Title: Computer-aided diagnosis of radiographic patterns of lung disease via MDCT images

Authors: Ye Xu, Edwin J.R. Van Beek, Kevin R. Flaherty, Ella A. Kazerooni, Eric A. Hoffman

Addresses: Computer Science Department, The University of Iowa, Iowa City, IA 52242, USA; Department of Clinical Informatics, Interventional, and Translational Solutions (CITS), Philips Research in North America, 345 Scarborough Road Briarcliff Manor, NY 10510, USA. ' Departments of Radiology, Biomedical Engineering, Carver College of Medicine, The University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242-1077, USA; Clinical Research Imaging Centre, University of Edinburgh, Western General Hospital, Crewe Road South, EH4 2XU, UK. ' Division of Pulmonary/Critical Care, University of Michigan Health System, 1500 East Medical Center Drive, Floor 6, Ann Arbor, MI 48109-5734, USA. ' Division of Cardiothoracic Radiology, Department of Radiology, University of Michigan Health System, 1500 East Medical Center Drive, Ann Arbor, MI 48109-5734, USA. ' Departments of Radiology, Carver College of Medicine, The University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242-1077, USA

Abstract: With the increasing ability of imaging technologies to provide true volumetric image data, there is an opportunity to fill the need to advance the field of computer-aided texture classification using 3D feature information. We present a method allowing for characterising lung regions of interstitial lung diseases. We compared the inter-observer variation between experts and computer classification results; and analysed the expert|s labelling error and computer classification error. The 3D adaptive multiple feature method was in agreement with an expert in 92%, whereas agreement between two experts was 67%. There was no significant classification difference for different selections of VOI sizes for 15 × 15, 21 × 21, and 31 × 31. We demonstrated that 3D texture features can successfully differentiate parenchymal pathologies associated with both emphysema and interstitial lung diseases as well as mixed patterns. The system may assist primary readers to quantify extent of lung disease, and as such could assist in monitoring of treatment effects.

Keywords: whole lung classification; computer-aided diagnosis; computer-aided assessment; interstitial lung diseases; ILD; radiographic patterns; MDCT images; medical imaging; texture classification; 3D texture features; parenchymal pathologies; emphysema; treatment monitoring; multidetector computed tomography.

DOI: 10.1504/IJCSE.2010.037680

International Journal of Computational Science and Engineering, 2010 Vol.5 No.3/4, pp.254 - 261

Available online: 24 Dec 2010 *

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