Eörs Máté

Method for automatically segmenting the spinal cord and canal from 3d CT images

We present two approaches for automatically segmenting the spinal cord/canal from native CT images of the thorax region containing the spine. Different strategies are included to handle images where only part of the spinal column is visible. The algorithms require one seed point given on a slice located in the middle region of the spine, and the rest is automatic. The spatial extent of the spinal cord/canal is determined automatically. An extended region-growing technique is suggested for segmenting the spinal canal while active contours are applied if the spinal cord is to be segmented. Both methods work in 2D and use propagated information from neighboring slices. They are also very rapid in execution, that means an efficient, user-friendly workflow. The methods were evaluated by radiologists and were found to be useful (in reducing/eliminating contouring labor and time) and met the accuracy and repeatability requirements for the particular task.

Application of automatic image registration in a segmentation framework of pelvic CT images

In radiation treatment (RT) planning, clinicians must trace the outline of a few critical structures on a large number of images. Using automated image segmentation could save tremendous time and effort. Segmentation of the organs near the pubic bone (prostate and bladder) is an important and challenging task: Some of the neighboring organs have similar density values in the CT images and the border between the different organs is hardly visible. In a segmentation framework, transforming a CT study to a common reference frame is used in two tasks: For statistical atlas (model) generation, and in the clinical application, establishing the voxel-to-voxel correspondence between the study and the model. In these cases precise alignment of all anatomical structures is not crucial, the focus is on proper alignment of the pubic bone area and fast execution. Our proposed method solves this by a new, two step process based on a voxel similarity-based registration algorithm.

Data processing system for nuclear medicine images

A software system (MicroSegams) is described which is able to acquire, store, present, and process nuclear medicine images. The most important basic procedures (e.g., image acquisition from a gamma camera, selection of a study from the catalogue, drawing regions of interest, etc.) and complete clinical procedures (e.g., renography, brain perfusion, etc.) can be activated directly from the system menu by mouse. The language sLan (a subset of programming language C containing more than 200 elementary functions) can also be used to write new procedures to be interpreted or compiled. The system is offered as a basic tool for the performance of static, dynamic, gated, or tomographic investigations in clinical practice.

Mathematical generation of normal data for evaluating myocardial perfusion studies

In this paper, we present a new mathematical method that synthesizes normal data sets for quantification of regional myocardium perfusion. In clinical practice, regional myocardial perfusion is often measured with a gamma camera and quantified via circumferential profile analysis. Normal reference profile data is used to increase the accuracy of the clinical interpretations. Our goal is to create reference data from an existing set of archived studies. An iterative mathematical method, based on two statistical hypotheses, was used to generate the study set instead of collecting normal examinations from a healthy population. Clinical validation is based on interpretations by six independent observers. Results of evaluation with synthesized normal data and its validation are presented.