In this paper, we propose to use filtering methods and a segmentation algorithm for the analysis of fetal heart in ultrasound images. Since noise speckle makes difficult the analysis of ultrasound images, the filtering process becomes a useful task in these types of applications. The filtering techniques consider in this work assume that the speckle noise is a random variable with a Rayleigh distribution. We use two multiresolution methods: one based on wavelet decomposition and the another based on the Hermite transform. The filtering process is used as way to strengthen the performance of the segmentation tasks. For the wavelet-based approach, a Bayesian estimator at subband level for pixel classification is employed. The Hermite method computes a mask to find those pixels that are corrupted by speckle. On the other hand, we picked out a method based on a deformable model or "snake" to evaluate the influence of the filtering techniques in the segmentation task of left ventricle in fetal echocardiographic images.