An automatic segmentation algorithm for the vertebrae in human body CT images is presented. Especially we focused
on constructing and utilizing 4 different statistical intensity-shape combined models for the cervical, upper / lower
thoracic and lumbar vertebrae, respectively. For this purpose, two previously reported methods were combined: a
deformable model-based initial segmentation method and a statistical shape-intensity model-based precise segmentation
method. The former is used as a pre-processing to detect the position and orientation of each vertebra, which determines
the initial condition for the latter precise segmentation method. The precise segmentation method needs prior knowledge
on both the intensities and the shapes of the objects. After PCA analysis of such shape-intensity expressions obtained
from training image sets, vertebrae were parametrically modeled as a linear combination of the principal component
vectors. The segmentation of each target vertebra was performed as fitting of this parametric model to the target image
by maximum a posteriori estimation, combined with the geodesic active contour method. In the experimental result by
using 10 cases, the initial segmentation was successful in 6 cases and only partially failed in 4 cases (2 in the cervical
area and 2 in the lumbo-sacral). In the precise segmentation, the mean error distances were 2.078, 1.416, 0.777, 0.939
mm for cervical, upper and lower thoracic, lumbar spines, respectively. In conclusion, our automatic segmentation
algorithm for the vertebrae in human body CT images showed a fair performance for cervical, thoracic and lumbar
Using an atlas, an image can be segmented by mapping its coordinate space to that of the atlas in an anatomically correct
way. In order to find the correct mapping between the two different coordinate spaces e.g. diffeomorphic demons
registration can be applied. The demons algorithm is a popular choice for deformable image registration and offers the
possibility to perform computationally efficient non-rigid (diffeomorphic) registration. However, this registration method
is prone to image artifacts and image noise. Therefore it has been the main objective of the presented work to combine
the efficiency of diffeomorphic demons and the stability of statistical models. In the presented approach a statistical
deformation model that describes "anatomically correct" displacements vector fields for a specific registration problem is
used to guide the demons registration algorithm. By projecting the current displacement vector field, which is calculated
during any iteration of the registration process, into the model space a regularized version of the vector field can be
computed. Using this regularized vector field for the update of the deformation field in the subsequent iteration of the
registration process the demons registration algorithm can be guided by the deformation model. The proposed method
was evaluated on 21 CT datasets of the right hip. Measuring the average and maximum segmentation error for all 21
datasets and all 120 test configurations it could be demonstrated that the newly proposed algorithm leads to a reduction
of the segmentation error of up to 13% compared to using the conventional diffeomorphic demons algorithm.
Nowadays clinical diagnostic techniques like e.g. dual-energy X-ray absorptiometry are used to quantify bone quality.
However, bone mineral density alone is not sufficient to predict biomechanical properties like the fracture load for an
individual patient. Therefore, the development of tools, which can assess the bone quality in order to predicting
individual biomechanics of a bone, would mean a significant improvement for the prevention of fractures. In this paper
an approach to predict the fracture load of proximal femora by using a statistical appearance model will be presented. For
this purpose, 96 CT-datasets of anatomical specimen of human femora are used to create statistical models for the
prediction of the individual fracture load. Calculating statistical appearance models in different regions of interest by
using principal component analysis (PCA) makes it possible to use geometric as well as structural information about the
By regressing the output of PCA against the individual fracture load of 96 femora multi-linear regression models using a
leave-one-out cross validation scheme have been created. The resulting correlations are comparable to studies that partly
use higher image resolutions.
The surgical treatment of femur fractures, which often result from osteoporosis, is highly dependent on the quality of the femoral bone. Unsatisfying results of surgical interventions like early loosening of implants may be one result of altered bone quality. However, clinical diagnostic techniques to quantify local bone quality are limited and often highly observer dependent. Therefore, the development of tools, which automatically and reproducibly place regions of interest (ROI) and asses the local quality of the femoral bone in these ROIs would be of great help for clinicians.
For this purpose, a method to position and deform ROIs automatically and reproducibly depending on the size and shape of the femur will be presented. Moreover, an approach to asses the femur quality, which is based on calculating texture features using co-occurrence matrices and these adaptive regions, will be proposed.
For testing purposes, 15 CT-datasets of anatomical specimen of human femora are used. The correlation between the texture features and biomechanical properties of the proximal femoral bone is calculated. First results are very promising and show high correlation between the calculated features and biomechanical properties. Testing the method on a larger data pool and refining the algorithms to further increase its sensitivity for altered bone quality will be the next steps in this project.