Automated Identification of Fiducial Points on 3D Torso Images
Breast reconstruction is an important part of the breast cancer treatment process for many women. Recently, 2D and 3D images have been used by plastic surgeons for evaluating surgical outcomes. Distances between different fiducial points are frequently used as quantitative measures for characterizin...
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Online Access: | https://doi.org/10.4137/BECB.S11800 |
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doaj-09b9d6bf59cb4b25a50fd4b770b562242020-11-25T03:11:12ZengSAGE PublishingBiomedical Engineering and Computational Biology1179-59722013-01-01510.4137/BECB.S11800Automated Identification of Fiducial Points on 3D Torso ImagesManas M. Kawale0Gregory P. Reece1Melissa A. Crosby2Elisabeth K. Beahm3Michelle C. Fingeret4Mia K. Markey5Fatima A. Merchant6Department of Computer Science, University of Houston, Houston, TX, USA.Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.Department of Engineering Technology, University of Houston, Houston, TX, USA.Breast reconstruction is an important part of the breast cancer treatment process for many women. Recently, 2D and 3D images have been used by plastic surgeons for evaluating surgical outcomes. Distances between different fiducial points are frequently used as quantitative measures for characterizing breast morphology. Fiducial points can be directly marked on subjects for direct anthropometry, or can be manually marked on images. This paper introduces novel algorithms to automate the identification of fiducial points in 3D images. Automating the process will make measurements of breast morphology more reliable, reducing the inter- and intra-observer bias. Algorithms to identify three fiducial points, the nipples, sternal notch, and umbilicus, are described. The algorithms used for localization of these fiducial points are formulated using a combination of surface curvature and 2D color information. Comparison of the 3D coordinates of automatically detected fiducial points and those identified manually, and geodesic distances between the fiducial points are used to validate algorithm performance. The algorithms reliably identified the location of all three of the fiducial points. We dedicate this article to our late colleague and friend, Dr. Elisabeth K. Beahm. Elisabeth was both a talented plastic surgeon and physician-scientist; we deeply miss her insight and her fellowship.https://doi.org/10.4137/BECB.S11800 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Manas M. Kawale Gregory P. Reece Melissa A. Crosby Elisabeth K. Beahm Michelle C. Fingeret Mia K. Markey Fatima A. Merchant |
spellingShingle |
Manas M. Kawale Gregory P. Reece Melissa A. Crosby Elisabeth K. Beahm Michelle C. Fingeret Mia K. Markey Fatima A. Merchant Automated Identification of Fiducial Points on 3D Torso Images Biomedical Engineering and Computational Biology |
author_facet |
Manas M. Kawale Gregory P. Reece Melissa A. Crosby Elisabeth K. Beahm Michelle C. Fingeret Mia K. Markey Fatima A. Merchant |
author_sort |
Manas M. Kawale |
title |
Automated Identification of Fiducial Points on 3D Torso Images |
title_short |
Automated Identification of Fiducial Points on 3D Torso Images |
title_full |
Automated Identification of Fiducial Points on 3D Torso Images |
title_fullStr |
Automated Identification of Fiducial Points on 3D Torso Images |
title_full_unstemmed |
Automated Identification of Fiducial Points on 3D Torso Images |
title_sort |
automated identification of fiducial points on 3d torso images |
publisher |
SAGE Publishing |
series |
Biomedical Engineering and Computational Biology |
issn |
1179-5972 |
publishDate |
2013-01-01 |
description |
Breast reconstruction is an important part of the breast cancer treatment process for many women. Recently, 2D and 3D images have been used by plastic surgeons for evaluating surgical outcomes. Distances between different fiducial points are frequently used as quantitative measures for characterizing breast morphology. Fiducial points can be directly marked on subjects for direct anthropometry, or can be manually marked on images. This paper introduces novel algorithms to automate the identification of fiducial points in 3D images. Automating the process will make measurements of breast morphology more reliable, reducing the inter- and intra-observer bias. Algorithms to identify three fiducial points, the nipples, sternal notch, and umbilicus, are described. The algorithms used for localization of these fiducial points are formulated using a combination of surface curvature and 2D color information. Comparison of the 3D coordinates of automatically detected fiducial points and those identified manually, and geodesic distances between the fiducial points are used to validate algorithm performance. The algorithms reliably identified the location of all three of the fiducial points. We dedicate this article to our late colleague and friend, Dr. Elisabeth K. Beahm. Elisabeth was both a talented plastic surgeon and physician-scientist; we deeply miss her insight and her fellowship. |
url |
https://doi.org/10.4137/BECB.S11800 |
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