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

作者：Fangzhao Li, Changjian Wang, Xiaohui Liu, Yuxing Peng, Shiyao Jin · 发表于：Computational Intelligence and Neuroscience · 年份：2018 · DOI：10.1155/2018/4149103 · 被引用次数：94 · 研究领域：Diabetic Foot Ulcer Assessment and Management、Pressure Ulcer Prevention and Management、Wound Healing and Treatments

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.