{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T09:19:19Z","timestamp":1648631959242},"reference-count":20,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2008,9]]},"abstract":"<jats:p> An automated method is presented for segmentation of two-dimensional HRCT images of the lung into regions of four lung patterns: normal, emphysema, honeycombing, and ground-glass opacity (GGO). Segmentation was implemented in two stages. At the first stage, pixel-wise classification of the lung area was performed using local textural features extracted by the wavelet transform. At the second stage, classification results were refined by application of knowledge-based rules. Performance of the method was compared on two sets of HRCT images: one included HRCT images with characteristic examples of lung patterns and the other consisted of unselected HRCT images that represented a model of routine operations at a general radiology practice. On the first set of images, sensitivity of the method ranged from 0.92 to 0.99, and specificity ranged from 0.96 to 0.99. On the second set of images, sensitivity and specificity were, respectively, 0.49 and 0.95 for emphysema, 0.87 and 0.55 for normal, 0.34 and 0.99 for honeycombing, and 0.57 and 0.94 for GGO. The two-stage approach allowed for simple and effective application of high-level knowledge about appearance of lung patterns on HRCT images and did not require feature and region of interest size selection for the first stage of pixel-wise lung pattern classification. <\/jats:p>","DOI":"10.1142\/s1469026808002259","type":"journal-article","created":{"date-parts":[[2009,1,22]],"date-time":"2009-01-22T09:55:20Z","timestamp":1232618120000},"page":"265-280","source":"Crossref","is-referenced-by-count":1,"title":["SEGMENTATION OF LUNG PATTERNS IN HIGH-RESOLUTION COMPUTED TOMOGRAPHY IMAGES OF THE LUNG"],"prefix":"10.1142","volume":"07","author":[{"given":"ALENA","family":"SHAMSHEYEVA","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia"}]},{"given":"ARCOT","family":"SOWMYA","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia"}]},{"given":"PETER","family":"WILSON","sequence":"additional","affiliation":[{"name":"I-MED Network Ltd., Australia"}]}],"member":"219","published-online":{"date-parts":[[2011,11,20]]},"reference":[{"key":"rf1","volume-title":"High-resolution CT of the Lung","author":"Webb W. 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