In this work we present a segmentation framework applied to fetal cardiac images. One of the main problems of the segmentation in ultrasound images is the speckle pattern that makes difficult to model images features such as edges and homogeneous regions. Our approach is based on two main processes. The first one aims at enhancing the ultrasound image using a noise reduction scheme. The Hermite transform is used for this purpose. In the second process a Point Distribution Model (PDM), previously trained, is used for the segmentation of the desired object. The filtering process is then employed before the segmentation stage with the aim of improving the results. The obtained result in the filtering process is used as a way to make more robust the segmentation stage. We evaluate the proposed method in the segmentation of the left ventricle of fetal ultrasound data. Different metrics are used to validate and compare the performance with other methods applied to fetal echocardiographic images.
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.
In this paper, we propose a detection method of low contrast structures in medical ultrasound images. Since noise
speckle makes difficult the analysis of ultrasound images, two approaches based on the wavelet and Hermite-transforms
for enhancement and noise reduction are compared. These techniques assume that speckle pattern is a random signal
characterized by a Rayleigh distribution and affects the image as a multiplicative noise. For the wavelet-based approach,
a Bayesian estimator at subband level for pixel classification is used. All the estimation parameters are calculated using
an adjustment method derived from the first and second order statistical moments. The Hermite method computes a
mask to find those pixels that are corrupted by speckle. In this work, we consider a statistical detection model that
depends on the variable size and contrast of the image speckle. The algorithms have been evaluated using several real
and synthetic ultrasound images. Combinations of the implemented methods can be helpful for automatic detection
applications of tumors in mammographic ultrasound images. The employed filtering techniques are quantitatively and
qualitatively compared with other previously published methods applied on ultrasound medical images.