NDLI: Computer-aided detection of hepatocellular carcinoma in hepatic CT: False positive reduction with feature selection

Content Provider	IEEE Xplore Digital Library
Author	Jian-Wu Xu Suzuki, K.
Copyright Year	2011
Description	Author affiliation: Department of Radiology, The University of Chicago, IL 60637 USA (Jian-Wu Xu; Suzuki, K.)
Abstract	This study presents a computer-aided detection (CADe) system of hepatocellular carcinoma (HCC) using sequential forward floating selection (SFFS) method with linear discriminant analysis (LDA). We extracted morphologic and texture features from the segmented HCC candidate regions from the arterial phase (AP) images of the contrast-enhanced hepatic CT images. To select the most discriminatory features for classification, we developed an SFFS method directly coupled with LDA that maximizes the area under the receiver-operating-characteristic curve (AUC) value. The maximal AUC value criterion directly reflects the CADe system performance used in clinical practice. The initial CADe before the classification achieved a 100% (23/23) sensitivity with 33.7 (775/23) false positives (FPs) per patient. The maximal AUC SFFS method for LDA with eleven selected features eliminated 48.0% (372/775) of the FPs without any removal of the HCCs in a leave-one-lesion-out cross-validation test; thus, a 95.6% sensitivity with 7.9 FPs per patient was achieved.
Starting Page	1097
Ending Page	1100
File Size	374000
Page Count	4
File Format	PDF
ISBN	9781424441273
ISSN	19457928
DOI	10.1109/ISBI.2011.5872592
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computed tomography Feature extraction Liver Image segmentation Sensitivity Three dimensional displays Cancer Maximal AUC Feature Selection Hepatocellular Carcinoma Linear Discriminant Analysis Computer-aided Detection
Content Type	Text
Resource Type	Article

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