Abstract
Automatic segmentation of lung lobes from CT data is becoming clinically relevant as an enabler for, e.g., lobe-based
quantitative analysis for diagnostics or more accurate interventional planning. The detection of fissures is
thereby usually a first step in a more comprehensive segmentation framework. Although many approaches have
been presented in the past addressing fissure detection, there are still several limitations. In this paper, we review
one of the most prominent algorithms for fissure detection which is based on eigenvalue analysis of the Hessian
matrix and discuss its inherent limitations. In order to overcome these shortcomings, we propose a novel line
enhancing filter using multiple hypotheses testing. Due to the large search space of a potential three-dimensional
surface orientation, we search for fissure line pieces in two-dimensional cut planes. For each voxel inside the lungs,
we match the local two-dimensional neighborhood around the voxel with a fissure template model representing
a bright line on a dark background. By testing out numerous rotated versions of the template model, we are
able to detect fissures of different orientations. In contrast to the eigenvalue analysis of the Hessian matrix, the
local neighborhood to be considered can be effectively varied for the new filter with a limited set of parameters
thus providing more flexibility. On 20 cases from a publicly available data base, an ROC curve analysis showed
that the line enhancing filter results in an average area under curve of 0.71 compared to 0.67 using the Hessian
filter.
