In an attempt to improve the visualisation techniques for diagnosis and treatment of musculoskeletal injuries,
we present a novel image fusion method for a pixel-wise fusion of CT and MR images. We focus on the spine
and it's related diseases including osteophyte growth, degenerate disc disease and spinal stenosis. This will have
benefit to the 50-75% of people who suffer from back pain, which is the reason for 1.8% of all hospital stays
in the United States.1 Pre-registered CT and MR image pairs were used. Rigid registration was performed
based on soft tissue correspondence. A pixel-wise image fusion algorithm has been designed to combine CT
and MR images into a single image. This is accomplished by minimizing an energy functional using a Graph
Cut approach. The functional is formulated to balance the similarity between the resultant image and the CT
image as well as between the resultant image and the MR image. Furthermore the variational smoothness of
the resultant image is considered in the energy functional (to enforce natural transitions between pixels). The
results have been validated based on the amount of significant detail preserved in the final fused image. Based
on bone cortex and disc / spinal cord areas, 95% of the relevant MR detail and 85% of the relevant CT detail
was preserved. This work has the potential to aid in patient diagnosis, surgery planning and execution along
with post operative follow up.
This study investigates a novel method of tracking Left Ventricle (LV) curve in Magnetic Resonance (MR)
sequences. The method focuses on energy minimization by level-set curve boundary evolution. The level-set
framework allows introducing knowledge of the field prior on the solution. The segmentation in each particular
time relies not only on the current image but also the segmented image from previous phase. Field prior is defined
based on the experimental fact that the mean logarithm of intensity inside endo and epi-cardium is approximately
constant during a cardiac cycle. The solution is obtained by evolving two curves following the Euler-Lagrange
minimization of a functional containing a field constraint. The functional measures the consistency of the field
prior over a cardiac sequence. Our preliminary results show that the obtained segmentations are very well
correlated with those manually obtained by experts. Furthermore, we observed that the proposed field prior
speeds up curve evolution significantly and reduces the computation load.
In this study, we propose an information theoretic neural network for
normal/abnormal left ventricular motion classification which outperforms significantly
other recent methods in the literature. The proposed framework consists of a supervised 3-layer artificial neural network (ANN) which uses hyperbolic tangent sigmoid and linear transfer functions for hidden and output layers, respectively. The ANN is fed by information theoretic measures of left ventricular
wall motion such as Shannon's differential entropy (SDE), Rényi entropy and Fisher information, which measure global information of subjects distribution. Using 395×20 segmented LV cavities of short-axis magnetic resonance images (MRI) acquired from 48 subjects, the experimental results show that the proposed method outperforms Support Vector Machine (SVM) and thresholding based information theoretic classifiers. It yields a specificity equal to 90%, a sensitivity of 91%, and a remarkable Area Under Curve (AUC) for Receiver Operating Characteristic (ROC), equal to 93.2%.
KEYWORDS: Image segmentation, Medical imaging, Computed tomography, Magnetic resonance imaging, Silver, Image processing algorithms and systems, Detection and tracking algorithms, Spine, Magnetism, Medicine
The level set framework has proven well suited to medical image segmentation1-6 thanks to its ability of balancing
the contribution of image data and prior knowledge in a principled, flexible and transparent way. It consists
of evolving a curve toward the target object boundaries. The curve evolution equation is sought following
the optimization of a cost functional containing two types of terms: data terms, which measure the fidelity of
segmentation to image intensities, and prior terms, which traduce learned prior knowledge. Without priors many
algorithms are likely to fail due to high noise, low contrast and data incompleteness. Different priors have been
investigated such as shape1 and appearance priors.7 In this study, we propose a simple type of priors: the area
prior. This prior embeds knowledge of an approximate object area and has two positive effects. First, It speeds
up significantly the evolution when the curve is far from the target object boundaries. Second, it slows down
the evolution when the curve is close to the target. Consequently, it reinforces curve stability at the desired
boundaries when dealing with low contrast intensity edges. The algorithm is validated with several experiments
using Magnetic Resonance (MR) images and Computed Tomography (CT) images. A comparison with another
level set method illustrates the positive effects of the area prior.
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