TY  - THES
AB  - Image registration aligns multiple images of the same scene so they can be compared and analyzed, yet automated correspondence detection—especially under non‑rigid deformation—remains challenging. This dissertation presents several contributions to non‑rigid image registration. First, a new intensity‑based similarity measure, Residual Complexity (RC), is introduced to handle spatially varying intensity distortions common in microscopy and MRI. Second, a novel rigid and non‑rigid point‑set registration method, the Coherent Point Drift (CPD) algorithm, is developed to jointly estimate point correspondences and transformations. Finally, a fully automated motion‑estimation framework for 3D+T echocardiography is proposed, incorporating ultrasound‑specific similarity measures and biomechanical constraints to generate dense deformation fields for quantifying myocardial strain and torsion. Validation using implanted marker data demonstrates the accuracy and clinical potential of the approach.
AD  - Oregon Health and Science University
AU  - Myronenko, Andriy
DA  - 2010
DO  - 10.6083/M45X26WK
DO  - DOI
ED  - Song, Xubo
ED  - Advisor
ID  - 369
KW  - Mathematics
KW  - Algorithms
KW  - Image Enhancement
KW  - Magnetic Resonance Imaging
KW  - Image Processing, Computer-Assisted
KW  - non-rigid image registration
L1  - https://digitalcollections.ohsu.edu/record/369/files/370_etd.pdf
L2  - https://digitalcollections.ohsu.edu/record/369/files/370_etd.pdf
L4  - https://digitalcollections.ohsu.edu/record/369/files/370_etd.pdf
LK  - https://digitalcollections.ohsu.edu/record/369/files/370_etd.pdf
N2  - Image registration aligns multiple images of the same scene so they can be compared and analyzed, yet automated correspondence detection—especially under non‑rigid deformation—remains challenging. This dissertation presents several contributions to non‑rigid image registration. First, a new intensity‑based similarity measure, Residual Complexity (RC), is introduced to handle spatially varying intensity distortions common in microscopy and MRI. Second, a novel rigid and non‑rigid point‑set registration method, the Coherent Point Drift (CPD) algorithm, is developed to jointly estimate point correspondences and transformations. Finally, a fully automated motion‑estimation framework for 3D+T echocardiography is proposed, incorporating ultrasound‑specific similarity measures and biomechanical constraints to generate dense deformation fields for quantifying myocardial strain and torsion. Validation using implanted marker data demonstrates the accuracy and clinical potential of the approach.
PB  - Oregon Health and Science University
PY  - 2010
T1  - Non-rigid image registration regularization, algorithms and applications
TI  - Non-rigid image registration regularization, algorithms and applications
UR  - https://digitalcollections.ohsu.edu/record/369/files/370_etd.pdf
Y1  - 2010
ER  -