Biomedical subjects

Thomas Tolxdorff

Publications and source records attributed to Thomas Tolxdorff.

5 recordsLinked to original sources

PubMed2005

Automatic parameter optimization for de-noising MR data.

This paper describes an automatic parameter optimization method for anisotropic diffusion filters used to de-noise 2D and 3D MR images. The filtering process is integrated into a closed-loop system where image improvement is monitored indirectly by comparing the characteristics of the suppressed noise with those of the assumed noise model at the optimal point. In order to verify the performance of this approach, experimental results obtained with this method are presented together with the results obtained by median and k-nearest neighbor filters.

Algorithms↗

PubMed2004

XML knowledge database of MRI-derived eye models.

In a model-based approach, MR images were used to build a database of individual eye models. In order to store the features of the specific eye morphology in an extensible, structured and Internet-accessible database, an appropriate XML structure was implemented. A document type definition was developed that managed the data of the correlated feature space and defined associations via training data sets. The classification and retrieval system has been implemented in Java and successfully applied to classify data sets. Classified data were then added to the database. The presented approach can be easily transferred to similar classification implementations.

Eye↗

PubMed2004

Feature extraction and supervised classification of MR images to support proton radiation therapy of eye tumors.

Proton therapy has the potential for high-precision radiotherapy of retinal tumors. However, the standardized eye models currently used do not fully account for the patient's individual anatomy. To better exploit the data provided by MR images, a model-based approach was used based on a database of eye models. A face recognition algorithm was advanced to define similarity criteria between the reference image and the actual image. After building a high-dimensional feature vector and using a training data set, the reference model was selected by using the minimum Mahalanobis distance between the image to be classified and the reference images.

Eye Neoplasms↗

PubMed2003

3D reconstruction of organ surfaces using model-based snakes.

In this article a new segmentation approach is described that is based on case-based reasoning and a combination of various established image processing concepts described in the current literature. Previously segmented data sets are used as anatomical models that represent the cases, called reference models. They describe the expected surface shape and representation of the organ in the data material. The segmentation task is solved by finding a reference model that is similar to the current data set and then by adapting the reference segmentation to the current data set. Image segmentation can be divided into the steps "determination of the image context", "selection and adjustment of the reference model", and "application of the model-based snake". The necessary interaction time was reduced by more than 60%, including postprocessing to correct for possible segmentation errors.

Computer Simulation↗

PubMed2002

Feature-based, automated segmentation of cerebral infarct patterns using T2- and diffusion-weighted imaging.

Diffusion-weighted imaging enables the diagnosis of cerebral ischemias very early, thus supporting therapies such as thrombolysis. However, morphology and tissue-characterizing parameters (e.g. relaxation times or water diffusion) may vary strongly in ischemic regions, indicating different underlying pathologic processes. As the determination of the parameters by a supervised segmentation is very time consuming, we evaluated whether different infarct patterns may be segmented by an automated, multidimensional feature-based method using a unified segmentation procedure. Ischemias were classified into 5 characteristic patterns. For each class, a 3D histogram based on T(2)- and diffusion-weighted images as well as calculated apparent diffusion coefficients (ADC) was generated from a representative data set. Healthy and pathologic tissue classes were segmented in the histogram as separate, local density maxima with freely shaped borders. Segmentation control parameters were optimized in a 3-step procedure. The method was evaluated using synthetic images as well as results of a supervised segmentation. For the analysis of cerebral ischemias, the optimal control parameter set led to sensitivities and specificities between 1.0 and 0.9.

Algorithms↗