Seeing and understanding 3-d medical images through 2-d renditions

Abstract. The efficiency of data analysis of 3-d measurement data such as medical images can be enhanced if the expert user is enabled to incorporate his knowledge into the analysis process in an interactive fashion. Potential misinterpretation may arise from the necessary information reduction when presenting the 3-d data by a 2-d rendition. We present concepts of data visualisation during analysis, which is intended to avoid such problems during data analysis. The emphasis is on providing a visualisation, which transfers information such that the user perceives not only the data but also the state of interpretation. Simple metaphors are presented which support the development of methods under these concepts. Hybrid and focal volume rendering illustrate as examples a realisation of aspects of the concept for analysing high-resolution CT data

Image Segmentation By Stochastically Relaxing Contour Fitting

Contour fitting in image segmentation guarantees the closedness of the segment boundary at any stage of the approximation, thus preserving an important global property of the segment. A contour fitting scheme consists of strategies to modify the contour and to optimize the current approximation. For the purpose of contour modification a two-dimensional adaptation of a geometrically deformable model (GDM) is employed. A GDM is a polygon being placed into a structure to be segmented and being deformed until it adequately matches the segment's boundary. Deformation occurs by vertex translation and by introducing new vertices. Sufficient boundary resemblance is achieved by choosing vertex locations in such way that a function is optimized whose different terms describe features attributed to the segment or its boundary. In order to find ideal vertex locations, a stochastic optimisation method is applied which is able to avoid termination of the deformation process in a local optimum (caused, e.g., by noise or artefacts). The deformation terminates after segment boundary and GDM are sufficiently similar. Missing boundary parts between vertices are detected by a path searching technique in a graph whose nodes represent pixel locations. The segmentation algorithm was found to be versatile and robust in the presence of noise being able to segment artificial as well as real image data

A WEIGHT-ADAPTIVE DYNAMIC MODEL FOR SHAPE SEGMENTATION

Physically based dynamic models are able to describe variable shapes without prior training. Their behaviour to find an object is intuitive, which facilitates corrections of false results. Expressing shape variation as physical feature, however, may be difficult because the physics of the model has little to do with the shape variation of instances of a class of objects. We present a dynamic model, which automatically adapts model parameters based on results of previous segmentations. The model was applied to artificial data and to images of leaves. Results show that the adapted model finds the correct shape more accurate than a model with preset parameters. Investigation of the parameterisation from adaptation also showed that they may be interpreted in terms of the semantics of the shape class represented. 1

Feasibility of Hough-Transform-based Iris Localisation for Real-Time-Application

We present a fast method for locating iris features in frontal face images based on the Hough transform. It consists of an initial iris detection step and a tracking step which uses iris features from initialisation for speeding up computation. The purpose of research was to evaluate the feasibility of the method for tracking at 200 frames per second or higher. Processing speed of the prototypical implementation on a 266Mhz Pentium II PC is approximately 6 seconds for initial iris detection and about 0.05 seconds for each tracking step. The algorithm was applied to images of subjects taken under normal room lighting conditions. Tests showed robustness with respect to shadowing and partial occlusion of the iris. The localisation error was below two pixels. Accuracy for tracking was within one pixel. A reduction of the number of pixels which are processed in the tracking step by 90% showed a modest degradation of the results.

Local Identification and Removal of Scatter Artefacts based on the Temporal Information in Dynamic SPECT images

Scatter in SPECT images may mask decreseased uptake of radiopharmaceuticals in the left ventricle of the heart which can alter the diagnostic outcome of the study. The newly developed dynamic SPECT (dSPECT) method, which reconstructs 4D images from a standard acquisition protocol, provides additional temporal information which may be helpful to recognise such artefacts. Each voxel carries a time signature, which is different for different organs. In this paper, we investigate whether this signature can be used to detect and remove scatter. Time activity curves (TACs) from segmented data are tested for their potential to locally identify scatter according to a simple model. The investigation is carried out on artificial artefacts in real patient data as well as on existing scatter. Tests on the artificial artefacts showed that scatter can indeed be detected and removed while tests on real data revealed that the simplified model may suffice to remove the majority of local scatter.