Structural Representation: Reducing Multi-Modal Image Registration to Mono-Modal Problem

Structural Representation: Reducing Multi-Modal Image Registration to Mono-Modal Problem

Registration of multi-modal images has been a challenging task due to the complex intensity relationship between images. The standard multi-modal approach tends to use sophisticated similarity measures, such as mutual information, to assess the accuracy of the alignment. Employing such measures imply the increase in the computational time and complexity, and makes it highly difficult for the op...

Multi-modal Image Registration

Multi-modal Image Registration for Correlative Microscopy

Correlative microscopy is a methodology combining the functionality of light microscopy with the high resolution of electron microscopy and other microscopy technologies. Image registration for correlative microscopy is quite challenging because it is a multi-modal, multi-scale and multi-dimensional registration problem. In this report, I introduce two methods of image registration for correlat...

Manifold Learning for Multi-Modal Image Registration

The standard approach to multi-modal registration is to apply sophisticated similarity metrics such as mutual information. The disadvantage of these measures, in contrast to simple L1 or L2 norm, is the increased computational complexity and consequently the prolongation of the registration time. An alternative approach, which has so far not yet gained much attention in the literature, is to fi...

Multi-Modal Image Registration and Atlas Formation