Abstract
The advantages of ultrasound (US) over other medical imaging modalities have provided a platform for its wide use in many medical fields, both for diagnostic and therapeutic purposes. However one of the limiting factors which has affected wide adoption of this cost-effective technology is requiring highly skilled sonographers and operators. We consider this problem in this paper which is motivated by advancements within the computer vision community. Our approach combines simple and standardized clinical ultrasound procedures with machine learning driven imaging solutions to provide users who have limited clinical experience, to perform simple diagnostic decisions (such as detection of a fetal breech presentation). We introduce LP-SIFT features constructed using the well-known SIFT features, utilizing a set of feature symmetry filters. We also illustrate how such features can be used in a bag of visual words representation on ultrasound images for classification of anatomical structures that have significant clinical implications in fetal health such as the fetal head, heart and abdomen, despite the high presence of speckle, shadows and other imaging artifacts in ultrasound images.
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Acknowledgments
Mohammad Ali Maraci acknowledges the support of RCUK Digital Economy Programme grant number EP/G036861/1 (Oxford Centre for Doctoral Training in Healthcare Innovation). The authors also acknowledges that the data was acquired as part of the Intergrowth-21st study.
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Maraci, M.A., Napolitano, R., Papageorghiou, A., Noble, J.A. (2014). Object Classification in an Ultrasound Video Using LP-SIFT Features. In: Menze, B., et al. Medical Computer Vision: Algorithms for Big Data. MCV 2014. Lecture Notes in Computer Science(), vol 8848. Springer, Cham. https://doi.org/10.1007/978-3-319-13972-2_7
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DOI: https://doi.org/10.1007/978-3-319-13972-2_7
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