title: Automated Identification of Fiducial Points on 3D Torso Images.
abstract: 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 co-ordinates 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.
authors.affiliation: Department of Computer Science, University of Houston, Houston, TX, USA.
authors.forename: Manas M
authors.initials: MM
authors.lastname: Kawale
authors.affiliation: Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Gregory P
authors.initials: GP
authors.lastname: Reece
authors.affiliation: Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Melissa A
authors.initials: MA
authors.lastname: Crosby
authors.affiliation: Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Elisabeth K
authors.initials: EK
authors.lastname: Beahm
authors.affiliation: Department of Behavioral Science, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Michelle C
authors.initials: MC
authors.lastname: Fingeret
authors.affiliation: Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA. ; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Mia K
authors.initials: MK
authors.lastname: Markey
authors.affiliation: Department of Computer Science, University of Houston, Houston, TX, USA. ; Department of Engineering Technology, University of Houston, Houston, TX, USA.
authors.forename: Fatima A
authors.initials: FA
authors.lastname: Merchant
keywords: 3D surface mesh; breast morphology; breast reconstruction; curvature; landmark detection