Ingwer C. Carlsen

Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images

A new multi-scale segmentation technique for line-like structures in 2D and 3D medical images is presented. It is based on normalized first and second derivatives and on the eigenvector analysis of the hessian matrix. Application areas are the segmentation and tracking of bloodvessels, electrodes, catheters and other line-like objects. It allows for the estimation of the local diameter, the longitudinal direction and the contrast of the vessel and for the distinction between edge-like and line-like structures. The method is applicable as automatic 2D and 3D line-filter, as well as for interactive algorithms that are based on local direction estimation. A 3D line-tracker has been constructed that uses the estimated longitudinal direction as step-direction. After extraction of the centerline, the hull of the structure is determined by a 2D active-contour algorithm, applied in planes, orthogonal to the longitudinal line-direction. The procedure results in a stack of contours allowing quantitative crosssection area determination and visualization by means of a triangulation based rendering.

Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy

During the last couple of years virtual endoscopic systems (VES) have emerged as standard tools that are nowadays close to be utilized in daily clinical practice. Such tools render hollow human structures, allowing a clinician to visualize their inside in an endoscopic-like paradigm. It is common practice that the camera of a virtual endoscope is attached to the centerline of the structure of interest, to facilitate navigation. This centerline has to be determined manually or automatically, prior to an investigation. While there exist techniques that can straightforwardly handle simple tube-like structures (e.g. colon, aorta), structures like the tracheobronchial tree still represent a challenge due to their complex branching. In these cases it is necessary to determine all branching points within the tree which is - because of the complexity - impractical to be accomplished in a manual manner. This paper presents a simultaneous segmentation/skeletonization algorithm that extracts all major airway branches and large parts of the minor distal branches (up to 7th order) using a front propagation approach. During the segmentation the algorithm keeps track of the centerline of the segmented structure and detects all branching points. This in turn allows the full reconstruction of the tracheobronchial tree.

A Multi-scale Line Filter with Automatic Scale Selection Based on the Hessian Matrix for Medical Image Segmentation

A multi-scale segmentation technique for line-like structures in 2D and 3D medical images is presented. It is based on normalized second derivatives and on the eigenvector analysis of the Hessian matrix. The method allows for the estimation of the local diameter, the longitudinal direction and the contrast of line-structures and for the distinction between edge-like and line-like structures. The characteristics of the method in respect to several analytic line-profiles as well as the influence of neighboring structures and line-bending is discussed. The method is applied to 3D medical images.

Motion compensated projection reconstruction

Over recent years, MRI has shown the capability for real‐time applications. Although the acquisition times of fast MRI methods have been reduced significantly, patient motion during a magnetic resonance imaging (MRI) examination still causes artifacts in the image. In this paper, the effects of motion in MRI using a radial acquisition scheme are examined. It is shown that motion can be estimated without the use of additional measurement, based on the acquired projections only. A new reconstruction technique is introduced that integrates a motion compensation algorithm into the MR‐reconstruction process, resulting in a significant reduction of blurring artifacts in the reconstructed images. The proposed method is applied to different kinds of motion such as kinetic joint studies. Magn Reson Med 41:954–963, 1999.

Surface reconstruction from stereoscopy and “shape from shading” in SEM images

The computational reconstruction of surface topographies from scanning electron microscope (SEM) images has been extensively investigated in the past, but fundamental image processing problems still exist. Since conventional approaches adapted from general-purpose image processing have not sufficiently met the requirements in terms of resolution and reliability, we have explored combining different methods to obtain better results.This paper presents a least-squares combination of conventional stereoscopy with “shape from shading” and a way of obtaining self-consistent surface profiles from stereoscopy and “stereo-intrinsic shape from shading” using dynamic programming techniques. Results are presented showing how this combined analysis of multi-sensorial data yields improvements of the reconstructed surface topography that cannot be obtained from individual sensor signals alone.

Automatic lesion tracking for a PET/CT based computer aided cancer therapy monitoring system

Response assessment of cancer therapy is a crucial component towards a more effective and patient individualized cancer therapy. Integrated PET/CT systems provide the opportunity to combine morphologic with functional information. However, dealing simultaneously with several PET/CT scans poses a serious workflow problem. It can be a difficult and tedious task to extract response criteria based upon an integrated analysis of PET and CT images and to track these criteria over time. In order to improve the workflow for serial analysis of PET/CT scans we introduce in this paper a fast lesion tracking algorithm. We combine a global multi-resolution rigid registration algorithm with a local block matching and a local region growing algorithm. Whenever the user clicks on a lesion in the base-line PET scan the course of standardized uptake values (SUV) is automatically identified and shown to the user as a graph plot. We have validated our method by a data collection from 7 patients. Each patient underwent two or three PET/CT scans during the course of a cancer therapy. An experienced nuclear medicine physician manually measured the courses of the maximum SUVs for altogether 18 lesions. As a result we obtained that the automatic detection of the corresponding lesions resulted in SUV measurements which are nearly identical to the manually measured SUVs. Between 38 measured maximum SUVs derived from manual and automatic detected lesions we observed a correlation of 0.9994 and a average error of 0.4 SUV units.

An Adaptive Irregular Grid Approach Using SIFT Features for Elastic Medical Image Registration

Elastic image registration is an active field of current research. In contrast to B-spline transformations, defined on a regular grid of control points, we consider physics-based transformations defined on irregular grids. With such control point arrangements, the required nonlinear behaviour can be described with less parameters. We combine this transformation model with the SIFT algorithm for identifying prominent image structures that serve well as initial control point positions. Medical applications from different modalities show that with this intelligent control point initialization the number of required control points can be further reduced, which significantly speeds up the registration process.

Knowledge Based Interpretation of Cranial MR Images

In this paper we report on a system for knowledge based interpretation of cranial MR images which has been set up within the framework of the European Community funded COVIRA project consortium [1]. The system combines an image-data-driven (bottom up) image segmentation based on fuzzy clustering with a model-driven (top down) interpretation approach which is based on fuzzy relational matching and fuzzy clique search. The application domain knowledge is represented in four knowledge sources which represent the available clinical and anatomical knowledge as well as knowledge about the (MR-) sensor and MR-specific tissue parameters. From these knowledge sources, a case model in the form of a semantic net is generated, containing knowledge relevant to the particular medical case under consideration.

Correlative averaging for radial magnetic resonance imaging

Abstract Although Magnetic Resonance Imaging (MRI) has faced a dramatic increase in real-time capabilities over the last year, acceptable image quality still limits the actually achievable acquisition speed. This paper presents a motion-compensated noise filter that, on the basis of hierarchical motion estimation and edge-preserving adaptive weighted averaging, has been integrated into a segmented radial MR acquisition scheme. In several studies of moving joints, the proposed approach led to significant reductions in the noise level without introducing motion blur. The improved image quality would, in principle, allow more than double the acquisition speed, retaining the original image quality.

IKSPFH—Concept and implementation of an object-oriented framework for image processing

Abstract This paper describes the concept of an Iconic Kernel System (IKSPFH)—a general software toolbox allowing image-processing algorithms to be implemented independently of computer hardware and operating systems. Following a strict object-oriented design philosophy, it provides a basic set of data structures and operations from which the application-specific software environments can be built. Special attention has been paid to the incorporation of already existing software into object-oriented environments, and to the integration of general classes of user interface systems. The concept has been implemented as a working prototype using several programming languages in a DEC-VAX/VMS environment.