Biomedical subjects

G Gerig

Publications and source records attributed to G Gerig.

14 recordsLinked to original sources

PubMed2003

Statistical shape analysis of neuroanatomical structures based on medial models.

Knowledge about the biological variability of anatomical objects is essential for statistical shape analysis and discrimination between healthy and pathological structures. This paper describes a novel approach that incorporates the variability of an object population into the generation of a characteristic 3D shape model. The proposed shape representation is a coarse-scale sampled medial description derived from a fine-scale spherical harmonics (SPHARM) boundary description. This medial description is composed of a net of medial samples (m-rep) with fixed graph properties. The medial model is computed automatically from a predefined shape space using pruned 3D Voronoi skeletons. A new method determines the stable medial branching topology from the shape space. An intrinsic coordinate system and an implicit correspondence between shapes is defined on the medial manifold. Several studies of biological structures clearly demonstrate that the novel representation has the promise to describe shape changes in a natural and intuitive way. A new medial shape similarity study of group differences between monozygotic and dizygotic twins in lateral ventricle shape demonstrates the meaningful and powerful representation of local and global form.

Algorithms↗

PubMed2001

Infant cerebral ventricle volume: a comparison of 3-D ultrasound and magnetic resonance imaging.

Enlargement of the cerebral lateral ventricles is observed in several neuropsychiatric disorders with origins in early brain development. Lateral ventricle size is also predictive of poor neurodevelopmental outcome in premature infants. Three-dimensional (3-D) ultrasound (US) offers an improved methodology for the study of lateral ventricle volume in neonates and infants. To assess the validity of ventricle volume measures obtained with 3-D US, we compared the volumes obtained by 3-D US with magnetic resonance imaging (MRI) in seven infants. Ventricle volumes were determined using a computer-assisted image analysis program, IRIS. There was excellent correlation between ventricle volumes obtained with 3-D US and those obtained with MRI (intraclass correlation coefficient 0.92, F = 23.28, p = 0.00027), indicating that 3-D US provides valid measures of overall lateral ventricle volume compared to the "gold standard" of MRI. 3-D US can provide an economical and practical means of studying lateral ventricle volume in neonates, a neurostructural marker of abnormal brain development.

Cerebral Ventricles↗

PubMed2001

Computer-assisted visualization of arteriovenous malformations on the home personal computer.

OBJECTIVE: Arteriovenous malformations (AVMs) are difficult lesions to treat, partly because it is difficult to formulate a three-dimensional mental image of the nidus and its supplying arteries, draining veins, and arteries of passage. Our purpose is to develop personal computer software that allows better visualization of complex, three-dimensional, connected vascular anatomy for surgical planning. METHODS: Vessels are defined from magnetic resonance angiograms and are symbolically linked to form vascular trees. The nidus of the AVM is also defined by magnetic resonance angiography. These representations of the nidus and vasculature can be viewed together in a software program that allows the user to color-code groups of vessels or to selectively turn connected groups of vessels "off" to avoid obscuring the part of the image that the user wants to observe. Structures can be viewed from any angle. The vessels can also be shown intersecting any magnetic resonance angiogram slice or superimposed upon digital subtraction angiograms obtained from the same patient. RESULTS: We report results from two patients with AVMs in which our representations were compared with the findings during surgery. Our three-dimensional vascular trees correctly depicted the relationship of the nidus to feeding vessels in three dimensions. We show findings in an additional, unoperated patient for whom vessel trees were created from three-dimensional digital subtraction angiography data and compared with a volume rendering of the original data set. CONCLUSION: Computer-assisted, three-dimensional visualizations of complex vascular anatomy can be helpful in planning the surgical excision of AVMs. Software programs that produce these images can provide important information that is difficult to obtain by traditional techniques. This imaging method is also applicable to guidance of endovascular procedures and removal of complex tumors.

Diagnosis, Computer-Assisted↗

PubMed2000

Exploring the discrimination power of the time domain for segmentation and characterization of active lesions in serial MR data.

