Automated Detection of Breast Contour in 3D Images of the Female Torso - 13.273

L. Zhao et al., "Automated Detection of Breast Contour in 3D Images of the Female Torso", in Proc. of 4th Int. Conf. on 3D Body Scanning Technologies, Long Beach CA, USA, 2013, pp. 273-278, https://doi.org/10.15221/13.273.

Title:

Automated Detection of Breast Contour in 3D Images of the Female Torso

Authors:

Lijuan ZHAO 1, Shishir K. SHAH 1, Gregory P. REECE 2, Melissa A. CROSBY 2, Michelle C. FINGERET 2,3, Fatima A. MERCHANT 1,4

1 Dept. of Computer Science, University of Houston, Houston (TX), USA;
2 Dept. of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston (TX), USA;
3 Dept. of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston (TX), USA;
4 Dept. of Engineering Technology, University of Houston, Houston (TX), USA

Abstract:

Stereophotogrammetry is finding increasing use in plastic surgery, both for breast reconstruction after oncological procedures and cosmetic augmentation/reduction. The ability to visualize and quantify morphological features of the breast facilitates pre-operative planning and post-operative outcome assessment. Breast contour is an important attribute for quantitative assessment of breast aesthetics. Based on the detected breast contour, relevant morphological measures such as breast size, shape, symmetry, volume and ptosis can be determined. In this study we present an approach for the automatic contour detection of the lower breast in three-dimensional (3D) images. Our approach employs surface curvature analysis. We first identify the points with the lowest Gaussian curvature within the one-ring neighborhood on the surface mesh, and then apply the random sample consensus (RANSAC) algorithm to non-deterministically estimate the lower breast contour from the set of low curvature points.

Keywords:

3D image, breast contour detection, Gaussian curvature, RANSAC algorithm

Details:

Full paper: 13.273.pdf
Proceedings: 3DBST 2013, 19-20 Nov. 2013, Long Beach California, USA
Pages: 273-278
DOI: https://doi.org/10.15221/13.273

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Proceedings: © Hometrica Consulting - Dr. Nicola D'Apuzzo, Switzerland, hometrica.ch.
Authors retain all rights to individual papers, which are licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The papers appearing in the proceedings reflect the author's opinions. Their inclusion in the proceedings does not necessary constitute endorsement by the editor or by the publisher.

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