Positron emission tomography (PET) using fluorine-18 deoxyglucose (18F-FDG) has become an increasingly recommended tool in clinical whole-body oncology imaging for the detection, diagnosis, and follow-up of many cancers. One way to improve the diagnostic utility of PET oncology imaging is to assist physicians facing difficult cases of residual or low-contrast lesions. This study aimed at evaluating different schemes of computer-aided detection (CADe) systems for the guided detection and localization of small and low-contrast lesions in PET. These systems are based on two supervised classifiers, linear discriminant analysis (LDA) and the nonlinear support vector machine (SVM). The image feature sets that serve as input data consisted of the coefficients of an undecimated wavelet transform. An optimization study was conducted to select the best combination of parameters for both the SVM and the LDA. Different false-positive reduction (FPR) methods were evaluated to reduce the number of false-positive detections per image (FPI). This includes the removal of small detected clusters and the combination of the LDA and SVM detection maps. The different CAD schemes were trained and evaluated based on a simulated whole-body PET image database containing 250 abnormal cases with 1230 lesions and 250 normal cases with no lesion. The detection performance was measured on a separate series of 25 testing images with 131 lesions. The combination of the LDA and SVM score maps was shown to produce very encouraging detection performance for both the lung lesions, with 91% sensitivity and 18 FPIs, and the liver lesions, with 94% sensitivity and 10 FPIs. Comparison with human performance indicated that the different CAD schemes significantly outperformed human detection sensitivities, especially regarding the low-contrast lesions.