Faculty Bibliography

This paper presents a new technique for the automatic model-based segmentation of three-dimensional (3-D) objects from volumetric image data. The development closely follows the seminal work of Taylor and Cootes on active shape models, but is based on a hierarchical parametric object description rather than a point distribution model. The segmentation system includes both the building of statistical models and the automatic segmentation of new image data sets via a restricted elastic deformation of shape models. Geometric models are derived from a sample set of image data which have been segmented by experts. The surfaces of these binary objects are converted into parametric surface representations, which are normalized to get an invariant object-centered coordinate system. Surface representations are expanded into series of spherical harmonics which provide parametric descriptions of object shapes. It is shown that invariant object surface parametrization provides a good approximation to automatically determine object homology in terms of sets of corresponding sets of surface points. Gray-level information near object boundaries is represented by 1-D intensity profiles normal to the surface. Considering automatic segmentation of brain structures as our driving application, our choice of coordinates for object alignment was the well-accepted stereotactic coordinate system. Major variation of object shapes around the mean shape, also referred to as shape eigenmodes, are calculated in shape parameter space rather than the feature space of point coordinates. Segmentation makes use of the object shape statistics by restricting possible elastic deformations into the range of the training shapes. The mean shapes are initialized in a new data set by specifying the landmarks of the stereotactic coordinate system. The model elastically deforms, driven by the displacement forces across the object's surface, which are generated by matching local intensity profiles. Elastic deformations are limited by setting bounds for the maximum variations in eigenmode space. The technique has been applied to automatically segment left and right hippocampus, thalamus, putamen, and globus pallidus from volumetric magnetic resonance scans taken from schizophrenia studies. The results have been validated by comparison of automatic segmentation with the results obtained by interactive expert segmentation.

This paper describes largely automated methods of creating connected, 3D vascular trees from individual vessels segmented from magnetic resonance angiograms. Vessel segmentation is initiated by user-supplied seed points, with automatic calculation of vessel skeletons as image intensity ridges and automatic estimation of vessel widths via medialness calculations. The tree-creation process employs a variant of the minimum spanning tree algorithm and evaluates image intensities at each proposed connection point. We evaluate the accuracy of nodal connections by registering a 3D vascular tree with 4 digital subtraction angiograms (DSAs) obtained from the same patient, and by asking two neuroradiologists to evaluate each nodal connection on each DSA view. No connection was judged incorrect. The approach permits new, clinically useful visualizations of the intracerebral vasculature.

Inspired by the discussion in neurological research about the callosal fiber connections with respect to brain asymmetry we developed a technique that measures distances between brain hemispheres in a non-Euclidean, curvilinear space. The technique is a generic morphometric tool for measuring minimal distances within and across 3-D structures. We applied the technique for distances from the cortical gray/white matter boundary to the cross-section of the corpus callosum. The method uses a 3-D extension of the F*-algorithm. The algorithm uses a cost matrix determined by the image data. The resulting distances are mapped to the cortical surface and differences on the two hemispheres can be visually compared. Distances were also projected back to the corpus callosum to represent asymmetry by comparing left and right measurements. We can present results obtained by processing 11 3-D magnetic resonance data sets representing a normal control group.

This paper describes a method for the enhancement of curvilinear structures such as vessels and bronchi in three-dimensional (3-D) medical images. A 3-D line enhancement filter is developed with the aim of discriminating line structures from other structures and recovering line structures of various widths. The 3-D line filter is based on a combination of the eigenvalues of the 3-D Hessian matrix. Multi-scale integration is formulated by taking the maximum among single-scale filter responses, and its characteristics are examined to derive criteria for the selection of parameters in the formulation. The resultant multi-scale line-filtered images provide significantly improved segmentation and visualization of curvilinear structures. The usefulness of the method is demonstrated by the segmentation and visualization of brain vessels from magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA), bronchi from a chest CT, and liver vessels (portal veins) from an abdominal CT.

This paper presents a new methodology for the automatic segmentation and characterization of object changes in time series of three-dimensional data sets. The purpose of the analysis is a detection and characterization of objects based on their dynamic changes. The technique was inspired by procedures developed for the analysis of functional MRI data sets. After precise registration of serial volume data sets to 4-D data, we applied a new time series analysis taking into account the characteristic time function of variable lesions. The images were preprocessed with a correction of image field inhomogeneities and a normalization of the brightness function over the whole time series. This leads to the hypothesis that static regions remain unchanged over time, whereas local changes in tissue characteristics cause typical functions in the voxel's time series. A set of features are derived from the time series and their derivatives, expressing probabilities for membership to the sought structures. These multiple sources of uncertain evidence were combined to a single evidence value using Dempster Shafer's theory. Individual processing of a series of 3-D data sets is therefore replaced by a fully 4-D processing. To explore the sensitivity of time information, active lesions are segmented solely based on time fluctuation, neglecting absolute intensity information. The project is driven by the objective of improving the segmentation and characterization of white matter lesions in serial MR data of multiple sclerosis patients. Pharmaceutical research and patient follow-up requires efficient and robust methods with high degree of automation. Further, an enhanced set of morphometric parameters might give a better insight into the course of the disease and therefore leads to a better understanding of the disease mechanism and of drug effects. The new method has been applied to two time series from different patient studies, covering time resolutions of 12 and 24 data sets over a period of roughly one year. The results demonstrate that time evolution is a highly sensitive feature to detect fluctuating structures.

For the measure of brain activation in functional MRI many methods compute a heuristically chosen metric. The statistic of the underlying metric which is implicitly derived from the original assumption about the noise in the data, provides only an indirect way to the statistical inference of brain activation. An alternative procedure is proposed by presenting a binary hypothesis-testing approach. This approach treats the problem of detecting brain activation by directly deriving a test statistic based on the probabilistic model of the noise in the data. Thereby, deterministic and parameterized models for the hemodynamic response can be considered. Results show that time series models can be detected even if they are characterized by unknown parameters, associated with the unclear nature of the mechanisms that mediate between neuronal stimulation and hemodynamic brain response. The likelihood ratio tests proposed in this paper are very efficient and robust in making a statistical inference about detected regions of brain activation. To validate the applicability of the approach a simulation environment for functional MRI is used. This environment also serves as a testbed for comparative study and systematic tests.