In recent years, fetal diagnostics have relied heavily on clinical assessment and biometric analysis of manually acquired
ultrasound images. There is a profound need for automated and standardized evaluation tools to characterize fetal growth
and development. This work addresses this need through the novel use of feature-based techniques to develop evaluators
of fetal brain gestation. The methodology is comprised of an automated database-driven 2D/3D image atlas construction
method, which includes several iterative processes. A unique database was designed to store fetal image data acquired as
part of the Intergrowth-21st study. This database drives the proposed automated atlas construction methodology using
local phase information to perform affine registration with normalized mutual information as the similarity parameter,
followed by wavelet-based image fusion and averaging. The unique feature-based application of local phase and wavelet
fusion towards creating the atlas reduces the intensity dependence and difficulties in registering ultrasound images. The
method is evaluated on fetal transthalamic head ultrasound images of 20 weeks gestation. The results show that the
proposed method is more robust to intensity variations than standard intensity-based methods. Results also suggest that
the feature-based approach improves the registration accuracy needed in creating a clinically valid ultrasound image
atlas.
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