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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 15/2020

30.10.2018 | Computer aided Medical Diagnosis

Evaluation on diabetic plantar pressure data-set employing auto-segmentation technologies

verfasst von: Hongwei Chen, Luying Cao, Zairan Li, D. Jude Hemanth, Lijun Wu, Victor Hugo C. de Albuquerque, Fuqian Shi

Erschienen in: Neural Computing and Applications | Ausgabe 15/2020

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Abstract

Plantar pressure imaging technologies play more important role in diabetics’ shoe-last design. The plantar pressure imaging data-set was acquired through pressure sensors system and was preprocessed using typical image process technologies including Gauss filtering, gamma correction, and wavelet transform enhancement in this work. To decrease the computational complexity, edge detection operator with Sobel, Roberts, Prewitt, Log and Canny were applied. Finally, threshold-based method, gray, watershed, feature clustering and fuzzy cluster, region growing-based plantar pressure image segmentation were employed, respectively. Results illustrated that threshold performs the better effectiveness by using analytic hierarchy process evaluation method through a special indices definition. The proposed methods in the research will be potential application and guidance for comfort shoe design for diabetics.
Vorheriger Artikel Toward a novel method to support decision-making process in health and behavioral factors analysis for the composition of IT projects teams
Nächster Artikel Adaptive computing-based biometric security for intelligent medical applications

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Metadaten
Titel
Evaluation on diabetic plantar pressure data-set employing auto-segmentation technologies
verfasst von
Hongwei Chen
Luying Cao
Zairan Li
D. Jude Hemanth
Lijun Wu
Victor Hugo C. de Albuquerque
Fuqian Shi
Publikationsdatum
30.10.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 15/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3838-x

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