Fully automatic segmentation and measurement of the fetal femur

Daniel Colín Garnica; Jorge Perez-Gonzalez; Scarlet Prieto Rodríguez; Lisbeth Camargo Marín; Mario Guzmán Huerta; Alma Delia Javier; Raquel Valdés Cristerna; Verónica Medina-Bañuelos

doi:10.1117/12.2511534

21 December 2018 Fully automatic segmentation and measurement of the fetal femur

Daniel Colín Garnica, Jorge Perez-Gonzalez, Scarlet Prieto Rodríguez, Lisbeth Camargo Marín, Mario Guzmán Huerta, Alma Delia Javier, Raquel Valdés Cristerna, Verónica Medina-Bañuelos

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Proceedings Volume 10975, 14th International Symposium on Medical Information Processing and Analysis; 1097512 (2018) https://doi.org/10.1117/12.2511534
Event: 14th International Symposium on Medical Information Processing and Analysis, 2018, Mazatlán, Mexico

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.

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Daniel Colín Garnica, Jorge Perez-Gonzalez, Scarlet Prieto Rodríguez, Lisbeth Camargo Marín, Mario Guzmán Huerta, Alma Delia Javier, Raquel Valdés Cristerna, and Verónica Medina-Bañuelos "Fully automatic segmentation and measurement of the fetal femur ", Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 1097512 (21 December 2018); https://doi.org/10.1117/12.2511534

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KEYWORDS

Image segmentation

Fetus

Bone

Statistical analysis

Anisotropic diffusion

Anisotropic filtering

Image filtering

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