This paper presents a new method for the automatic segmentation and characterization of object changes in time series of three-dimensional data sets. The technique was inspired by procedures developed for analysis of functional MRI data sets. After precise registration of serial volume data sets to 4-D data, we applied a 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 over the whole time series. Thus, static regions remain unchanged over time, whereas changes in tissue characteristics produce typical intensity variations in the voxel's time series. A set of features was derived from the time series, 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. The project was 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 a high degree of automation. The new approach replaces conventional segmentation of series of 3-D data sets by a 1-D processing of the temporal change at each voxel in the 4-D image data set. The new method has been applied to a total of 11 time series from different patient studies, covering time resolutions of 12 and 24 data sets over a period of about 1 year. The results demonstrate that time evolution is a highly sensitive feature for detection of fluctuating structures.

Brain↗

PubMed2000

Parametric estimate of intensity inhomogeneities applied to MRI.

This paper presents a new approach to the correction of intensity inhomogeneities in magnetic resonance imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. The new correction method called parametric bias field correction (PABIC) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity field. We assume that the image is composed of pixels assigned to a small number of categories with a priori known statistics. Further we assume that the image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a nonlinear energy minimization problem using an evolution strategy (ES). The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based on homomorphic filtering. Furthermore, PABIC can correct bias distortions much larger than the image contrast. Input parameters are the intensity statistics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with histogram adjustment, making it well suited for normalizing the intensity histogram of datasets from serial studies. We present simulations and a quantitative validation with phantom and test images. A large number of MR image data acquired with breast, surface, and head coils, both in two dimensions and three dimensions, have been processed and demonstrate the versatility and robustness of this new bias correction scheme.

Brain↗

PubMed1999

Elastic model-based segmentation of 3-D neuroradiological data sets.

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.

Algorithms↗

PubMed1998

Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images.

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.

Bronchography↗

PubMed1996

Segmentation of 2-D and 3-D objects from MRI volume data using constrained elastic deformations of flexible Fourier contour and surface models.

This paper describes a new model-based segmentation technique combining desirable properties of physical models (snakes), shape representation by Fourier parametrization, and modelling of natural shape variability. Flexible parametric shape models are represented by a parameter vector describing the mean contour and by a set of eigenmodes of the parameters characterizing the shape variation. Usually the segmentation process is divided into an initial placement of the mean model and an elastic deformation restricted to the model variability. This, however leads to a separation of biological variation due to a global similarity transform from small-scale shape changes originating from elastic deformations of the normalized model contours only. The performance can be considerably improved by building shape models normalized with respect to a small set of stable landmarks (AC-PC in our application) and by explaining the remaining variability among a series of images with the model flexibility. This way the image interpretation is solved by a new coarse-to-fine segmentation procedure based on the set of deformation eigenmodes, making a separate initialization step unnecessary. Although straightforward, the extension to 3-D is severely impeded by difficulties arising during the generation of a proper surface parametrization for arbitrary objects with spherical topology. We apply a newly developed surface parametrization which achieves a uniform mapping between object surface and parameter space. The 3-D procedure is demonstrated by segmenting deep structures of the human brain from MR volume data.

Anatomy, Cross-Sectional↗

PubMed1994

Temporal lobe sulco-gyral pattern anomalies in schizophrenia: an in vivo MR three-dimensional surface rendering study.

Neuroanatomical and histological findings from post-mortem brains, as well as in vivo findings from magnetic resonance imaging (MRI) studies, suggest the presence of morphologic temporal lobe abnormalities in schizophrenia. To determine whether or not sulco-gyral pattern abnormalities in the temporal lobe could be detected in vivo, we applied computerized surface rendering techniques to MR data sets in order to make both qualitative and quantitative analyses of three-dimensional reconstructions of the temporal and frontal cortex in 15 schizophrenic patients and 15 normal controls. The qualitative analysis, based on a visual classification of the temporal lobe sulco-gyral pattern by 4 raters blind to diagnosis, showed that in schizophrenics there was a more vertical orientation to the sulci in the left temporal lobe, with an interrupted course of sulci due to gyri coursing across the sulci. Normal controls, in contrast, showed a more horizontal orientation with no interruptions. These findings were supported by the quantitative analysis, where more sulcal lines, representing an interrupted course of sulci, were observed in the temporal lobes (more pronounced on the left) in schizophrenics than in normal controls. These data suggest that some of the abnormalities observed in schizophrenia may have their origin in alterations occurring during the course of neurodevelopment when the sulco-gyral pattern is determined.

