ESAC: An Algorithm for Fissure Line Detection

Pulmonary fissure detection is an important step for lung lobe segmentation which is necessary for accurate diagnostics and surgical planning. Automatic detection of fissures in CT images is a challenging task due to varying intensity, pathological deformation and noisy acquisitions. In this paper, we propose a novel fissure line detection technique using eigen analysis of the hessian matrix and an exhaustive sample consensus (ESAC) based line fitting in small overlapping windows. The idea behind using the line fitting technique is that the fissure line appears as piece-wise linear segment in a small window. As opposed to RANSAC, the point selection mechanism in the proposed method chooses all combination of data points exhaustively. This approach reduces the possibility of missing the possible candidate points for a fissure line. Our main contribution lies in detection of the fissure line without using any training data as well as any template matching model. The performance of our method is validated on the publicly available LOLA11 database. Comparisons with some existing approaches on this database indicate the advantage of the proposed solution.