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Proceedings Paper

Surgical wound segmentation based on adaptive threshold edge detection and genetic algorithm
Author(s): Hsueh-Fu Shih; Te-Wei Ho; Jui-Tse Hsu; Chun-Che Chang; Feipei Lai; Jin-Ming Wu
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Paper Abstract

Postsurgical wound care has a great impact on patients' prognosis. It often takes few days, even few weeks, for the wound to stabilize, which incurs a great cost of health care and nursing resources. To assess the wound condition and diagnosis, it is important to segment out the wound region for further analysis. However, the scenario of this strategy often consists of complicated background and noise. In this study, we propose a wound segmentation algorithm based on Canny edge detector and genetic algorithm with an unsupervised evaluation function. The results were evaluated by the 112 clinical images, and 94.3% of images were correctly segmented. The judgment was based on the evaluation of experimented medical doctors. This capability to extract complete wound regions, makes it possible to conduct further image analysis such as intelligent recovery evaluation and automatic infection requirements.

Paper Details

Date Published: 8 February 2017
PDF: 5 pages
Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 1022517 (8 February 2017); doi: 10.1117/12.2266105
Show Author Affiliations
Hsueh-Fu Shih, National Taiwan Univ. (Taiwan)
Te-Wei Ho, National Taiwan Univ. (Taiwan)
Jui-Tse Hsu, National Taiwan Univ. (Taiwan)
Chun-Che Chang, National Taiwan Univ. (Taiwan)
Feipei Lai, National Taiwan Univ. (Taiwan)
Jin-Ming Wu, National Taiwan Univ. Hospital (Taiwan)


Published in SPIE Proceedings Vol. 10225:
Eighth International Conference on Graphic and Image Processing (ICGIP 2016)
Yulin Wang; Tuan D. Pham; Vit Vozenilek; David Zhang; Yi Xie, Editor(s)

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