Sharif Digital Repository

Image registration is one of the fundamental and essential tasks for medical imaging and remote sensing applications. One of the most common challenges in this area is the presence of complex spatially varying intensity distortion in the images. The widely used similarity metrics, such as MI (Mutual Information), CC (Correlation Coefficient), SSD (Sum of Square Difference), SAD (Sum of Absolute Difference) and CR (Correlation Ratio), are not robust against this kind of distortion; because stationarity assumption and the pixel-wise independence cannot be obeyed and captured by these metrics. In this research, we proposed a new intensity-based method for simultaneous image registration and... 

Image Registration is a fundamental task in numerous applications in medical image processing and is defined as the process of determining the correspondence of between images collected at different times or using different imaging modalities. This correspondence can be used for aligning images so that the pair can be directly compared, combined or analyzed. Image-guided surgery systems use registration for establishing an accurate relation between preoperative and intraoperative image space. There have been several methods for rigid registration which are not easily applicable for soft tissues. Indeed, nonrigid deformation of soft tissues will endanger the accuracy of rigid methods, so it... 

Image registration is the process of matching the coordinate systems of two or more images. Medical image registration has been used in a variety of applications such as segmentation, motion tracking and etc. Recently, the use of deep neural networks has been demonstrated as a useful approach to registration problems. In this work, we propose two separate novel Convolutional Neural Network (CNN) architectures for multi-modal rigid and affine registration of the CT-MRI images of the brain. A dataset consisting of CT-MRI images of 37 subjects was used for training and evaluation of the networks. For both networks, the proposed models achieved high mutual information value between predicted CT...