Deep-segmentation of plantar pressure images convolutional neural networks

被引：16

作者：

Wang, Dan ^{[1
]}

Li, Zairan ^{[2
]}

Dey, Nilanjan ^{[3
]}

Ashour, Amira S. ^{[4
]}

Moraru, Luminita ^{[5
]}

Sherratt, R. Simon ^{[6
]}

Shi, Fuqian ^{[7
]}

机构：

[1] Tianjin Univ, Sch Elect Engn & Automat, Tianjin Key Lab Proc Measurement & Control, Tianjin, Peoples R China

[2] Wenzhou Polytech, Wenzhou, Peoples R China

[3] Techno India Coll Technol, Dept IT, Kolkata, W Bengal, India

[4] Tanta Univ, Fac Engn, Tanta, Egypt

[5] Dunarea de Jos Univ Galati, Fac Sci & Environm, Dept Chem Phys & Environm, Galati, Romania

[6] Univ Reading, Dept Biomed Engn, Reading, Berks, England

[7] Rutgers State Univ, Canc Inst New Jersey, New Brunswick, NJ USA

来源：

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING | 2020年 / 40卷 / 01期

关键词：

Plantar pressure imaging; Level set; Threshold-based segment; Full convolutional networks; VCG; SegNet; SEMANTIC SEGMENTATION;

D O I：

10.1016/j.bbe.2020.01.004

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Comfort shoe-last design relies on the key points of last curvature. Traditional plantar pressure image segmentation methods are limited to their local and global minimization issues. In this work, an improved fully convolutional networks (FCN) employing SegNet (SegNet+FCN 8 s) is proposed. The algorithm design and operation are performed using the visual geometry group (VGG). The method has high efficiency for the segmentation in positive indices of global accuracy (0.8105), average accuracy (0.8015), and negative indices of average cross-ratio (0.6110) and boundary F1 index (0.6200). The research has potential applications in improving the comfort of shoes. (c) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

引用

页码：546 / 558

页数：13

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