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Evaluation of surgical wound segmentation using quantitative analysis

Published: 24 November 2017 Publication History

Abstract

To provide assistance to medical practitioners in wound care, numerous studies have been developing clinical decision support tools for wound monitoring and tracing. Image segmentation is the foundation of image analysis. A superior segmentation could address further image analysis and accurately interpret an image even with background noise. However, the segmentation performance of the wound using objective quantitative analysis has not been well-documented. In this study, we evaluated the segmentation algorithm based on the Canny edge detector and genetic algorithm along with a quantitative objective analysis based on empirical objective and precision-recall measure. According to a majority vote among three surgeons as the gold standard for over 100 wound images, the results revealed that the proposed segmentation algorithm achieved 89% accuracy (95% confidence interval: 87.5--90.6) for wound region detection. In conclusion, the proposed segmentation detector is robust and reliable enough to perform further image analysis. The findings provide further support to evaluate image segmentation as a part of image processing.

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  • (2019)Brain Tumor Segmentation Using U-Net and Edge Contour EnhancementProceedings of the 2019 3rd International Conference on Digital Signal Processing10.1145/3316551.3316554(75-79)Online publication date: 24-Feb-2019

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    cover image ACM Other conferences
    ICCIP '17: Proceedings of the 3rd International Conference on Communication and Information Processing
    November 2017
    545 pages
    ISBN:9781450353656
    DOI:10.1145/3162957
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    Published: 24 November 2017

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    Author Tags

    1. image segmentation
    2. quantitative analysis

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    • Ministry of Science and Technology, Taiwan

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    • (2019)Brain Tumor Segmentation Using U-Net and Edge Contour EnhancementProceedings of the 2019 3rd International Conference on Digital Signal Processing10.1145/3316551.3316554(75-79)Online publication date: 24-Feb-2019

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