Breast ultrasound image segmentation: an optimization approach based on super-pixels and high-level descriptors

Joan Massich; Guillaume Lemaître; Joan Martí; Fabrice Mériaudeau

doi:10.1117/12.2182843

30 April 2015 Breast ultrasound image segmentation: an optimization approach based on super-pixels and high-level descriptors

Joan Massich, Guillaume Lemaître, Joan Martí, Fabrice Mériaudeau

Author Affiliations +

Proceedings Volume 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015; 95340C (2015) https://doi.org/10.1117/12.2182843
Event: The International Conference on Quality Control by Artificial Vision 2015, 2015, Le Creusot, France

Abstract

Breast cancer is the second most common cancer and the leading cause of cancer death among women. Medical imaging has become an indispensable tool for its diagnosis and follow up. During the last decade, the medical community has promoted to incorporate Ultra-Sound (US) screening as part of the standard routine. The main reason for using US imaging is its capability to differentiate benign from malignant masses, when compared to other imaging techniques. The increasing usage of US imaging encourages the development of Computer Aided Diagnosis (CAD) systems applied to Breast Ultra-Sound (BUS) images. However accurate delineations of the lesions and structures of the breast are essential for CAD systems in order to extract information needed to perform diagnosis. This article proposes a highly modular and flexible framework for segmenting lesions and tissues present in BUS images. The proposal takes advantage of optimization strategies using super-pixels and high-level descriptors, which are analogous to the visual cues used by radiologists. Qualitative and quantitative results are provided stating a performance within the range of the state-of-the-art.

Citation Download Citation

Joan Massich, Guillaume Lemaître, Joan Martí, and Fabrice Mériaudeau "Breast ultrasound image segmentation: an optimization approach based on super-pixels and high-level descriptors", Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 95340C (30 April 2015); https://doi.org/10.1117/12.2182843

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