Fetal abdominal contour extraction and measurement in ultrasound images

A novel method is developed for the fetal abdominal contour extraction and measurement in ultrasound images. Fetal abdominal circumference (AC) is one of the standardized measurements in the antepartum ultrasound monitoring. Among several standardized measurements, AC is best correlated with fetal growth but is also the most difficult to be accurately measured. To overcome the difficulties in the abdominal contour extraction, the proposed method is a four-step procedure that integrates several image segmentation techniques. The proposed method is able to make the best use of the strength of different segmentation algorithms, while avoiding their deficiencies. An enhanced instantaneous coefficient of variation (ICOV) edge detector is first developed to detect edges of the abdominal contour and alleviate the effects of most speckle noise. Then, the Fuzzy C-Means clustering is employed to distinguish salient edges attributable to the abdominal contour from weak edges due to the other texture. Subsequently, the iterative Hough transform is applied to determine an elliptical contour and obtain an initial estimation of the AC. Finally, the gradient vector field (GVF) snake adapts the initial ellipse to the real edges of the abdominal contour. Quantitative validation of the proposed method on synthetic images under different imaging conditions achieves satisfactory segmentation accuracy (98.78%+/-0.16%). Experiments on 150 clinical images are carried out in three aspects: comparisons between inter-observer and inter-run variation, the fitness analysis between the automatically detected ellipse and the manual delineation, and the accuracy comparisons between automatic measurements and manual measurements in estimation of fetal weight (EFW). Experimental results show that the proposed method can provide consistent and accurate measurements. The reductions of the mean absolute difference and the standard deviation of EFW based on automatic measurements are about 1.2% and 2.1%, respectively, which indicate its potential in clinical antepartum monitoring application.