Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/75002
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Type: Journal article
Title: Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree
Author: Carneiro, G.
Georgescu, B.
Good, S.
Comaniciu, D.
Citation: IEEE Transactions on Medical Imaging, 2008; 27(9):1342-1355
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Issue Date: 2008
ISSN: 0278-0062
1558-254X
Statement of
Responsibility: 
Gustavo Carneiro, Bogdan Georgescu, Sara Good and Dorin Comaniciu
Abstract: We propose a novel method for the automatic detection and measurement of fetal anatomical structures in ultrasound images. This problem offers a myriad of challenges, including: difficulty of modeling the appearance variations of the visual object of interest, robustness to speckle noise and signal dropout, and large search space of the detection procedure. Previous solutions typically rely on the explicit encoding of prior knowledge and formulation of the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are constrained by the validity of the underlying assumptions and usually are not enough to capture the complex appearances of fetal anatomies.We propose a novel system for fast automatic detection and measurement of fetal anatomies that directly exploits a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns automatically to distinguish between the appearance of the object of interest and background by training a constrained probabilistic boosting tree classifier. This system is able to produce the automatic segmentation of several fetal anatomies using the same basic detection algorithm. We show results on fully automatic measurement of biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), humerus length (HL), and crown rump length (CRL). Notice that our approach is the first in the literature to deal with the HL and CRL measurements. Extensive experiments (with clinical validation) show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer.
Keywords: Discriminative classifier; medical image analysis; supervised learning; top-down image segmentation; visual object recognition
Rights: © IEEE
RMID: 0020114291
DOI: 10.1109/TMI.2008.928917
Appears in Collections:Computer Science publications

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