为了对胎儿进行准确的头周测量和脑畸形诊断，需要自动准确地检测胎儿颅骨椭圆，为此提出了一种超声图像胎儿颅骨椭圆的自动检测方法。该方法首先使用K-均值算法将像素分为3类；然后取出亮物体，仅保留其中较大的连通分量；最后抽取骨架后再使用随机Hough变换(RHT)进行椭圆检测。该方法采用的预处理过程，使RHT需要考察的像素数极大地减少，从而提高了检测速度；另外，该方法还提出了一种新的RHT得分机制，由于该机制综合考虑了颅骨曲线在图像空间和参数空间的表现，从而提高了检测精度。

For automatic fetal head measurements and brain abnormalities diagnoses，the detection of fetal skull automatically is needed.So，a method for fetal head skull ellipse detection in ultrasound image is proposed.The main process is as follow: firstly the K-mean algorithm is used to cluster pixels into 3 classes and the bright objects are identified； then only the bigger connected components in the bright objects are preserved and skeletonized； finally randomized Hough transform (RHT) is applied to skeletonized components for ellipse detection.The adopted preprocessing method greatly reduces the number of pixels to be examined by RHT and improves the detection speed.Furthermore，a new scoring mechanism for RHT is introduced，which takes into account the head curves performance in both image and parameter spaces and increases detection accuracy.