2,278 research outputs found
Fast Approximate Time-Delay Estimation in Ultrasound Elastography Using Principal Component Analysis
Time delay estimation (TDE) is a critical and challenging step in all
ultrasound elastography methods. A growing number of TDE techniques require an
approximate but robust and fast method to initialize solving for TDE. Herein,
we present a fast method for calculating an approximate TDE between two radio
frequency (RF) frames of ultrasound. Although this approximate TDE can be
useful for several algorithms, we focus on GLobal Ultrasound Elastography
(GLUE), which currently relies on Dynamic Programming (DP) to provide this
approximate TDE. We exploit Principal Component Analysis (PCA) to find the
general modes of deformation in quasi-static elastography, and therefore call
our method PCA-GLUE. PCA-GLUE is a data-driven approach that learns a set of
TDE principal components from a training database in real experiments. In the
test phase, TDE is approximated as a weighted sum of these principal
components. Our algorithm robustly estimates the weights from sparse feature
matches, then passes the resulting displacement field to GLUE as initial
estimates to perform a more accurate displacement estimation. PCA-GLUE is more
than ten times faster than DP in estimation of the initial displacement field
and yields similar results.Comment: Accepted to be Published in 2019, 41th Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),
Berlin, German
A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots
In the gastrointestinal (GI) tract endoscopy field, ingestible wireless
capsule endoscopy is considered as a minimally invasive novel diagnostic
technology to inspect the entire GI tract and to diagnose various diseases and
pathologies. Since the development of this technology, medical device companies
and many groups have made significant progress to turn such passive capsule
endoscopes into robotic active capsule endoscopes to achieve almost all
functions of current active flexible endoscopes. However, the use of robotic
capsule endoscopy still has some challenges. One such challenge is the precise
localization of such active devices in 3D world, which is essential for a
precise three-dimensional (3D) mapping of the inner organ. A reliable 3D map of
the explored inner organ could assist the doctors to make more intuitive and
correct diagnosis. In this paper, we propose to our knowledge for the first
time in literature a visual simultaneous localization and mapping (SLAM) method
specifically developed for endoscopic capsule robots. The proposed RGB-Depth
SLAM method is capable of capturing comprehensive dense globally consistent
surfel-based maps of the inner organs explored by an endoscopic capsule robot
in real time. This is achieved by using dense frame-to-model camera tracking
and windowed surfelbased fusion coupled with frequent model refinement through
non-rigid surface deformations
Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography
Ultrasound elastography estimates the mechanical properties of the tissue
from two Radio-Frequency (RF) frames collected before and after tissue
deformation due to an external or internal force. This work focuses on strain
imaging in quasi-static elastography, where the tissue undergoes slow
deformations and strain images are estimated as a surrogate for elasticity
modulus. The quality of the strain image depends heavily on the underlying
deformation, and even the best strain estimation algorithms cannot estimate a
good strain image if the underlying deformation is not suitable. Herein, we
introduce a new method for tracking the RF frames and selecting automatically
the best possible pair. We achieve this by decomposing the axial displacement
image into a linear combination of principal components (which are calculated
offline) multiplied by their corresponding weights. We then use the calculated
weights as the input feature vector to a multi-layer perceptron (MLP)
classifier. The output is a binary decision, either 1 which refers to good
frames, or 0 which refers to bad frames. Our MLP model is trained on in-vivo
dataset and tested on different datasets of both in-vivo and phantom data.
Results show that by using our technique, we would be able to achieve higher
quality strain images compared to the traditional methods of picking up pairs
that are 1, 2 or 3 frames apart. The training phase of our algorithm is
computationally expensive and takes few hours, but it is only done once. The
testing phase chooses the optimal pair of frames in only 1.9 ms
Two-Dimensional Gel Electrophoresis Image Registration Using Block-Matching Techniques and Deformation Models
[Abstract] Block-matching techniques have been widely used in the task of estimating displacement in medical images, and they represent the best approach in scenes with deformable structures such as tissues, fluids, and gels. In this article, a new iterative block-matching technique—based on successive deformation, search, fitting, filtering, and interpolation stages—is proposed to measure elastic displacements in two-dimensional polyacrylamide gel electrophoresis (2D–PAGE) images. The proposed technique uses different deformation models in the task of correlating proteins in real 2D electrophoresis gel images, obtaining an accuracy of 96.6% and improving the results obtained with other techniques. This technique represents a general solution, being easy to adapt to different 2D deformable cases and providing an experimental reference for block-matching algorithms.Galicia. Consellería de Economía e Industria; 10MDS014CTGalicia. Consellería de Economía e Industria; 10SIN105004PRInstituto de Salud Carlos III; PI13/0028
Using CamiTK for rapid prototyping of interactive Computer Assisted Medical Intervention applications
Computer Assisted Medical Intervention (CAMI hereafter) is a complex
multi-disciplinary field. CAMI research requires the collaboration of experts
in several fields as diverse as medicine, computer science, mathematics,
instrumentation, signal processing, mechanics, modeling, automatics, optics,
etc
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