Viewpoints on Medical Image Processing: From Science to Application.

Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics.

Grid computing for detailed hemodynamics-simulation-based planning of endovascular interventions.

Detailed numerical simulations of blood flow in arteries with various malformations and its conjugate loads on the vessel walls have been a research topic for specialized medical and engineering communities over decades. The present state of computing resources and software allows access to these elaborate diagnostic and research tools to a broad user circle and even to integrate them into clinical workflows. To tap the full potential of hemodynamic simulations, a Grid-based "virtual vessel surgery" application has been developed and deployed as part of the image processing module of the MediGRID project of the German Federal Ministry of Education and Science (BMBF).

Classification models for early detection of prostate cancer.

We investigate the performance of different classification models and their ability to recognize prostate cancer in an early stage. We build ensembles of classification models in order to increase the classification performance. We measure the performance of our models in an extensive cross-validation procedure and compare different classification models.

DICOM image communication in globus-based medical grids.

Grid computing, the collaboration of distributed resources across institutional borders, is an emerging technology to meet the rising demand on computing power and storage capacity in fields such as high-energy physics, climate modeling, or more recently, life sciences. A secure, reliable, and highly efficient data transport plays an integral role in such grid environments and even more so in medical grids. Unfortunately, many grid middleware distributions, such as the well-known Globus Toolkit, lack the integration of the world-wide medical image communication standard Digital Imaging and Communication in Medicine (DICOM).

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.

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.

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.

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.

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.