Automatic Fetal Head Circumference Measurement in Ultrasound Using Random Forest and Fast Ellipse Fitting

Jing Li, Yi Wang, Baiying Lei, Jie Zhi Cheng, Jing Qin, Tianfu Wang, Shengli Li, Dong Ni

Research output: Journal article publicationJournal articleAcademic researchpeer-review

36 Citations (Scopus)

Abstract

Head circumference (HC) is one of the most important biometrics in assessing fetal growth during prenatal ultrasound examinations. However, the manual measurement of this biometric by doctors often requires substantial experience. We developed a learning-based framework that used prior knowledge and employed a fast ellipse fitting method (ElliFit) to measure HC automatically. We first integrated the prior knowledge about the gestational age and ultrasound scanning depth into a random forest classifier to localize the fetal head. We further used phase symmetry to detect the center line of the fetal skull and employed ElliFit to fit the HC ellipse for measurement. The experimental results from 145 HC images showed that our method had an average measurement error of 1.7 mm and outperformed traditional methods. The experimental results demonstrated that our method shows great promise for applications in clinical practice.
Original languageEnglish
Article number7927411
Pages (from-to)215-223
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Volume22
Issue number1
DOIs
Publication statusPublished - 1 Jan 2018

Keywords

  • Ellipse fitting
  • fetal head
  • head circumference
  • machine learning
  • object localization
  • random forest
  • ultrasound

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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