title: Automated calculation of ptosis on lateral clinical photographs.
abstract: OBJECTIVE
  The goal is to fully automate the calculation of a breast ptosis measure from clinical photographs through automatic localization of fiducial points relevant to the measure.
  
  
  METHODS
  Sixty-eight women (97 clinical photographs) who underwent or were scheduled for breast reconstruction were included. The photographs were divided into a development set (N = 49) and an evaluation set (N = 48). The breast ptosis measure is obtained automatically from distances between three fiducial points: the nipple, the lowest visible point of breast (LVP), and the lateral terminus of the inframammary fold (LT). The nipple is localized using the YIQ colour space to highlight the contrast between the areola and the surrounding breast skin. The areola is localized using its shape, location and high Q component intensity. The breast contour is estimated using Dijkstra's shortest path algorithm on the gradient of the photograph in greyscale. The lowest point of the estimated contour is set as the LVP. To locate the anatomically subtle LT, the location of patient's axilla is used as a reference.
  
  
  RESULTS
  The algorithm's efficacy was evaluated by comparing manual and automated localizations of the fiducial points. The average nipple diameter was used as a cut-off to define success. The algorithm showed 90, 91 and 83% accuracy for locating the nipple, LVP and LT in the evaluation set, respectively.
  
  
  CONCLUSIONS
  This study presents a new automated algorithm that may facilitate the quantification of breast ptosis from lateral views of patients' photographs.
authors.affiliation: Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.|Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Juhun
authors.identifier: http://orcid.org/0000-0001-7151-0540
authors.initials: J
authors.lastname: Lee
authors.affiliation: Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Edward
authors.initials: E
authors.lastname: Kim
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 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
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001940:Breast / Q000033:anatomy & histology*; D001943:Breast Neoplasms / Q000601:surgery; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016462:Mammaplasty / Q000379:methods*; D008875:Middle Aged; D009558:Nipples / Q000033:anatomy & histology; D010781:Photography
keywords: automated detection; breast cancer; digital photographs; lateral terminus; nipple; ptosis