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.