Learning Similarity Measure for Multi-Modal 3D Image Registration

Multi-modal image registration is a challenging problem in medical imaging. The goal is to align anatomically identical structures; however, their appearance in images acquired with different imaging devices, such as CT or MR, may be very different. Registration algorithms generally deform one image, the floating image, such that it matches with a second, the reference image, by maximizing some similarity score between the deformed and the reference image. Instead of using a universal, but a priori fixed similarity criterion such as mutual information, we propose learning a similarity measure in a discriminative manner such that the reference and correctly deformed floating images receive high similarity scores. To this end, we develop an algorithm derived from max-margin structured output learning, and employ the learned similarity measure within a standard rigid registration algorithm. Compared to other approaches, our method adapts to the specific registration problem at hand and ex...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Bernhard Schölkopf, Daewon Lee, Florian Steinke, Matthias Hofmann, Nathan D. Cahill, Yasemin Altun
• Keywords: Computer Vision, CVPR 2009, Medical Image Registration, Similarity Measure, Structured Output Prediction