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Proceedings Paper

Spatial composition of US images using probabilistic weighted means
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Paper Abstract

Ultrasound (US) images of the fetal brain provide the experts with valuable indicators of the fetal development. However as the skull thickens, it obstructs the transmission of the acoustic waves, which in turn occludes the anatomy behind the thickened fetal skull. A viable option to improve the visibility of the fetal brain, before complete calcification of the skull, is the calculation of a compounded image made of different views of the same anatomical plane. In this work we report a new method for the composition of ultrasound images based on the Weighted Mean of the pixels, from different views, which correspond to each position (x, y) in the final compounded image. A support vector machine (SVM) is used to calculate the weights of each pixel from a different view, based on intensity, entropy and variance features. We present the initial test results of our method on synthetic US images of a head phantom, contaminated with speckle noise; we report the signal to noise ratio (SNR) and the normalized mutual information (NMI), for different number of views (2, 3, and 5), and compare the results against images compounded using the Mean, Root Mean Square (RMS), and Geometrical Mean composition methods. With our scheme we were able to recover the occluded information to increase the NMI from 16% to 26%, representing a 58% improvement.

Paper Details

Date Published: 22 December 2015
PDF: 7 pages
Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 968111 (22 December 2015); doi: 10.1117/12.2207958
Show Author Affiliations
J. L. Perez-Gonzalez, Univ. Autónoma Metropolitana (Mexico)
Fernando Arámbula Cosío, Univ. Nacional Autónoma de México (Mexico)
V. Medina-Bañuelos, Univ. Autónoma Metropolitana (Mexico)

Published in SPIE Proceedings Vol. 9681:
11th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Juan D. García-Arteaga; Jorge Brieva, Editor(s)

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