Current Medical Imaging - Volume 9, Issue 2, 2013

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.

In this work an experimental setup validating recently introduced optimization results for a dynamic field free line imaging device for the use in magnetic particle imaging is presented. In this setup, curved rectangular coils are used for magnetic field generation in dynamic field free line imaging for the first time. These coils promise an increase in magnetic field quality by a factor of five, as well as a simultaneous decrease in electrical power consumption by a factor of almost four compared to results achieved with commonly used circular coil. With this optimized setup, a complete dynamic field free line trajectory is realized. To do so, the MPI selection and drive field is simultaneously generated, measured and evaluated with respect to magnetic field quality and electrical power consumption. With the generated fields, it will be possible to take advantage of efficient Radon-based reconstruction algorithms, which arise for a line detection scheme and will considerably speed up reconstruction time. The presented results agree with simulations and do therefore support the implementation of a very efficient and precise dynamic field free line imager.

Diagnostic perfusion imaging using CT or MRI has been available for several years. One of its applications is acute stroke diagnosis. Interventional perfusion imaging using C-arm CT is a novel eld of research. It could provide perfusion information during catheter-guided stroke treatment in order to optimize patient management. In this survey article, the clinical and technical background of this topic are described and rst results from in-vivo measurements are presented.

Spatio-temporal analysis of multi-cellular systems at the single cell level has emerged as one of the fundamental techniques in cell biology, developmental biology, and experimental medicine. The vast amount of image data obtained by tracking experiments requires computerized approaches, either fully unsupervised or semi-automated to facilitate an in-depth analysis of the underlying biological principles. Automated cell tracking methods need to deal with different modes of cell migration, touching and overlapping of cells, as well as with changing cell numbers due to mitosis. In this paper, we explore the applicability of a rather general tracking paradigm based on fluid registration of the image sequence coupled to a level set segmentation. We demonstrate that a consistent combination of registration and segmentation can overcome limitations of previous registration based tracking methods and yields encouraging results in terms of tracking reliability and mitosis detection.

Acquisition of 3-D anatomical structure in minimally invasive surgery is an important step towards intraoperative guidance. In this context, the first prototype of a Time-of-Flight/RGB endoscope was engineered for simultaneous range and color data acquisition. Intrinsic and stereo camera calibration are essential to achieve an intuitive visualization of colored surfaces. Due to the early prototype stage, inhomogeneous illumination and low resolution (64x50 px) complicate the calibration significantly. To overcome these challenges, we propose a fully automatic multiscale calibration framework using a self-encoded marker for checkerboard detection. A first application demonstrates the feasibility of intra-operative measurement. Using our calibration scheme, we achieved a reprojection error of less than 0.7 px for the Time-of-Flight camera and 0.5 px of the RGB camera. Our framework eases calibration and enables future applications to use combined range and colored data.

In order to achieve an accurate attenuation correction in brain PET images acquired by hybrid PET/MR scanners, it is mandatory to delineate cortical bone and cavities in the MR images. Automated segmentation of the anatomical Ultra short echo time (UTE) MR images into different regions allows to assign them to the corresponding attenuation coefficients. The UTE sequence yields two components obtained by echo times TE=0.07 ms and TE=2.46 ms. UTE images were first normalized by means of a scatterplot-based normalization technique, in which the scatterplot of a given scan is fitted into that of reference‘s. Second, a correction mask was generated to reduce the problem of the head edges resulting in the first component. Third, the fully automatic virtual extraction was realized by developing two methods: the two-class Support Vector Machine (C SVM) -based method and the single-class Support Vector Machine (S SVM)-based method using different kernels. Four datasets were evaluated with the corresponding registered CT scans and with an expert manual segmentation of the cavities. The C SVM-based segmentation of the skull using the RBF kernel reached a Dice coefficient (D) value of 0.83±0.042 (mean ± SD). The S SVM-based segmentation of cavities using the RBF kernel attained a D value of 0.73±0.02. Based on the present results, the following conclusions can be drawn: First with our methods, the fully automatic segmentation of cortical bone and cavities reaches good results. Second, intensity normalization enables the development of the S SVMbased method for segmentation of cortical bone and cavities.

