Automated calculation of ptosis on lateral clinical photographs. Issue 5 (17th June 2015)

• Record Type: Journal Article
• Authors: Lee, Juhun; Kim, Edward; Reece, Gregory P.; Crosby, Melissa A.; Beahm, Elisabeth K.; Markey, Mia K.
• Is Part Of: Journal of evaluation in clinical practice. Volume 21:Issue 5(2015)
• Page Start: 900
• Page End: 910
• Publication Date: 2015-06-17
• Subjects: Clinical medicine -- Periodicals; 616.005
• DOI: 10.1111/jep.12397
• Languages: English
• ISSNs: 1356-1294

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <sec id="jep12397-sec-0001" sec-type="section"> <title>Rationale, aims and objectives</title> <p>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.</p> </sec> <sec id="jep12397-sec-0002" sec-type="section"> <title>Methods</title> <p>Sixty‐eight women (97 clinical photographs) who underwent or were scheduled for breast reconstruction were included. The photographs were divided into a development set (<italic>N</italic> = 49) and an evaluation set (<italic>N</italic> = 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.</p> </sec> <sec id="jep12397-sec-0003" sec-type="section"> <title>Results</title> <p>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.</p> </sec> <sec id="jep12397-sec-0004" sec-type="section"> <title>Conclusion</title> <p>This study presents a new automated algorithm that may facilitate the quantification of breast ptosis from lateral views of patients' photographs.</p> </sec> </abstract>