers to segmenting images into more subcomponents, a domain-based segmentation model of plantar pressure images was constructed. The domain growth algorithm was subsequently modified by optimizing its parameters. A SOM with 10, 15, 20, and 30 hidden layers was compared and validated according to domain growth characteristics by using merging and splitting algorithms. Furthermore, we incorporated a level set segmentation method into the plantar pressure image algorithm to enhance its efficiency. Compared to the literature, this proposed method has significantly improved pixel accuracy, average cross-combination ratio, frequency-weighted cross-combination ratio, and boundary F1 index comparison. Using the proposed methods, shoe lasts can be designed optimally, and wearing comfort is enhanced, particularly for people with high blood pressure.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/119027
Identification Number/DOI	10.1080/0954898X.2024.2413849
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Biomedical Sciences Life Sciences > School of Biological Sciences > Department of Bio-Engineering
Publisher	Taylor and Francis
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