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Proceedings Paper

A novel scheme for detection of diffuse lung disease in MDCT by use of statistical texture features
Author(s): Jiahui Wang; Feng Li; Kunio Doi; Qiang Li
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Paper Abstract

The successful development of high performance computer-aided-diagnostic systems has potential to assist radiologists in the detection and diagnosis of diffuse lung disease. We developed in this study an automated scheme for the detection of diffuse lung disease on multi-detector computed tomography (MDCT). Our database consisted of 68 CT scans, which included 31 normal and 37 abnormal cases with three kinds of abnormal patterns, i.e., ground glass opacity, reticular, and honeycombing. Two radiologists first selected the CT scans with abnormal patterns based on clinical reports. The areas that included specific abnormal patterns in the selected CT images were then delineated as reference standards by an expert chest radiologist. To detect abnormal cases with diffuse lung disease, the lungs were first segmented from the background in each slice by use of a texture analysis technique, and then divided into contiguous volumes of interest (VOIs) with a 64×64×64 matrix size. For each VOI, we calculated many statistical texture features, including the mean and standard deviation of CT values, features determined from the run length matrix, and features from the co-occurrence matrix. A quadratic classifier was employed for distinguishing between normal and abnormal VOIs by use of a leave-one-case-out validation scheme. A rule-based criterion was employed to further determine whether a case was normal or abnormal. For the detection of abnormal VOIs, our CAD system achieved a sensitivity of 86% and a specificity of 90%. For the detection of abnormal cases, it achieved a sensitivity of 89% and a specificity of 90%. This preliminary study indicates that our CAD system would be useful for the detection of diffuse lung disease.

Paper Details

Date Published: 27 February 2009
PDF: 8 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726039 (27 February 2009); doi: 10.1117/12.811635
Show Author Affiliations
Jiahui Wang, Duke Univ. (United States)
Feng Li, The Univ. of Chicago (United States)
Kunio Doi, The Univ. of Chicago (United States)
Qiang Li, Duke Univ. (United States)

Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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