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Proceedings Paper

Adaptive feature analysis of false positives for computerized detection of lung nodules in digital chest images
Author(s): Xin-Wei Xu; Heber MacMahon; Maryellen Lissak Giger; Kunio Doi
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Paper Abstract

To assist radiologists in diagnosing early lung cancer, we have developed a computer-aided diagnosis (CAD) scheme for automated detection of lung nodules in digital chest images. The database used for this study consisted of two hundred PA chest radiographs, including 100 normals and 100 abnormals. Our CAD scheme has four basic steps, namely, (1) preprocessing, (2) identification of initial nodule candidates (rule-based test #1), (3) grouping of initial nodule candidates into six groups, and (4) elimination of false positives (rule-based test #2 - #5 and artificial neural network). Our CAD scheme achieves, on average, a sensitivity of 70%, with 1.7 false positives per chest image. We believe that this CAD scheme with its current performance is ready for clinical evaluation.

Paper Details

Date Published: 25 April 1997
PDF: 9 pages
Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); doi: 10.1117/12.274129
Show Author Affiliations
Xin-Wei Xu, Univ. of Chicago (United States)
Heber MacMahon, Univ. of Chicago (United States)
Maryellen Lissak Giger, Univ. of Chicago (United States)
Kunio Doi, Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 3034:
Medical Imaging 1997: Image Processing
Kenneth M. Hanson, Editor(s)

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