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

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.