Early　nodule　detection　is　significant　for　the　diagnosis　and　clinical　treatment　of　lung　cancer.　An　efficient　computer-aided　detection　system　is　developed　to　detect　lung　nodules　in　computed　tomography　scan　image.　In　order　to　highlight　lesion　area,　lung　parenchyma　segmentation　including　bronchial　removal　and　contour　pruning　is　implemented　by　iterative　threshold　and　rolling　ball　algorithm.　Mean-shift　algorithm　is　applied　to　further　smooth　and　enhance　inner-structures　of　lung　parenchyma.　To　effectively　reduce　false　positive　nodules,　hybrid　features　are　extracted　using　the　rule-based　feature　pruning　technology,　they　are　regarded　as　input　samples　of　SVM　classifier　to　distinguish　nodules　from　nodule　candidates.　Numerous　experiments　are　conducted　on　a　large　dataset　from　lung　image　database　consortium　by　various　classifies,　the　diagnosis　results　demonstrate　that　the　proposed　nodule　detection　system　achieves　a　promising　classification　accuracy,　sensitivity　and　specificity　in　overall　performance.