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.