NDLI: Supervised enhancement of lung nodules by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD)

Content Provider	IEEE Xplore Digital Library
Author	Suzuki, K. Zhenghao Shi Jun Zhang
Copyright Year	2008
Description	Author affiliation: Dept. of Radiol., Univ. of Chicago, Chicago, IL, USA (Suzuki, K.; Zhenghao Shi; Jun Zhang)
Abstract	Computer-aided diagnostic (CAD) schemes often employ a filter for enhancement of lesions as a preprocessing step for improving sensitivity and specificity. The filter enhances objects similar to a model employed in the filter; e.g., a blob enhancement filter based on the Hessian matrix enhances sphere-like objects. Actual lesions, however, often differ from a simple model, e.g., a lung nodule is generally modeled as a solid sphere, but there are nodules of various shapes and with inhomogeneities inside such as a spiculated one and a ground-glass opacity. Thus, conventional filters often fail to enhance actual lesions. Our purpose in this study was to develop a supervised filter for enhancement of lesions by use of a massive-training artificial neural network (MTANN) in a computer-aided diagnostic (CAD) scheme for detection of lung nodules in CT. The MTANN filter was trained with actual nodules in CT images to enhance actual patterns of nodules. By use of the MTANN filter, the sensitivity and specificity of our CAD scheme were improved substantially. With the database with 69 lung cancers, our CAD scheme with the MTANN filter achieve a 97% sensitivity with 6.7 false positives (FPs) per section, whereas a conventional CAD scheme with a difference-image technique achieved a 96% sensitivity with 19.3 FPs per section.
Starting Page	1
Ending Page	4
File Size	446002
Page Count	4
File Format	PDF
ISBN	9781424421749
ISSN	10514651
DOI	10.1109/ICPR.2008.4761114
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lungs Artificial neural networks Computer aided diagnosis Filters Lesions Sensitivity and specificity Solid modeling Computed tomography Shape Computer networks
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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