Segmentation of fetal organs such as head, femur, and humérus on ultrasound image is one of the challenges in realization of automated system for fetal biometry measurements. Although many methods have been developed to overcome this problem, most of them are generally specific to one organ of the body alone. The research in this paper will focus on a machine learning method that has been available before: multilayer super pixel classification using random forest. The focus of this study is to improve the accuracy by exploring compactness parameter in the formation of super-pixels. In addition, we also add moment image features such as translation, rotation, and scale invariant to improve the segmentation performance. The experimental results showed that the difference in compactness parameters will provide different result for the accuracy, Fl-score, recall, and specificity. The addition of moment features can also improve the performance of image segmentation of fetal organs even though increase was not significant. Fetal head segmentation using proposed method has higher Fl-score and specificity, but lower accuracy and recall compared to previous methods. Whereas fetal femur and humérus segmentation using proposed method has higher accuracy, Fl-score, recall and specificity compared to previous method.