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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. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment.

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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). The very resource-intensive parts of that application, including not only the numerical simulation itself, but also automated data pre- and post-processing and visualization are implemented and are made available through the MediGRID portal. Some highly interactive workflow steps of the application are left at the user site but automated - via a specially developed Web interface - to a degree allowing intuitive, fast interaction and seamless integration with the Grid components. Standard middleware provided by D-Grid is used throughout. The complete workflow is implemented into the grid and can in principle be carried out using no external software. It was applied to real vessel data with a stenosis and an aneurysm, respectively.

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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. The datasets come from clinical examinations and some of the classification models are already in use to support the urologists in their clinical work.

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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). Currently, the DICOM protocol first needs to be converted to the file transfer protocol (FTP) that is offered by the grid middleware. This effectively reduces most of the advantages and security an integrated network of DICOM devices offers. In this paper, a solution is proposed that adapts the DICOM protocol to the Globus grid security infrastructure and utilizes routers to transparently route traffic to and from DICOM systems. Thus, all legacy DICOM devices can be seamlessly integrated into the grid without modifications. A prototype of the grid routers with the most important DICOM functionality has been developed and successfully tested in the MediGRID test bed, the German grid project for life sciences.

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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.

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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.

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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.

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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.

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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.

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