Detection and measurement of fetal abdominal contour in ultrasound images via local phase information and iterative randomized Hough transform.

Due to the characteristic artifacts of ultrasound images, e.g., speckle noise, shadows and intensity inhomogeneity, traditional intensity-based methods usually have limited success on the segmentation of fetal abdominal contour. This paper presents a novel approach to detect and measure the abdominal contour from fetal ultrasound images in two steps. First, a local phase-based measure called multiscale feature asymmetry (MSFA) is de ned from the monogenic signal to detect the boundaries of fetal abdomen. The MSFA measure is intensity invariant and provides an absolute measurement for the signi cance of features in the image. Second, in order to detect the ellipse that ts to the abdominal contour, the iterative randomized Hough transform is employed to exclude the interferences of the inner boundaries, after which the detected ellipse gradually converges to the outer boundaries of the abdomen. Experimental results in clinical ultrasound images demonstrate the high agreement between our approach and manual approach on the measurement of abdominal circumference (mean sign difference is 0.42% and correlation coef cient is 0.9973), which indicates that the proposed approach can be used as a reliable and accurate tool for obstetrical care and diagnosis.

[1]  P. Doubilet,et al.  Atlas of Ultrasound in Obstetrics and Gynecology: A Multimedia Reference , 2011 .

[2]  Ahror Belaid,et al.  Phase based level set segmentation of ultrasound images , 2009, 2009 9th International Conference on Information Technology and Applications in Biomedicine.

[3]  Gustavo Carneiro,et al.  Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree , 2008, IEEE Transactions on Medical Imaging.

[4]  Jinhua Yu,et al.  Fetal abdominal contour extraction and measurement in ultrasound images. , 2008, Ultrasound in medicine & biology.

[5]  J. Alison Noble,et al.  Ultrasound image segmentation: a survey , 2006, IEEE Transactions on Medical Imaging.

[6]  Jinglu Tan,et al.  Automated fetal head detection and measurement in ultrasound images by iterative randomized Hough transform. , 2005, Ultrasound in medicine & biology.

[7]  Mário A. T. Figueiredo,et al.  Segmentation of fetal ultrasound images. , 2005, Ultrasound in medicine & biology.

[8]  Michael Brady,et al.  On the Choice of Band-Pass Quadrature Filters , 2004, Journal of Mathematical Imaging and Vision.

[9]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[10]  Jinglu Tan,et al.  Hough transforms for shape identification and applications in medical image processing , 2003 .

[11]  M. Kupferminc,et al.  Prediction of fetal weight by ultrasound: the contribution of additional examiners , 2002, Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology.

[12]  Michael Felsberg,et al.  The monogenic signal , 2001, IEEE Trans. Signal Process..

[13]  Michael Felsberg,et al.  A New Extension of Linear Signal Processing for Estimating Local Properties and Detecting Features , 2000, DAGM-Symposium.

[14]  J. Alison Noble,et al.  2D+T Acoustic Boundary Detection in Echocardiography , 1998, MICCAI.

[15]  Robert A. McLaughlin,et al.  Randomized Hough Transform: Improved ellipse detection with comparison , 1998, Pattern Recognit. Lett..

[16]  S D Pathak,et al.  Interactive automatic fetal head measurements from ultrasound images using multimedia computer technology. , 1997, Ultrasound in medicine & biology.

[17]  Peter Kovesi,et al.  Symmetry and Asymmetry from Local Phase , 1997 .

[18]  George K. Matsopoulos,et al.  Use of morphological image processing techniques for the measurement of a fetal head from ultrasound images , 1994, Pattern Recognit..

[19]  R A Peters,et al.  Automatic segmentation of ultrasound images using morphological operators. , 1991, IEEE transactions on medical imaging.

[20]  Erkki Oja,et al.  A new curve detection method: Randomized Hough transform (RHT) , 1990, Pattern Recognit. Lett..

[21]  Josef Kittler,et al.  Detecting partially occluded ellipses using the Hough transform , 1989, Image Vis. Comput..

[22]  Robyn A. Owens,et al.  Feature detection from local energy , 1987, Pattern Recognit. Lett..

[23]  F. P. Hadlock,et al.  Estimating fetal age: computer-assisted analysis of multiple fetal growth parameters. , 1984, Radiology.