Adult↗

PubMedDate not supplied

Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging.

A computerized system for processing spin-echo magnetic resonance (MR) imaging data was implemented to estimate whole brain (gray and white matter) and cerebrospinal fluid volumes and to display three-dimensional surface reconstructions of specified tissue classes. The techniques were evaluated by assessing the radiometric variability of MR volume data and by comparing automated and manual procedures for measuring tissue volumes. Results showed (a) the homogeneity of the MR data and (b) that automated techniques were consistently superior to manual techniques. Both techniques, however, were affected by the complexity of the structure, with simpler structures (eg, the intracranial cavity) showing less variability and better spatial correlation of segmentation results between raters. Moreover, the automated techniques were completed for whole brain in a fraction of the time required to complete the equivalent segmentation manually. Additional evaluations included interrater reliability and an evaluation that included longitudinal measurement, in which one subject was imaged sequentially 24 times, with reliability computed from data collected by three raters over 1 year. Results showed good reliability for the automated segmentation procedures.

Body Fluid Compartments↗

PubMedDate not supplied

Image analysis and computer vision in medicine.

Multimedia lives with images; medical images are born from digital imaging. A physician's multimedia workstation cannot exist without tools for manipulating images, performing measurements and, generally speaking, extracting and collecting pieces of information from the available data. Image analysis and computer vision constitute a wide and rapidly evolving field. This paper is intended as an introductory document for medical imaging researchers and practitioners wishing an overview of recent developments and trends. The major lines of activities in the domain are presented, under the common framework of a processing pipeline. After a presentation of the various stages of this pipeline, current subjects of research are indicated.

Computer Graphics↗

PubMedDate not supplied

3D surface rendered MR images of the brain and its vasculature.

Both time-of-flight and phase contrast magnetic resonance angiography images are combined with stationary tissue images to provide data depicting two contrast relationships yielding intrinsic discrimination of brain matter and flowing blood. A computer analysis is based on nearest neighbor segmentation and the connection between anatomical structures to partition the images into different tissue categories: from which, high resolution brain parenchymal and vascular surfaces are constructed and rendered in juxtaposition, aiding in surgical planning.

Algorithms↗

PubMedDate not supplied

Semiautomated ROI analysis in dynamic MR studies. Part I: Image analysis tools for automatic correction of organ displacements.

The most important problem in the analysis of time sequences is the compensation for artifactual motion. Owing to motion, medical images of the abdominal region do not represent organs with fixed configuration. Analysis of organ function with dynamic contrast medium studies using regions of interest (ROIs) is thus not readily accomplished. Images of the organ of interest need to be registered and corrected prior to a detailed local analysis. We have developed an image analysis scheme that allows the automatic detection of the organ contours, the extraction of the motion parameters per frame, and the registration of images. The complete procedure requires only minimal user interaction and results in a readjusted image sequence, where organs of interest remain fixed. Both a visual analysis of the dynamic behavior of functional properties and a quantitative statistical analysis of signal intensity versus time within local ROIs are considerably facilitated using the corrected series.

Algorithms↗

PubMedDate not supplied

Semiautomated ROI analysis in dynamic MR studies. Part II: Application to renal function examination.

Fast MR techniques and the application of water-soluble contrast agents allow the simultaneous examination of renal morphology and the functional aspects of glomerular filtration using bolus injections of Gd-DTPA. Spatial resolution is sufficient to resolve individual renal pyramids, but the quantitative examination of regions of interest (ROIs) is severely impeded by organ movements due to variations of the end-inspiratory position. A new image-processing scheme has been used and tested in 23 normal volunteers and patients. This scheme replaces a tedious frame-by-frame ROI analysis by positional correction of renal regions of all frames of the sequence such that the definition of the regions has to be performed only once. The signal intensities (SIs) of the local regions in each frame are used to compute statistics and to generate curves representing local temporal SI changes due to contrast agent excretion. The success rate of the procedure depends largely on the image quality and on the adherence to a proved acquisition protocol. The present article shows that the combination of MR and robust and reliable image-processing methods can be important for the highly automated analysis of a large number of images acquired as dynamic studies.

Adult↗