Authors:
Liliana Lo Presti
and
Marco La Cascia
Affiliation:
University of Palermo, Italy
Keyword(s):
Image Registration, Mutual Information, Medical Images.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Medical Imaging
;
Pattern Recognition
;
Software Engineering
;
Stochastic Methods
;
Theory and Methods
Abstract:
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.
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