  Image registration is a process of aligning two or more images of same
objects using geometric transformation. Most of the existing approaches work on
the assumption of location invariance. These approaches require object-centric
images to perform matching. Further, in absence of intensity level symmetry
between the corresponding points in two images, the learning based registration
approaches rely on synthetic deformations, which often fail in real scenarios.
To address these issues, a combination of convolutional neural networks (CNNs)
to perform the desired registration is developed in this work. The complete
objective is divided into three sub-objectives: object localization,
segmentation and matching transformation. Object localization step establishes
an initial correspondence between the images. A modified version of single shot
multi-box detector is used for this purpose. The detected region is cropped to
make the images object-centric. Subsequently, the objects are segmented and
matched using a spatial transformer network employing thin plate spline
deformation. Initial experiments on MNIST and Caltech-101 datasets show that
the proposed model is able to produce accurate matching. Quantitative
evaluation performed using dice coefficient (DC) and mean intersection over
union (mIoU) show that proposed method results in the values of 79% and 66%,
respectively for MNIST dataset and the values of 94% and 90%, respectively for
Caltech-101 dataset. The proposed framework is extended to the registration of
CT and US images, which is free from any data specific assumptions and has
better generalization capability as compared to the existing
rule-based/classical approaches.
