Coupling the geodesic active contours model with statistical information based on regions introduces robustness in the segmentation of images with weak or inhomogeneous gradients. In the estimation of the probability density function for each region take part the definition of the features that describe the image inside the different regions and the method of density estimation itself. A Gaussian Mixture Model is frequently proposed for density estimation. This approach is based on the assumption that the intensity distribution of the image is the most discriminant feature in a region. However, the use of second order features provides a better discrimination of the different regions, as these features represent more accurately the local properties of the image manifold.
Due to the high dimensionality of the problem, the use of non parametric density estimation methods becomes necessary. In this article, we present a novel method of introducing the second order information of an image for non parametric estimation of the probability density functions of the different tissues that are present in medical images. The novelty of the method stems on the use of the response of the image under an orthogonal harmonic
operator set projected onto a prototype space for feature generation. The technique described here is applied to the segmentation of brain aneurysms in Computed Tomography Angiography (CTA) and 3D Rotational Angiography (3DRA) showing a qualitative improvement from the Gaussian Mixture Model approach.
The rupture mechanism of intracranial aneurysms is still not fully understood. Although the size of the aneurysm is the shape index most commonly used to predict rupture, some controversy still
exists about its adequateness as an aneurysm rupture predictor. In this work, an automatic method to geometrically characterize the shape of cerebral saccular aneurysms using 3D moment invariants is proposed. Geometric moments are efficiently computed via application of the Divergence Theorem over the aneurysm surface using a non-structured mesh. 3D models of the aneurysm and its connected parent vessels have been reconstructed from segmentations of both 3DRA and CTA images. Two alternative approaches have been used for segmentation, the first one based on isosurface deformable models, and the second one based on the level set method. Several experiments were also conducted to both assess the influence of pre-processing steps in the stability of the aneurysm shape descriptors, and to know the robustness of the proposed method. Moment invariants have proved to be a robust technique while providing a reliable way to discriminate between ruptured and unruptured aneurysms (Sensitivity=0.83, Specificity=0.74) on a data set containing 55 aneurysms. Further investigation over larger databases is necessary to establish their adequateness as reliable predictors of rupture risk.
KEYWORDS: 3D modeling, Angiography, Data modeling, Arteries, Image segmentation, Cerebral aneurysms, Hemodynamics, Visualization, 3D image reconstruction, 3D image processing
Characterization of the blood flow patterns in cerebral aneurysms is important to explore possible correlations between the hemodynamics conditions and the morphology, location, type and risk of rupture of intracranial aneurysms. For this purpose, realistic patient-specific models are constructed from computed tomography angiography and 3D rotational angiography image data. Visualizations of the distribution of hemodynamics forces on the aneurysm walls as well as the intra-aneurysmal flow patterns are presented for a number of cerebral aneurysms of different sizes, types and locations. The numerical models indicate that there are different classes of intra-aneurysmal flow patterns, that may carry different risks of rupture.
Knowledge of brain aneurysm dimensions is essential during the
planning stage of minimally invasive surgical interventions using
Guglielmi Detachable Coils (GDC). These parameters are obtained in
clinical routine using 2D Maximum Intensity Projection images from
Computed Tomographic Angiography (CTA). Automated quantification
of the three dimensional structure of aneurysms directly from the
3D data set may be used to provide accurate and objective
measurements of the clinically relevant parameters. The properties
of Implicit Deformable Models make them suitable to accurately
extract the three dimensional structure of the aneurysm and its
connected vessels. We have devised a two-stage segmentation
algorithm for this purpose. In the first stage, a rough
segmentation is obtained by means of the Fast Marching Method
combining a speed function based on a vessel enhancement filtering
and a freezing algorithm. In the second stage, this rough
segmentation provides the initialization for Geodesic Active
Contours driven by region-based information. The latter problem is
solved using the Level Set algorithm. This work presents a
comparative study between a clinical and a computerized protocol
to derive three geometrical descriptors of aneurysm morphology
that are standard in assessing the viability of surgical treatment
with GDCs. The study was performed on a data base of 40 brain
aneurysms. The manual measurements were made by two
neuroradiologists in two independent sessions. Both inter- and
intra-observer variability and comparison with the automated
method are presented. According to these results, Implicit
Deformable Models are a suitable technique for this application.
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