Maximal partial AUC feature selection in computer-aided detection of hepatocellular carcinoma in contrast-enhanced hepatic CT

Jian-Wu Xu; Kenji Suzuki

doi:10.1117/12.910526

23 February 2012 Maximal partial AUC feature selection in computer-aided detection of hepatocellular carcinoma in contrast-enhanced hepatic CT

Jian-Wu Xu, Kenji Suzuki

Author Affiliations +

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 83150H (2012) https://doi.org/10.1117/12.910526
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

A major challenge in the current computer-aided detection (CADe) of hepatocellular carcinomas (HCCs) in contrastenhanced hepatic CT is to reduce the number of false-positive (FP) detections while maintaining a high sensitivity level. In this paper, we propose a feature selection method based on a sequential forward floating selection procedure coupled with a linear discriminant analysis classifier to improve the classification performance in computerized detection of HCCs in contrast-enhanced hepatic CT. The proposed method selected the most relevant features that would maximize the partial area under the receiver-operating-characteristic (ROC) curve (partial AUC) value, which would essentially lead to the maximum classification performance in the computer-aided detection scheme in a clinical setting. The partial AUC value is defined as the normalized AUC value in the high sensitivity region of the ROC curve, which is of clinical importance. In order to test the performance of the proposed method, we compared it against the popular stepwise feature selection method based on Wilks' lambda and a recently developed maximal AUC feature selection for an HCC database (23 HCCs and 1279 non-HCCs). We extracted 88 morphologic, gray-level-based, and texture features from the segmented lesion candidate regions in the hepatic CT images. The proposed method selected 9 features and achieved 100% sensitivity at 5.5 FPs per patient. Experiments showed a significant improvement in the performance of the classifier with the proposed feature selection method over that with the popular stepwise feature selection based on Wilks' lambda (17.3 FPs per patient) and the maximal AUC feature selection (10.0 FPs per patient) in terms of AUC values and FP rates.

Citation Download Citation

Jian-Wu Xu and Kenji Suzuki "Maximal partial AUC feature selection in computer-aided detection of hepatocellular carcinoma in contrast-enhanced hepatic CT", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150H (23 February 2012); https://doi.org/10.1117/12.910526

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