This paper explores the use of statistical parametric mapping, which has been widely used in brain imaging applications, in the assessment of morphological changes in rat articular cartilage during OA. This approach can not only detect but also localize changes, effectively zooming in statistically more informative areas and therefore maximizing the sensitivity. The analysis of in-vivo MR images of rat articular cartilage demonstrates that the new approach is at least as sensitive as the cartilage volume analysis.
This paper presents a new image segmentation algorithm using surface-to-image registration. The algorithm employs multi-level transformations and multi-resolution image representations to progressively register atlas surfaces (modeling anatomical structures) to subject images based on weighted external forces in which weights and forces are determined by gradients and local intensity profiles obtained from images. The algorithm is designed to prevent atlas surfaces converging to unintended strong edges or leaking out of structures of interest through weak edges where the image contrast is low. Segmentation of bone structures on MR images of rat knees analyzed in this manner performs comparably to technical experts using a semi-automatic tool.
This paper presents two non-rigid image registration algorithms: Thirion's Demons method and its spline-based extension, and compares their performance on the task of inter-subject registration of MRI brain images. The methods are designed to be fast and derive their speed from the uncoupling of the correspondence calculation and deformation interpolation procedures, each of which are then amenable to efficient implementation. The evaluation results indicate that this uncoupling does not significantly limit the registration accuracy that can be achieved.
Tangible solutions to image registration are paramount in longitudinal as well as multi-modal medical imaging studies. In this paper, we introduce tensor scale - a recently developed local morphometric parameter - in rigid image registration. A tensor scale-based registration method incorporates local structure size, orientation and anisotropy into the matching criterion, and therefore, allows efficient multi-modal image registration and holds potential to overcome the effects of intensity inhomogeneity in MRI. Two classes of two-dimensional image registration methods are proposed - (1) that computes angular shift between two images by correlating their tensor scale orientation histogram, and (2) that registers two images by maximizing the similarity of tensor scale features. Results of applications of the proposed methods on proton density and T2-weighted MR brain images of (1) the same slice of the same subject, and (2) different slices of the same subject are presented. The basic superiority of tensor scale-based registration over intensity-based registration is that it may allow the use of local Gestalts formed by the intensity patterns over the image instead of simply considering intensities as isolated events at the pixel level. This would be helpful in dealing with the effects of intensity inhomogeneity and noise in MRI.