A Differential Geometry Approach for Change Detection in Medical Images

Abstract

Change detection is of paramount importance in medical imaging, serving as a non-invasive quantifiable powerful tool in diagnosis and in assessment of the outcome of treatment of tumors. We present a new quantitative method for detecting changes in volumetric medical data and in clustering of anatomical structures, based on assessment of volumetric distortions that are required in order to deform a test three-dimensional medical dataset segment onto its previously-acquired reference, or a given prototype in the case clustering. Unlike the voxel-based classical techniques of shape comparison, our algorithm operates on tetrahedral meshes and can, therefore be applied on both closed, simply-connected, surfaces and in volumetric domains with more sophisticated boundaries.