|Table of Contents|

Automatic measurement of fetal femur length in ultrasound image(PDF)

《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

Issue:
2017年No.4(331-440)
Page:
421-427
Research Field:
电子与信息科学
Publishing date:

Info

Title:
Automatic measurement of fetal femur length in ultrasound image
Author(s):
Luo Na1 Li Jing1 Zhou Chengli2 Zheng Jiezhi1 and Ni Dong1
1) National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China
2) Department of Ultrasound, Shenzhen Maternity & Child Healthcare Hospital, Shenzhen 518000, Guangdong Province, P.R.China
Keywords:
biomedical engineering ultrasound images image processing femur length measurement Frangi filter prenatal diagnosis
PACS:
R 318; TP 751
DOI:
10.3724/SP.J.1249.2017.04421
Abstract:
We propose a novel automatic method to measure the fetal femur length. First, the candidate regions containing the femur are detected in the ultrasound image using Frangi filter and gray information. Then, the femur region is localized based on both the shape and position of the candidate regions. Finally, the femur end points are determined by detecting the edges of the femur region and fitting the femur skeleton. The femur length is measured. Comparing with the results measured by doctors, the average measurement error of 70 ultrasound femur images is 1.18 mm, which indicates that our method can accurately measure the femur length.

References:

[1] 倪东,陈思平,汪天富.基于曲光线跟踪算法的超声成像实时模拟研究[J].深圳大学学报理工版,2012,29(4):322-327.
Ni Dong, Chen Siping, Wang Tianfu. A beam width aware curvilinear ray tracing method for real-time ultrasound simulation[J]. Journal of Shenzhen University Science and Engineering, 2012, 29(4): 322- 327.(in Chinese)
[2] 万仪芳,沈国芳,朱家安.超声测量评估胎儿主要生长参数的研究[J].上海医学,2010,33(12):1132-1134.
Wan Yifang, Shen Guofang, Zhu Jiaan. Ultrasonography determination in evaluation of major growth parameters of fetuses[J]. Shanghai Medical Journal, 2010, 33(12): 1132-1134.(in Chinese)
[3] 詹林,文桂琼,林毅,等.产前超声筛查诊断胎儿肢体畸形的价值[J].中国医学影像学杂志,2010,18(3):213-216.
Zhan Lin, Wen Guiqiong, Lin Yi. et al. The effectiveness of ultrasound screening in the prenatal diagnosis of fetal malformation[J]. Chinese Journal of Medical Imaging, 2010, 18(3): 213-216.(in Chinese)
[4] 李威,贾淑文,吕祥.产前超声检查对胎儿畸形的诊断价值[J].中国妇幼保健,2013,28(8):1290-1292.
Li Wei, Jia Shuwen, Lv Xiang. The effectiveness of ultrasound screening in the prenatal diagnosis of fetal malformation[J]. Maternal and Child Health Care of China, 2013, 28(8): 1290-1292.(in Chinese)
[5] 李璟,倪东,李胜利,等.超声图像中胎儿头围的自动测量[J].深圳大学学报理工版,2014,31(5):455-463.
Li Jing, Ni Dong, Li Shengli, et al. The automatic ultrasound measurement of fetal head circumference[J]. Journal of Shenzhen University Science and Engineering, 2014, 31(5): 455-463.(in Chinese)
[6] Maurits V T, Antti M, Bart K. Repetitive strain injury[J]. Lancet, 2007, 369(9575): 1815-1822.
[7] Zhang Lei, Ye Xujiong, Lambrou T, et al. A supervised texton based approach for automatic segmentation and measurement of the fetal head and femur in 2D ultrasound images[J]. Physics in Medicine and Biology, 2016, 61(3): 1095-1115.
[8] Wang C W. Automatic entropy-based femur segmentation and fast length measurement for fetal ultrasound images[C]// International Conference on Advanced Robotics and Intelligent Systems.[S.l.]: IEEE, 2014: 1-5.
[9] 余锦华,汪源源,陈萍,等.胎儿超声图像分割及自动径线测量[J].中国生物医学工程学报,2007,26(6):867-873.
Yu Jinhua, Wang Yuanyuan, Chen Ping, et al. Fetal ultrasound image segmentation and automatic diameter and length measurement[J]. Chinese Journal of Biomedical Engineering, 2007, 26(6): 867-873.(in Chinese)
[10] Salomon L J, Alfirevic Z, Berghella V, et al. Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan[J]. Ultrasound in Obstetrics & Gynecology: the Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology, 2011, 37(1): 116-126.
[11] Frangi A F, Niessen W J, Vincken K L, et al. Multiscale vessel enhancement filtering[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin: Springer Berlin Heidelberg, 2000: 130-137.
[12] Jimenez-Carretero D, Santos A, Kerkstra S, et al. 3D Frangi-based lung vessel enhancement filter penalizing airways[C]// IEEE the 10th International Symposium on Biomedical Imaging.[S. l.]: IEEE, 2013: 926-929.
[13] Shashank, Bhattacharya M, Sharma G K. Optimized coronary artery segmentation using Frangi filter and Anisotropic diffusion filtering[C]// International Symposium on Computational and Business Intelligence. New Delhi, India: IEEE, 2013: 261-264.
[14] 吕哲,王福利,常玉清,等.改进的形态学骨架提取算法[J].计算机工程,2009,35(19):23-25.
Lv Zhe, Wang Fuli, Chang Yuqing, et al. Improved morphological skeleton extraction algorithm[J]. Computer Engineering, 2009, 35(19): 23-25.(in Chinese)
[15] Liu Hongzhi, Wu Zhonghai, Zhang Xing, et al. A skeleton pruning algorithm based on information fusion[J]. Pattern Recognition Letters, 2013, 34(10): 1138-1145.
[16] Udupa J K, Leblanc V R, Ying Zhuge, et al. A framework for evaluating image segmentation algorithms[J]. Computerized Medical Imaging and Graphics, 2006, 30(2): 75-87.
[17] Heimann T, Van Ginneken B, Styner M A, et al. Comparison and evaluation of methods for liver segmentation from CT datasets[J]. IEEE Transactions on Medical Imaging, 2009, 28(8):1251-1265.
[18] Myles P S, Cui J. Using the Bland-Altman method to measure agreement with repeated measure[J]. British Journal of Anaesthesia, 2007, 99(3): 309-311.
[19] Rueda S, Fathima S, Knight C L, et al. Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge[J]. IEEE Transactions on Medical Imaging,?2014, 33(4): 797-813.
[20] Székely G J, Rizzo M L. The distance correlation t-test of independence in high dimension[J]. Journal of Multivariate Analysis, 2013, 117(3): 193-213.

Memo

Memo:
-
Last Update: 2017-06-26