1 Ocak 2018 Dergi makalesi Açık Erişim

Doganay, Emine; Kara, Sada; Ozcelik, Hatice Kutbay; Kart, Levent

Dublin Core

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Doganay, Emine</dc:creator>
  <dc:creator>Kara, Sada</dc:creator>
  <dc:creator>Ozcelik, Hatice Kutbay</dc:creator>
  <dc:creator>Kart, Levent</dc:creator>
  <dc:date>2018-01-01</dc:date>
  <dc:description>The lung is an essential organ and is dark in Computed Tomography (CT) images because of air. Lung segmentation and correct lung region separation is a prerequisite for the development of computer-aided diagnostic algorithms and disease treatment planning. However, this remains a nontrivial problem because of lung anatomical structures. Here, we addressed this problem and proposed a reliable and robust solution that is based on a histogram-based fuzzy C-means (FCM) algorithm and morphological mathematical algorithms. There were 1632 high resolution CT slices with 1 mm thickness used from asthma patients with low dose; right and left lungs were classified using the proposed algorithm. We extracted right lung regions with 96.05% accuracy and left lung regions at 96.32%. The computation time is 1.3 s per slice.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/33693</dc:identifier>
  <dc:identifier>oai:zenodo.org:33693</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 6(6) 638-648</dc:source>
  <dc:title>A hybrid lung segmentation algorithm based on histogram-based fuzzy C-means clustering</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>