Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Original publication




Conference paper

Publication Date