Patient specific surface models of the jaw are beneficial for pre-operative planning and manufacturing of customized prosthesis. Such models can be generated on the basis of dental cone-beam CT images, but those suffer from a comparatively bad image quality with regard to the signal-to-noise ratio. Therefore, in this work, a statistical shape model (SSM) is used for robust segmentation of the mandible bone. While previous works with that application require manual interaction during SSM construction, we establish correspondence fully automatic by minimizing the description length of the model. Subsequently, the mandible bone is automatically localized and segmented using the SSM as shape constraint. The standard SSM constraint is known to be inherently limited insofar as patient specific anatomical details can often not be represented. To overcome this limitation, a new, mathematically sound, computationally fast, and intuitively interpretable, relaxed SSM constraint is derived, which can be applied without any user-provided parameter. Evaluation on clinical cone beam CT images yields an improvement of the Jaccard coefficient up to 45% compared to the standard SSM constraint. Our results are similar to that of alternative methods in the literature, indicating the general potential of the proposed relaxed SSM constraint for medical image segmentation.

The analysis of blood vessel structures in the retinal fundus images is important for the diagnosis of many diseases. Vessel segmentation can assist in the detection of pathological changes which are possible indicators for arteriosclerosis, retinopathy, micro aneurysms and macular degeneration. In this article, two approaches to blood vessel segmentation are presented. Both of them are based on the evaluation of phase symmetry information using complex logarithmic Gabor wavelets. In the first approach, a phase symmetry filter is combined with the front propagation algorithm fast marching, the second method uses a hysteresis thresholding step. The approaches have shown excellent results for the vessel segmentation on colon polyps. Although they were adapted to structures in retinal fundus imaging, neither eye specific knowledge nor supervised classification methods are used. For high comparability with previous publications in the field, the algorithms are evaluated on the two publicly available image databases DRIVE and STARE. The hysteresis thresholding approach which performs slightly superior achieves an average accuracy of 94.92% (sensitivity: 71.22%, specificity: 98.41%) for the DRIVE and 95, 65% (sensitivity: 71.87%, specificity: 98.34%) for the STARE database.

This paper provides a new mathematical approach for modelling the influence of the cells of the immune system, more precisely of microglial cells, on the progression of malignant primary brain tumours. A hybrid approach is used to model the cellular tumour progression, the development of the local nutrient concentration and of the density of the extracellular matrix (ECM). Microglia present in the surrounding of brain tumours are activated and attracted by signals emitted by tumour cells, which are described by a partial differential equation. The secretion of matrix degrading enzymes from microglia/macrophages can be modelled with the help of an additional term for the degradation of the ECM. This supports a more invasive migration of tumour cells. Parameter estimation and model validation use data from published studies and from our own in-vitro experiments. To our knowledge, we present for the first time a model of microglial cells in the context of tumour growth. The simulations reproduce quantitatively the in-vitro experimental data on glioma cells invasion and proliferation. Moreover, the experimental in-silico results match with the cell behaviour reported in the literature. The proposed model, thus, is a promising approach for modelling brain tumour growth at the cellular level taking the innate immune system into account.

Virtual reality techniques can be used for the training of needle insertion interventions. Visuo-haptic training environments consist of a visual display of a simulated scene and a force-feedback device for haptic interaction. Here, we address the visualization of deformations of soft tissue in real-time. A finite differences method is used to calculate inverse displacement fields and deform volumetric image data in a liver puncturing scenario. Real patient CT-image data was used for this. A diffusive and linear-elastic formulation of the propagation of deformations in the inverse displacement field is augmented with a material function and a simplified modeling of a needle sliding in tissue. Evaluation has been done by comparison of the resulting inverse displacement from this algorithm to those from a finite element simulation. Based on the patient image data, a 2D mesh for the finite element simulation was created. On several points on the mesh, a needle insertion has been performed. Additionally, computational times of the implemented methods are measured to prove its real-time capability and the benefit of an implementation on general purpose graphics hardware. The results show a slightly better performance of the difusive formulation.