Anisotropic filtering for model-based segmentation of 4D cylindrical echocardiographic images

Abstract

This paper presents a 4D (3D þ time) echocardiographic image anisotropic filtering and a 3D model-based segmentation system. To improve the extraction of left ventricle boundaries, we rely on two preprocessing stages. First, we apply an anisotropic filter that reduces image noise. This 4D filter takes into account the spatial and temporal nature of echocardiographic images. Second, we adapt the usual gradient filter estimation to the cylindrical geometry of the 3D ultrasound images. The reconstruction of the endocardium takes place by deforming a deformable simplex mesh having an a priori knowledge of left ventricle shape and that is guided by a region-based data attraction force. The external force formulation improves the segmentation robustness against noise and outliers. We illustrate our method by showing experimental results on very challenging sparse and noisy ultrasound images of the heart and by computing quantitative measurements of the left ventricle volume.

Fast block flow tracking of atrial septal defects in 4D echocardiography

“…An interesting 3D cross-correlation-based approach for speckle tracking on simulated data is proposed in (Chen et al, 2005). Some good examples of temporal segmentation of cardiac US can be found in (Montagnat et al, 2003;Morsy and von Ramm, 1999;Papademetris et al, 2003). Model-based segmentation employing simplex meshes (Montagnat et al, 2003) or finite element models (FEM) (Papademetris et al, 2003) have shown promising results, especially for the left ventricle.…”

LV Motion Tracking from 3D Echocardiography Using Textural and Structural Information

“…For the external energy, we use a negative gradient norm of 3D ultrasound images preprocessed by 3D anisotropic diffusion [13]. This way we remove the texture information, preserve the contrast, and emphasize the boundaries [4]. To reduce the computational complexity, we discretize the initial contour C t and precompute the B-splines basis function values in the matrix D. We minimize the energy function:…”

“…Despite certain advances in technology, 3D ultrasound images are still of low quality with many artifacts, such as attenuation, speckle, shadows, and signal dropout. A relatively small amount of research has been done in motion analysis from 3D echocardiography [3,4,5]. Montagnat and colleagues [4] develop a 3D model-based ultrasound segmentation; the method filters the image by (4D) anisotropic diffusion and fits the LV model to the high intensity gradients.…”

“…A relatively small amount of research has been done in motion analysis from 3D echocardiography [3,4,5]. Montagnat and colleagues [4] develop a 3D model-based ultrasound segmentation; the method filters the image by (4D) anisotropic diffusion and fits the LV model to the high intensity gradients. Sanchez-Ortiz and collaborators [5] use multi-scale fuzzy-clustering method together with phase-based acoustic feature detection to fit the LV model.…”

Progress towards an electromechanical model of the heart for cardiac image analysis

“…Different type of forces may be applied depending on the image modality. We chose to combine intensity and gradient information with a regionbased approach [7] applied to the intensity profile extracted at each vertex in its normal direction. It consists in defining a region with a range of intensity values and then finding its boundary by looking at the voxels of high gradient value.…”