Rigid Registration of Renal Perfusion Images Using a Neurobiology-Based Visual Saliency Model

摘要

General mutual information- (MI-) based registration methods treat all voxels equally. But each voxel has a different utility depending upon the task. Because of its robustness to noise, low computation time, and agreement with human fixations, the Itti-Koch visual saliency model is used to determine voxel utility of renal perfusion data. The model is able to match identical regions in spite of intensity change due to its close adherence to the center-surround property of the visual cortex. Saliency value is used as a pixel's utility measure in an MI framework for rigid registration of renal perfusion data exhibiting rapid intensity change and noise. We simulated varying degrees of rotation and translation motion under different noise levels, and a novel optimization technique was used for fast and accurate recovery of registration parameters. We also registered real patient data having rotation and translation motion. Our results show that saliency information improves registration accuracy for perfusion images and the Itti-Koch model is a better indicator of visual saliency than scale-space maps.