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13 March 2013 Lobar fissure detection using line enhancing filters
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 86693C (2013)
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
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.
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Tobias Klinder, Hannes Wendland, and Rafael Wiemker "Lobar fissure detection using line enhancing filters", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86693C (13 March 2013);

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