One of the common methods for monitoring fetal growth is measuring its head circumference in ultrasound images taken from the mother's womb. In recent years utilizing deep learning methods have been expanded in this application thanks to its potential in promoting the accuracy of estimating head circumference. However, the performance of deep neural networks is highly dependent on the volume of training data. On the other hand, the region of the fetal head is segmented with considerable errors, due to the presence of various types of noise. In this article, a new method is presented to improve fetal head circumference estimation in ultrasound images in which by using unsupervised data augmentation an attempt is made to increase the amount of training data of the deep network. Parallelly by utilizing an elliptical contour estimation method, an optimal contour is created to decrease the segmentation errors . Comparing the performance of the proposed scheme with the basic method as well as state-of-art schemes shows the improvement of fetal head circumference estimation with the help of the proposed algorithm in such way that not only the quality of fetal head circumference measurement with the Dice parameter has been improved by 0.6% and 3.24% respectively compared to the closest alternative and the basic method, but also the variance of the obtained results in both types of these comparisons have improved dramatically. These achievements demonstrate the performance of the proposed method is also more focused and reliable in addition to being more accurate.