Osteoarthritis is one of the leading causes of pain and disability worldwide and a major health problem in
developed countries due to the gradually aging population. Though the symptoms are easily recognized and
described by a patient, it is difficult to assess the level of damage or loss of articular cartilage quantitatively. We
present a novel method for fully automated knee cartilage thickness measurement and subsequent assessment
of the knee joint. First, the point correspondence between a pre-segmented training bone model is obtained
with use of Shape Context based non-rigid surface registration. Then, a single Active Shape Model (ASM) is
used to segment both Femur and Tibia bone. The surfaces obtained are processed to extract the Bone-Cartilage
Interface (BCI) points, where the proper segmentation of cartilage begins. For this purpose, the cartilage ASM
is trained with cartilage edge positions expressed in 1D coordinates at the normals in the BCI points. The
whole cartilage model is then constructed from the segmentations obtained in the previous step. An absolute
thickness of the segmented cartilage is measured and compared to the mean of all training datasets, giving as a
result the relative thickness value. The resulting cartilage structure is visualized and related to the segmented
bone. In this way the condition of the cartilage is assessed over the surface. The quality of bone and cartilage
segmentation is validated and the Dice's coefficients 0.92 and 0.86 for Femur and Tibia bones and 0.45 and
0.34 for respective cartilages are obtained. The clinical diagnostic relevance of the obtained thickness mapping
is being evaluated retrospectively. We hope to validate it prospectively for prediction of clinical outcome the
methods require improvements in accuracy and robustness.
Multi modal image registration enables images from different modalities to be analyzed in the same coordinate
system. The class of B-spline-based methods that maximize the Mutual Information between images produce
satisfactory result in general, but are often complex and can converge slowly. The popular Demons algorithm,
while being fast and easy to implement, produces unrealistic deformation fields and is sensitive to illumination
differences between the two images, which makes it unsuitable for multi-modal registration in its original form.
We propose a registration algorithm that combines a B-spline grid with deformations driven by image forces.
The algorithm is easy to implement and is robust against large differences in the appearance between the images
to register. The deformation is driven by attraction-forces between the edges in both images, and a B-spline grid
is used to regularize the sparse deformation field. The grid is updated using an original approach by weighting
the deformation forces for each pixel individually with the edge strengths. This approach makes the algorithm
perform well even if not all corresponding edges are present.
We report preliminary results by applying the proposed algorithm to a set of (multi-modal) test images.
The results show that the proposed method performs well, but is less accurate than state of the art registration
methods based on Mutual Information. In addition, the algorithm is used to register test images to manually
drawn line images in order to demonstrate the algorithm's robustness.
This paper is about the quantitative prediction of the long term outcome of the endovascular coiling treatment
of a patient's cerebral aneurysm. It is generally believed that the local hemodynamic properties of the patient's
cerebral arteries are strongly influencing the origin and growth of aneurysms. We describe our approach: modelling
the flow in a 3D Rotational Angiography (3DRA) reconstruction of the aneurysms including supplying
and draining blood vessels, in combination with simulations and experiments of artificial blood vessel phantom
constructs and measurements. The goal is to obtain insight in the observed phenomena to support the diagnostic
decision process in order to predict the outcome of the intervention with possible simulation of the flow
alternation due to the pertinent intervention.