For the type of pleural connected pulmonary nodule, the segmentation accuracy is low due to fuzzy boundary, intensity inhomogeneity and low contrast between the pleural part and pulmonary nodule part. To solve this problem, an improved active contour model is proposed. First, the eigenvector with the combination of wavelet energy and local binary mode (LBP) is constructed to enhance the dissimilarities between pulmonary nodule and other tissues, such as pulmonary parenchyma and pleura. Secondly, the membership degree of robust speed function is calculated by the fuzzy Knearest neighbor method which combines wavelet energy feature and LBP feature. Thirdly, the robust speed function is introduced into the active contour model. At the boundary of the pulmonary nodule, the robust speed function is close to 0. The evolution of the active contour curve stops, and the segmentation results of pleural connected pulmonary nodule are obtained. Experimental results show that the proposed model has a true positive rate of 090, a false positive rate of 006 and a similarity of 085. These results are very close to the ideal result of manual segmentation by clinical experts.