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<article article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="aggregator">72010604</journal-id>
      <journal-title>Electronic Imaging</journal-title>
      <issn pub-type="ppub">2470-1173</issn><issn pub-type="epub"></issn>
      <publisher>
        <publisher-name>Society for Imaging Science and Technology</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.2352/ISSN.2470-1173.2017.2.VIPC-404</article-id>
      <article-id pub-id-type="sici">2470-1173(20170129)2017:2L.38;1-</article-id>
      <article-id pub-id-type="publisher-id">s8.phd</article-id>
      <article-id pub-id-type="other">/ist/ei/2017/00002017/00000002/art00008</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>A fast and accurate segmentation method for medical images</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Wu</surname>
            <given-names>Jiatao</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Li</surname>
            <given-names>Yong</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Peng</surname>
            <given-names>Yun</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Fan</surname>
            <given-names>Chunxiao</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>29</day>
        <month>01</month>
        <year>2017</year>
      </pub-date>
      <volume>2017</volume>
      <issue>2</issue>
      <fpage>38</fpage>
      <lpage>43</lpage>
      <permissions>
        <copyright-year>2017</copyright-year>
      </permissions>
      <abstract>
        <p>Selecting regions of interest (ROI) of the medical images is an important task in medical image processing. Manual selection of ROIs serves as the main method for single images and it has a high accuracy. However, it will become infeasible to manually segment ROIs on a large number
 of images. Observing this problem, this paper proposes a fast and accurate segmentation method to obtain ROIs on a batch of medical images. Firstly, we segment the standard brain image S<sub>t</sub> which has not been injected with tracer. Secondly, we use a B-Spline elastic registration method
 to get the inverse-registration parameters. Thirdly, we get the template image T<sub>e</sub> with the registration parameters. Finally, we search the target region by template matching. Experimental results show that the proposed method performs well on medical image segmentation.</p>
      </abstract>
      <kwd-group>
        <kwd>VIDEO OBJECT SEGMENTATION</kwd>
        <kwd>SLIC SUPERPIXELS</kwd>
        <kwd>OBJECT EXTRACTION</kwd>
        <kwd>CONSUMER VIDEO</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
