A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

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ID: 35018
2018
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Abstract
Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment.
Reference Key
li2018acomputational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Li, Fangzhao;Wang, Changjian;Liu, Xiaohui;Peng, Yuxing;Jin, Shiyao;
Journal Computational Intelligence and Neuroscience
Year 2018
DOI
DOI not found
URL
http://dx.doi.org/10.1155/2018/4149103
Keywords Keywords not found

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