Elastography, an ultrasound modality based on the relation between tissue strain and its mechanical properties, has a strong potential in the diagnosis and prognosis of tumors. For instance, tissue affected by breast and prostate cancer undergoes a change in its elastic properties. These changes can be measured using ultrasound signals. The standard way to visualize the elastic properties of tissues in elastography is the representation of the axial strain. Other approaches investigate the information contained in shear strain elastograms, vorticity or the representation of the full strain tensor. In this paper, we propose to represent the elastic behaviour of tissues through the visualization of the Strain Index, related with the trace of the strain tensor. Based on the mathematical interpretation of the strain tensor, this novel parameter is equivalent to the sum of the eigenvalues of the strain tensor, and constitutes a measure of the total amount of strain of the soft tissue. In order to show the potential of this visualization approach, a tissue-mimicking phantom was modeled as a 10x10x5 cm region containing a centered 10mm cylindrical inclusion three times stiffer than the surrounding material, and its elastic behavior was simulated using finite elements software. Synthetic pre- and post-compression (1.25%) B-mode images were computer-generated with ultrasound simulator. Results show that the visualization of the tensor trace significantly improves the representation and detection of inclusions, and can help add insight in the detection of different types of tumors.
This paper presents a novel level set method for contour detection in multiple-object scenarios applied to the segmentation of teeth images. Teeth segmentation from 2D images of dental plaster cast models is a difficult problem because it is necessary to independently segment several objects that have very badly defined borders between them. Current methods for contour detection which only employ image information cannot successfully segment such structures. Being therefore necessary to use prior knowledge about the problem domain, current approaches in the literature are limited to the extraction of shape information of individual objects, whereas the key factor in such a problem are the relative positions of the different objects composing the anatomical structure. Therefore, we propose a novel method for introducing such information into a level set framework. This results in a new energy term which can be explained as a regional term that takes into account the relative positions of the different objects, and consequently creates an attraction or repulsion force that favors a
determined configuration. The proposed method is compared with balloon and GVF snakes, as well as with the Geodesic Active Regions model, showing accurate results.