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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-loc>7003 Kilworth Lane, Springfield, VA 22151 USA</publisher-loc>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.2352/ISSN.2470-1173.2016.11.IMAWM-469</article-id>
      <article-id pub-id-type="sici">2470-1173(20160214)2016:11L.1;1-</article-id>
      <article-id pub-id-type="publisher-id">ei_24701173_v2016n11_input/s12.xml</article-id>
      <article-id pub-id-type="other">/ist/ei/2016/00002016/00000011/art00004</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Automatic Mobile Retinal Microaneurysm Detection Using Handheld Fundus Camera via Cloud Computing</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>YOU</surname>
            <given-names>Jane</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>LI</surname>
            <given-names>Qin</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>GUO</surname>
            <given-names>Zhenhua</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>14</day>
        <month>02</month>
        <year>2016</year>
      </pub-date>
      <volume>2016</volume>
      <issue>11</issue>
      <fpage>1</fpage>
      <lpage>5</lpage>
      <permissions>
        <copyright-year>2016</copyright-year>
      </permissions>
      <abstract>
        <p>
          <italic>This paper presents a new system to monitor retinal microaneurysm which are regarded as the first sign of diabetic retinopathy(DR). The proposed approach to automatic microaneurysm detection aims to enhance screening large populations. Most of the existing computer-aided systems
 for microaneurysm detection are based on the sophisticated medical device in a clinical environment. However, the popular medical devices such as table fundus camera and portable fundus camera are subject to certain limitations for its usage beyond the scope of clinical practice. The challenges
 include the complexity of operation, cost issue and requirement of professional maintenance, etc. Unlike the conventional approaches, we developed an automatic mobile retinal microaneurysm detection system by using a handheld fundus camera to facilitate retinal healthcare and monitoring with
 flexibility and convenience. Our system includes: (1) retinal image capturing by handheld fundus camera;(2) retinal image analysis via cloud computing;(3) microaneurysm detection by Multi-orientation Sum of Matched Filter and SVM. The experimental results demonstrate the feasibility of our
 system by performance improvement on the aspects of speed, accuracy, and convenience.</italic>
        </p>
      </abstract>
    </article-meta>
  </front>
</article>
