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Histogram of oriented gradient based plantar pressure image feature extraction and classification employing fuzzy support vector machine

Wang, C., Li, Z., Dey, N., Ashour, A., Fong, S., Sherratt, R. S., Wu, L. and Shi, F. (2018) Histogram of oriented gradient based plantar pressure image feature extraction and classification employing fuzzy support vector machine. Journal of Medical Imaging and Health Informatics, 8 (4). pp. 842-854. ISSN 2156-7018

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To link to this item DOI: 10.1166/jmihi.2018.2310

Abstract/Summary

To identify and design comfortable footwear for patients with diabetic conditions (e.g. high blood pressure), an experimental design was proposed to collect plantar pressure data-set using a RSscan sensor system. The data-set acquired by the pressure sensor was reformed into images to allow for image analysis technologies to be applied. In this paper, image features were extracted including color of Hue-Saturation-Value (HSV), gray difference based features (mean, entropy and auto-correlation function), gray-level co-occurrence matrix based features (energy and correlation) and Histogram of Oriented Gradient (HOG). The features were normalized into a high dimensional vector applied to a Fuzzy Support Vector Machine (FSVM), and finally, the FSVM was trained and used for prediction of diabetic plantar pressure images. Normal features and HOG were compared in different classifiers including SVM, LSVM and FSVM. HOG with normal features of image for FSVM performed with a higher accuracy classification effectiveness (84.3%) than the current state of the art. The proposed methods have clear applications in revealing the key zone of foot plantar of diabetics and offer a new direction in producing comfortable diabetic footwear.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Biomedical Sciences
Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:72376
Publisher:American Scientific Publishers

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