Applications of Wavelet Transform in Registration, Segmentation, Denoising, and Compression of Medical Images - Proceedings of All India Seminar on Biomedical Engineering 2012 (AISOBE-2012) - page 113

Fig. 1 Example of a multiscale edge detection method finding local maxima of wavelet
modulus, with a first-derivative wavelet function. a Input image. b-e Multiscale edge map at
expansion scale 1-4
• Multimodalities study.
• Cross-subject normalization and template/atlas analysis.
• Patient monitoring over time with tracking of the pathological evolution for the
same patient and the same modality.
Many registration methods follow a feature matching procedure. Feature points
(often referred to as ''control points'', or CP) are first identified in both the ref-
erence image and the input image. An optimal spatial transformation (rigid or non-
rigid) is then computed that can connect and correlate the two sets of control points
with minimal error. Registration has always been considered as very costly in
terms of computational load. Besides, when the input image is highly deviated
from the reference image, the optimization process can be easily trapped into local
minima before reaching the correct transformation mapping. Both issues can be
alleviated by embedding the registration into a ''coarse to fine'' procedure.
Registration at higher resolution is initialized with the result from the lower
resolution, and only need to refine the mapping between the two images with local
deformations for updating the transformation parameters.