Image registration is the process of finding the geometric transformation that, applied to the floating image, gives the registered image with the highest similarity to the reference image. Registering a pair of images involves the definition of a similarity function in terms of the parameters of the geometric transformation that allows the registration. This paper proposes to register a pair of images by iteratively maximizing the empirical mutual information through coordinate gradient descent. Hence, the registered image is obtained by applying a sequence of local affine transformations. Rather than adopting a uniformly spaced grid to select image blocks to locally register, as done by state-of-the-art techniques, this paper proposes a method which is similar in spirit to boosting strategies used in classification. In this work, a probability distribution over the pixels of the registered image is maintained. At each pixel, this distribution represents the probability that a local affine transformation of a block centered on this pixel should be computed to improve the similarity between the registered and the reference images. The distribution is updated iteratively during the registration process to move probability mass towards pixels unaffected by the estimated local transformation. The paper presents preliminary results by a qualitative evaluation on several pairs of medical images acquired by different sources.
|Number of pages||7|
|Publication status||Published - 2018|
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition