Incorporating sheet-likeness information in intensity-based lung CT image registration

Image registration is a useful technique to measure the change between two or more images. Lung CT image registration is widely used an non-invasive method to measure the lung function changes. Non-invasive lung function measurement accuracy highly depends on lung CT image registration accuracy. Improving the registration accuracy is an important issue.

In this thesis, we propose incorporating information of the anatomical structure of the lung (fissures) as an additional cost function of the lung CT image registration. The intensity-based similarity measurement method (sum of the squared tissue volume differences) is also used to complement lung tissue information matching. However, since fissures are hard to segment, a sheet-likeness filter is applied to detect fissure-like structures. Sheet-likeness is used as an additional cost function of the intensity-based registration. The registration accuracy is verified by the visual assessment and landmark error measurement. The landmark error measurement can show an improvement of the proposed algorithm.