Steven Lobregt

A discrete dynamic contour model

A discrete dynamic model for defining contours in 2-D images is developed. The structure of this model is a set of connected vertices. With a minimum of interaction, an initial contour model can be defined, which is then automatically modified by an energy minimizing process. The internal energy of the model depends on local contour curvature, while the external energy is derived from image features. Solutions are presented to avoid undesirable deformation effects, like shrinking and vertex clustering, which are common in existing active contour models. The deformation process stops when a local minimum of the energy function is reached. The final shape of the model is a reproducible approximation of the desired contour. Results of applying the method to computer-generated images, as well as clinical images, are presented.

Automated 3-D PDM construction from segmented images using deformable models

In recent years, several methods have been proposed for constructing statistical shape models to aid image analysis tasks by providing a priori knowledge. Examples include principal component analysis of manually or semiautomatically placed corresponding landmarks on the learning shapes [point distribution models (PDMs)], which is time consuming and subjective. However, automatically establishing surface correspondences continues to be a difficult problem. This paper presents a novel method for the automated construction of three-dimensional PDM from segmented images. Corresponding surface landmarks are established by adapting a triangulated learning shape to segmented volumetric images of the remaining shapes. The adaptation is based on a novel deformable model technique. We illustrate our approach using computed tomography data of the vertebra and the femur. We demonstrate that our method accurately represents and predicts shapes.

Shape Constrained Deformable Models for 3D Medical Image Segmentation

To improve the robustness of segmentation methods, more and more methods use prior knowledge. We present an approach which embeds an active shape model into an elastically deformable surface model, and combines the advantages of both approaches. The shape model constrains the flexibility of the surface mesh representing the deformable model and maintains an optimal distribution of mesh vertices. A specific external energy which attracts the deformable model to locally detected surfaces, reduces the danger that the mesh is trapped by false object boundaries. Examples are shown, and furthermore a validation study for the segmentation of vertebrae in CT images is presented. With the exception of a few problematic areas, the algorithm leads reliably to a very good overall segmentation.

Automatic Contour Propagation in Cine Cardiac Magnetic Resonance Images

We have developed a method for automatic contour propagation in cine cardiac magnetic resonance images. The method consists of a new active contour model that tries to maintain a constant contour environment by matching gray values in profiles perpendicular to the contour. Consequently, the contours should maintain a constant position with respect to neighboring anatomical structures, such that the resulting contours reflect the preferences of the user. This is particularly important in cine cardiac magnetic resonance images because local image features do not describe the desired contours near the papillary muscle. The accuracy of the propagation result is influenced by several parameters. Because the optimal setting of these parameters is application dependent, we describe how to use full factorial experiments to optimize the parameter setting. We have applied our method to cine cardiac magnetic resonance image sequences from the long axis two-chamber view, the long axis four-chamber view, and the short axis view. We performed our optimization procedure for each contour in each view. Next, we performed an extensive clinical validation of our method on 69 short axis data sets and 38 long axis data sets. In the optimal parameter setting, our propagation method proved to be fast, robust, and accurate. The resulting cardiac contours are positioned within the interobserver ranges of manual segmentation. Consequently, the resulting contours can be used to accurately determine physiological parameters such as stroke volume and ejection fraction

A novel method for digital X-ray imaging of the complete spine

One of the practical reasons for applying film-cassettes instead of digitized video images, is that for some applications the entrance plane of the Image Intensifier is too small to cover the relevant part of a patients anatomy in one single exposure. Imaging of the deformed spine is such an application.

Automated 3D PDM construction using deformable models

In recent years several methods have been proposed for constructing statistical shape models to aid image analysis tasks by providing a-priori knowledge. Examples include principal component analysis (PCA) of manually or semi-automatically placed corresponding landmarks on the learning shapes (point distribution models, PDM), which is time consuming and subjective. However automatically establishing surface correspondences continues to be a difficult problem. This paper presents a novel method for the automated construction of 3D PDM from segmented images. Corresponding surface landmarks are established by adapting a triangulated learning shape to segmented volumetric images of the remaining shapes. The adaptation is based on a novel deformable model technique. We illustrate our approach using CT data of the vertebra and the femur. We demonstrate that our method accurately represents and predicts shapes.

Image Quality Improvements in Volume Rendering

This paper presents some methods to improve the image quality obtained with volume rendering. By computing the opacity, color and gradient of each sample point directly at the sample position, the image quality has improved over methods which compute these values at the voxel positions. A new method for calculating the gradient is presented. These improvements result in small details becoming clearly visible. It also allows high zoom rates without generating blurry images. The opacity is corrected for the sample rate, allowing a consistent translucency setting.

Coupled deformable models with spatially varying features for quantitative assessment of left ventricular function from cardiac MRI

Cardiac MRI has improved the diagnosis of cardiovascular diseases by enabling the quantitative assessment of functional parameters. This requires an accurate identification of the myocardium of the left ventricle. This paper describes a novel segmentation technique for automated delineation of the myocardium. We propose to use prior knowledge by integrating a statistical shape model and a spatially varying feature model into a deformable mesh adaptation framework. Our shape model consists of a coupled, layered triangular mesh of the epi- and endocardium. It is adapted to the image by iteratively carrying out i) a surface detection and ii) a mesh reconfiguration by energy minimization. For surface detection a feature search is performed to find the point with the best feature combination. To accommodate the different tissue types the triangles of the mesh are labeled, resulting in a spatially varying feature model. The energy function consists of two terms: an external energy term, which attracts the triangles towards the features, and an internal energy term, which preserves the shape of the mesh. We applied our method to 40 cardiac MRI data sets (FFE-EPI) and compared the results to manual segmentations. A mean distance of about 3 mm with a standard deviation of 2 mm to the manual segmentations was achieved.

Dental Implant Planning in EasyVision

This work is part of the EC funded PISA project which aims at the development of tools for design and manufacturing of Personalized Implants and Surgical Aids. Sub-optimal implant positioning is a major reason for implant failure and (too) early revision. Although planning is possible to various extends on currently available systems, there is in general no means to transfer the planning to the patient. The PISA project covers the transfer to the patient as well as the planning. Within this project, PMS focused on dental implant planning and design of appropriate drill guides to transfer the planning to the patient. A prototype application was developed on our EasyVision clinical workstation, which includes the following steps:

Shape-model-based adaptation of 3D deformable meshes for segmentation of medical images

Segmentation methods based on adaptation of deformable models have found numerous applications in medical image analysis. Many efforts have been made in the recent years to improve their robustness and reliability. In particular, increasingly more methods use a priori information about the shape of the anatomical structure to be segmented. This reduces the risk of the model being attracted to false features in the image and, as a consequence, makes the need of close initialization, which remains the principal limitation of elastically deformable models, less crucial for the segmentation quality. In this paper, we present a novel segmentation approach which uses a 3D anatomical statistical shape model to initialize the adaptation process of a deformable model represented by a triangular mesh. As the first step, the anatomical shape model is parametrically fitted to the structure of interest in the image. The result of this global adaptation is used to initialize the local mesh refinement based on an energy minimization. We applied our approach to segment spine vertebrae in CT datasets. The segmentation quality was quantitatively assessed for 6 vertebrae, from 2 datasets, by computing the mean and maximum distance between the adapted mesh and a manually segmented reference shape. The results of the study show that the presented method is a promising approach for segmentation of complex anatomical structures in medical images.