Computer aided diagnosis for pulmonary nodules by shape feature extraction

In this paper, we propose a new diagnosis method of pulmonary nodules in CT images to reduce false positive (FP) rate for high true positive (TP) rate conditions. An essential core of the method is to extract two novel and effective features from the raw CT images: One is orientation features of nodules in a region of interest (ROI) extracted by a gabor filter, while the other is variation of CT values of the ROI in the direction along body axis. By using the extracted features, a principal component analysis technic and neural network approaches are then used to discriminate between nodule and non-nodule images. Simulation results show that discrimination performance using the proposed features is extremely improved compared to that of the conventional method.