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25 April 1997 Adaptive feature analysis of false positives for computerized detection of lung nodules in digital chest images
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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.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xin-Wei Xu, Heber MacMahon, Maryellen Lissak Giger, and Kunio Doi "Adaptive feature analysis of false positives for computerized detection of lung nodules in digital chest images", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274129
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