Unmanned Aerial Vehicles (UAVs) have shown great capability for disaster management due to their fast speed, automated deployment and low maintenance requirements. In recent years, disasters such as flooding are having increasingly damaging societal and environmental effects. To reduce their impact, real-time and reliable flood monitoring and prevention strategies are required. However, the limited battery life of small lightweight UAVs imposes efficient strategies to subsample the sensing field in this context. This paper proposes a novel solution to maximise the number of inspected flooded surface while keeping the travelled distance bounded. Our proposal solves the so-called continuous Travelling Salesman Problem (TSP), where the costs of travelling from one location to another depend not only on the distance, but also on the presence of water. To determine the optimal path between checkpoints, we employ the fast sweeping algorithm using a cost function defined from hyperspectral satellite maps identifying flooded regions. Preliminary results using MODIS flood maps show that our UAV planning strategy achieves a covered flooded surface approximately 3.33 times greater for the same travelled distance when compared to the conventional TSP solution. These results show new insights on the use of hyperspectral imagery acquired from UAVs to monitor water resources.
Changes in tissue stiffness correlate with pathological phenomena that can aid the diagnosis of several diseases
such as breast and prostate cancer. Ultrasound elastography measures the elastic properties of soft tissues using
The standard way to estimate the displacement field from which researchers obtain the strain in elastography
is the time-domain cross-correlation estimator (TDE). Optical flow (OF) methods have been also characterized
and their use keeps increasing.
We introduce in this paper the use of a Modified Demons Algorithm (MDA) to estimate the displacement
field and we compare it with OF. A least-squares strain estimator (LSE) is applied to estimate the strain from the
displacement. The input for the algorithm comes from the ultrasound scanner standard video output; therefore,
its clinical implementation is immediate.
To test the algorithm, a tissuemimicking 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 COMSOL Multiphysics 3.2 software. Synthetic pre- and post-compression (1.25%) B-mode images
were computer generated using FIELD II ultrasound simulator. Afterward, the algorithm was tested with a
commercial CIRS breast elastography phantom, applying a 2% freehand compression.
Axial displacement fields and strain figures are presented and in the case of the synthetic one compared
to the ground truth given by the FE software. Although other researchers have used registration methods for
elastography, as far as we know, they have not been used as stand alone but together with elastic modulus
reconstruction or FE which iteratively varies material properties to improve registration.
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.