Image Analysis Using Machine Learning: Anatomical landmarks detection in fetal ultrasound images

被引：8

作者：

Rahmatullah, Bahbibi ^{[1
]}

Papageorghiou, Aris T. ^{[2
]}

Noble, J. Alison ^{[1
]}

机构：

[1] Univ Oxford, Dept Engn Sci, Inst Biomed Engn, Parks Rd, Oxford OX1 3PJ, England

[2] Univ Oxford, John Radcliffe Hosp, Nuffield Dept Obstet & Gynaecol, Oxford OX3 9DU, England

来源：

2012 IEEE 36TH ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC) | 2012年

关键词：

component; image analysis; machine learning; ultrasound; detection;

D O I：

10.1109/COMPSAC.2012.52

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Accurate and robust image analysis software is crucial for assessing the quality of ultrasound images of fetal biometry. In this work, we present the result of our automated image analysis method based on a machine learning algorithm in detecting important anatomical landmarks employed in manual scoring of ultrasound images of the fetal abdomen. Experimental results on 2384 images are promising and the clinical validation using 300 images demonstrates a high level agreement between the automated method and experts.

引用

页码：354 / +

页数：2