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Proceedings Paper

Fully automatic segmentation and measurement of the fetal femur
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Paper Abstract

Ultrasound (US) images are necessary in obstetrics because they provide the most important clinical parameters for fetal health assessment during the second and third trimesters: head circumference, biparietal diameter, abdominal circumference and femur length. These fetometric indices are helpful for gestational age and fetal weight estimation; they are also helpful for obstetricians to diagnose fetal development abnormalities. However, these indices are obtained manually, which provokes high intra and interobserver variability and lack of repeatability. A fully automatic method to segment and measure femur’s length is presented in this paper. The proposed methodology incorporates texture information and introduces a novel curvature analysis to adequately detect the femur. It consists on pre–processing US images with an anisotropic diffusion filter, followed by morphological operations and thresholding to isolate femur–candidate regions. A normalized metric composed of intensity, length, centroid position and entropy is assigned to each region in order to select the most probable candidate to be femur. This selected region is afterwards thinned to a one–pixel line, whose curvature is analyzed with an angle threshold criterion to accurately locate femur’s extrema. The method was tested on 64 US images (20 taken on the second and 44 on the third trimester of pregnancy); a correlation coefficient of 0.984 and an error of 1.016±2.764 mm were achieved between expert–obtained manual measures and automatically calculated indices. Results are consistent, outperform those reported previously by other authors and show a high correlation with measures obtained by experts; therefore, the developed method is suitable to be adapted for clinical use.

Paper Details

Date Published: 21 December 2018
PDF: 8 pages
Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 1097512 (21 December 2018); doi: 10.1117/12.2511534
Show Author Affiliations
Daniel Colín Garnica, Univ. Autónoma Metropolitana (Mexico)
Jorge Perez-Gonzalez, Univ. Autónoma Metropolitana (Mexico)
Tecnológico de Monterrey (Mexico)
Scarlet Prieto Rodríguez, Instituto Nacional de Perinatología (Mexico)
Lisbeth Camargo Marín, Instituto Nacional de Perinatología (Mexico)
Mario Guzmán Huerta, Instituto Nacional de Perinatología (Mexico)
Alma Delia Javier, Univ. Autónoma Metropolitana (Mexico)
Raquel Valdés Cristerna, Univ. Autónoma Metropolitana (Mexico)
Verónica Medina-Bañuelos, Univ. Autónoma Metropolitana (Mexico)


Published in SPIE Proceedings Vol. 10975:
14th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva, Editor(s)

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