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Open Access Arrowhead detection in biomedical images

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Medical images in biomedical documents tend to be complex by nature and often contain several regions that are annotated using arrows. Arrowhead detection is a critical precursor to regionof-interest (ROI) labeling and image content analysis. To detect arrowheads, images are first binarized using fuzzy binarization technique to segment a set of candidates based on connected component principle. To select arrow candidates, we use convexity defect-based filtering, which is followed by template matching via dynamic programming. The similarity score via dynamic time warping (DTW) confirms the presence of arrows in the image. Our test on biomedical images from imageCLEF 2010 collection shows the interest of the technique.
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Document Type: Research Article

Publication date: February 17, 2016

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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