Lung Nodules Detection in CT Images Using Gestalt‐Based Algorithm

Abstract

To overcome low accuracy and high false positive of existing computer-aided lung nodules detection. We propose a novel lung nodule detection scheme based on the Gestalt visual cognition theory. The proposed scheme involves two parts which simulate human eyes cognition features such as simplicity, integrity and classification. Firstly, lung region was segmented from lung Computed tomography (CT) sequences. Then local threedimensional information was integrated into the Maximum intensity projection (MIP) images from axial, coronal and sagittal profiles. In this way, lung nodules and vascular are strengthened and discriminated based on pathologic image characteristics of lung nodules. The experimental database includes fifty-three high resolution CT images contained lung nodules, which had been confirmed by biopsy. The experimental results show that, the accuracy rate of the proposed algorithm achieves 91.29%. The proposed framework improves performance and computation speed for computer aided nodules detection.