OBJECTIVES: Image registration is commonly used in dental applications for aligning imaging data sets, which is particularly useful when assessing the progression or regression of particular pathomorphic conditions. However, due to the nature of the processed data or the data acquisition process itself, rigid body registration may be insufficient to accurately align the processed data sets. In such cases, deformable models are employed. This study presents a comparison of four well-established deformable models for aligning CBCT volumes. METHODS: The compared models include the original Demons algorithm, symmetric forces Demons, diffeomorphic Demons and level-set motion. The compared techniques are incorporated into a general image registration scheme featuring two distinct stages: a common, fast, rigid-based alignment for pre-registering the data and a finer elastic registration phase, based on the four compared deformation models. RESULTS: The proposed framework was applied to a total of 40 CBCT volume pairs with known and unknown initial differences. CONCLUSIONS: After both qualitative and quantitative assessment of the produced aligned data, it was concluded that the level-set motion method outperformed all other techniques for data pairs with both unknown initial differences, as well as with known elastic deviations based on fixed sinusoidal models and B-splines.
OBJECTIVES: Image registration is commonly used in dental applications for aligning imaging data sets, which is particularly useful when assessing the progression or regression of particular pathomorphic conditions. However, due to the nature of the processed data or the data acquisition process itself, rigid body registration may be insufficient to accurately align the processed data sets. In such cases, deformable models are employed. This study presents a comparison of four well-established deformable models for aligning CBCT volumes. METHODS: The compared models include the original Demons algorithm, symmetric forces Demons, diffeomorphic Demons and level-set motion. The compared techniques are incorporated into a general image registration scheme featuring two distinct stages: a common, fast, rigid-based alignment for pre-registering the data and a finer elastic registration phase, based on the four compared deformation models. RESULTS: The proposed framework was applied to a total of 40 CBCT volume pairs with known and unknown initial differences. CONCLUSIONS: After both qualitative and quantitative assessment of the produced aligned data, it was concluded that the level-set motion method outperformed all other techniques for data pairs with both unknown initial differences, as well as with known elastic deviations based on fixed sinusoidal models and B-splines.
Authors: George K Matsopoulos; Pantelis A Asvestas; Nikolaos A Mouravliansky; Konstantinos K Delibasis Journal: IEEE Trans Med Imaging Date: 2004-12 Impact factor: 10.048
Authors: He Wang; Lei Dong; Jennifer O'Daniel; Radhe Mohan; Adam S Garden; K Kian Ang; Deborah A Kuban; Mark Bonnen; Joe Y Chang; Rex Cheung Journal: Phys Med Biol Date: 2005-06-01 Impact factor: 3.609