Automatic bone registration in MR knee images for cartilage morphological analysis

Abstract

We propose a cartilage matching technique based on the registration of the corresponding bone structures instead of using the cartilage. Our method consists of five steps. First, cartilage and corresponding bone structures are extracted by semi-automatic segmentation. Second, gross translational mismatch between corresponding bone structures is corrected by point-based rough registration. The center of inertia (COI) of each segmented bone structure is considered as the reference point. Third, the initial alignment is refined by distance-based surface registration. For fast and robust convergence of the distance measure to the optimal value, a 3D distance map is generated by the Gaussian-weighted narrow-band distance propagation. Fourth, rigid transformation of the bone surface registration is applied to the cartilage of baseline MR images. Finally, morphological differences of the corresponding cartilages are visualized by color-coded mapping and image fusion. Experimental results show that the cartilage morphological changes of baseline and followup MR knee images can be easily recognized by the correct registration of the corresponding bones.

Quantitative Assessment of Shape Deformation of Regional Cranial Bone for Evaluation of Surgical Effect in Patients with Craniosynostosis

“…To adjust the scale difference between the skull surface model and the mean normal skull surface model and to correct the position error caused by the scale transformation, alternate surface-and landmark-based registrations are performed repeatedly. The surfacebased registration is accomplished by minimizing Gaussian-weighted distance measures using a Gaussian-weighted narrow-band distance map [18]. In the Gaussian-weighted narrow-band distance map, the Gaussian weight is assigned to the surface points of the skull surface model, and the narrow-band width set to 20-pixels.…”