Automated breast segmentation in ultrasound computer tomography SAFT images

T. Hopp; W. You; M. Zapf; W. Y. Tan; H. Gemmeke; N. V. Ruiter

doi:10.1117/12.2254057

13 March 2017 Automated breast segmentation in ultrasound computer tomography SAFT images

T. Hopp, W. You, M. Zapf, W. Y. Tan, H. Gemmeke, N. V. Ruiter

Proceedings Volume 10139, Medical Imaging 2017: Ultrasonic Imaging and Tomography; 101390G (2017) https://doi.org/10.1117/12.2254057
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Ultrasound Computer Tomography (USCT) is a promising new imaging system for breast cancer diagnosis. An essential step before further processing is to remove the water background from the reconstructed images. In this paper we present a fully-automated image segmentation method based on three-dimensional active contours. The active contour method is extended by applying gradient vector flow and encoding the USCT aperture characteristics as additional weighting terms. A surface detection algorithm based on a ray model is developed to initialize the active contour, which is iteratively deformed to capture the breast outline in USCT reflection images. The evaluation with synthetic data showed that the method is able to cope with noisy images, and is not influenced by the position of the breast and the presence of scattering objects within the breast. The proposed method was applied to 14 in-vivo images resulting in an average surface deviation from a manual segmentation of 2.7 mm. We conclude that automated segmentation of USCT reflection images is feasible and produces results comparable to a manual segmentation. By applying the proposed method, reproducible segmentation results can be obtained without manual interaction by an expert.

Conference Presentation

Citation Download Citation

T. Hopp, W. You, M. Zapf, W. Y. Tan, H. Gemmeke, and N. V. Ruiter "Automated breast segmentation in ultrasound computer tomography SAFT images", Proc. SPIE 10139, Medical Imaging 2017: Ultrasonic Imaging and Tomography, 101390G (13 March 2017); https://doi.org/10.1117/12.2254057

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