Abstract
A semi-automated fetal ultrasound image segmentation system is developed to improve the estimation of fetal weight (EFW). Four standardized fetal parameters are measured by the proposed segmentation system: biparietal diameter, head circumference, abdominal circumference and femur length. Computerized measurements of 215 fetuses are compared with manual measurements in term of fitness analysis and difference analysis. Among 215 cases, computerized measurements of 103 fetuses within 3 days of delivery are utilized in the fetal weight estimation. The EFW based on computerized measurements and manual measurements are compared by using regression analysis, artificial neural network and support vector regression. By using different estimation methods, the computerized measurements decrease the EFW errors about 40–70 g. The lowest mean absolute percentage error of EFW decrease from 6.71% for manual measurements to 4.66% for computerized measurements. The proposed fetal ultrasound image segmentation system can provide more accurate EFW in antepartum examination.
Similar content being viewed by others
References
Aysal TC, Barner KE (2007) Rayleigh-maximum-likelihood filtering for speckle reduction of ultrasound images. IEEE Trans Med Imaging 26(5):712–727. doi:10.1109/TMI.2007.895484
Bezdek JC (1980) A convergence theorem for the fuzzy ISODATA clustering algorithm. IEEE Trans Pattern Anal Mach Intell 1(2):1–8
Brinkley JF, McCallum WD, Muramatsu SK, Liu DY (1984) Fetal weight estimation from lengths and volumes found by three-dimensional ultrasound measurements. J Ultrasound Med 3(4):163–168
Campbell S, Wilkin D (1975) Ultrasonic measurement of the fetal abdomen circumference in the estimation of fetal weight. Br J Obstet Gynaecol 82:689–697
Cevenini G, Severi FM, Bocchi C, Petraglia F, Barbini P (2008) An informative probability model enhancing real time echobiometry to improve fetal weight estimation accuracy. Med Biol Eng Comput 46:109–120. doi:10.1007/s11517-007-0299-2
Chang TC, Robson SC, Spencer JA, Gallivan S (1993) Ultrasonic fetal weight estimation: analysis of inter- and intra-observer variability. J Clin Ultrasound 21:515–519. doi:10.1002/jcu.1870210808
Chen SC, Zhang DQ (2004) Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans Syst Man Cybern 34(4):1907–1916. doi:10.1109/TSMCB.2004.831165
Chuang L, Hwang JY, Chang CH, Yu CH, Chang FM (2002) Ultrasound estimation of fetal weight with the use of computerized artificial neural network model. Ultrasound Med Biol 28(8):991–996. doi:10.1016/S0301-5629(02)00554-9
Dudley NJ (2004) A systematic review of the ultrasound estimation of fetal weight. Ultrasound Obstet Gynecol 25(1):80–89. doi:10.1002/uog.1751
Hadlock FP, Harrist RB, Sharman RS, Deter RL, Park SK (1985) Estimation of fetal weight with the use of head, body, and femur measurements—a prospective study. Am J Obstet Gynecol 151:333–337
Jardim SMGVB, Figueiredo MAT (2005) Segmentation of fetal ultrasound images. Ultrasound Med Biol 31(2):243–250. doi:10.1016/j.ultrasmedbio.2004.11.003
Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331. doi:10.1007/BF00133570
Kiryati N, Eldar Y, Bruckstein AM (1991) A probabilistic Hough transform. Pattern Recognit 24(4):303–316. doi:10.1016/0031-3203(91)90073-E
Lu W, Tan J, Floyd R (2005) Automated fetal head detection and measurement in ultrasound images by iterative randomized Hough transform. Ultrasound Med Biol 31(7):929–936. doi:10.1016/j.ultrasmedbio.2005.04.002
Michailovich OV, Tannenbaum A (2006) Despeckling of medical ultrasound images. IEEE Trans Ultrason Ferrolelectr Freq Control 53(1):64–78
Perona P, Malik J (1990) Scale space and edge detection using anisotropic diffusion. IEEE Trans Pattern Mach Intell 12(7):629–639. doi:10.1109/34.56205
Sanders R, James A (1985) The principles and practice of ultrasonography in obstetrics and gynecology. Appleton Century Crofts, Connecticut
Song XF, Han P, Zou L, Chen DZ, Hu SX (2004) A new method for estimation of fetal weight using support vector machine. Chin J Biomed Eng 23(6):516–522
Stetzer BP, Thomas A, Amini SB, Catalano PM (2002) Neonatal anthropometric measurements to predict birth weight by ultrasound. J Perinatol 22(5):397–402. doi:10.1038/sj.jp.7210754
Vapnik VN (1998) Statistical learning theory. Wiley, New York
Wang SR, Sun YN, Chang FM (2008) Artifact removal and texture-based rendering for visualization of 3D fetal ultrasound images. Med Biol Eng Comput 46:575–588. doi:10.1007/s11517-007-0286-7
Warsof SL, Wolf P, Coulehan J, Queenan JT (1986) Comparison of fetal weight estimation formulae with and without head measurements. Obstet Gynecol 67(4):569–573
Xu CY, Prince JL (1998) Snake, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369. doi:10.1109/83.661186
Xu L, Oja E, Kultanen P (1990) A new curve detection method: Randomized Hough transform (RHT). Pattern Recognit Lett 11(5):331–338. doi:10.1016/0167-8655(90)90042-Z
Yu JH, Wang YY, Chen P, Shen YZ (2008) Fetal abdominal contour extraction and measurement in ultrasound images. Ultrasound Med Biol 34(2):169–182. doi:10.1016/j.ultrasmedbio.2007.06.026
Yu YJ, Acton ST (2002) Speckle reducing anisotropic diffusion. IEEE Trans Image Process 11(11):1260–1270. doi:10.1109/TIP.2002.804276
Yuen HK, Illingworth J, Kitter J (1989) Detecting partially occluded ellipse using the Hough transform. Image Vis Comput 7(1):31–37. doi:10.1016/0262-8856(89)90017-6
Acknowledgments
This work was supported by the National Basic Research Program of China (No. 2006CB705707), Natural Science Foundation of China (No. 30570488), Shanghai Leading Academic Discipline Project (No. B112) and Postgraduate Innovation Fund of Fudan University (No. EYH1220001).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yu, J., Wang, Y. & Chen, P. Fetal ultrasound image segmentation system and its use in fetal weight estimation. Med Biol Eng Comput 46, 1227–1237 (2008). https://doi.org/10.1007/s11517-008-0407-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-008-0407-y