CT-based lung emphysema quantification is sensitive to the kernel used for image reconstruction. In this paper, we present and evaluate three methods for normalization of CT images reconstructed with different kernels to homogenize image sharpness and pixel noise. For 56 subjects, chest CT images reconstructed with a soft kernel (B20f) and a medium sharp kernel (B50f) were acquired. Normalization of the B50f images was performed using a Laplacian frequency decomposition, an edge-preserving frequency decomposition, and a heuristic filter-based method. To compare the normalization methods, emphysema indices (EIs) were computed from the normalized images and compared to the baseline EIs computed from the B20f images. Further, volume overlaps of the detected emphysema regions were computed. Average differences in EI between kernels decreased for all normalization methods. Laplacian and edge-preserving frequency normalization show a similar agreement with the baseline EI, however, emphysema regions detected after Laplacian frequency normalization show degraded volume overlaps likely caused by haloing artifacts in the normalized images. Overall, edge-preserving frequency decomposition shows the best normalization performance yet high computational demands.
|Bildverarbeitung für die Medizin 2016
|Th. Tolxdorff, Th.M. Deserno , H. Handels, H.-P. Meinzer
|Springer Vieweg, Berlin Heidelberg
|Veröffentlicht - 01.2016
|Workshop on Bildverarbeitung fur die Medizin 2016
- Berlin, Deutschland
Dauer: 13.03.2016 → 15.03.2016