First experiences with the modelling and simulation package MIRACLES applied to a picture archiving and communication system (PACS) in a clinical environment

Since the construction of picture archiving and communication systems (PACS) appears to be extremely difficult, computer modelling and simulation are used as decision support tools. The package MIRACLES (Medical Image Representation, Archiving and Communication Learned from Extensive Simulation) has been developed at BAZIS in order to support the construction of simulation models of image information systems. This article discusses the application of MIRACLES to a prototypical PACS as being installed in a clinical environment. Attention is focussed to the required system analysis and difficulties which arose during the construction of the simulation model. The emphasis is on the presentation of the results of the simulation study, which show that simulation can be fruitfully used to predict, to analyse and to assist in solving performance problems. The simulation study confirmed assumptions and suppositions concerning both the system performance itself and strategies to improve the performance. The study also resulted in a number of concrete recommendations which might be useful for the set-up of the prototypical PACS.     

A 3D face matching framework for facial curves

Among the many 3D face matching techniques that have been developed, are variants of 3D facial curve matching, which reduce the amount of face data to one or a few 3D curves. The face’s central profile, for instance, proved to work well. However, the selection of the optimal set of 3D curves and the best way to match them has not been researched systematically. We propose a 3D face matching framework that allows profile and contour based face matching. Using this framework we evaluate profile and contour types including those described in the literature, and select subsets of facial curves for effective and efficient face matching. With a set of eight geodesic contours we achieve a mean average precision (MAP) of 0.70 and 92.5% recognition rate (RR) on the 3D face retrieval track of the Shape Retrieval Contest (SHREC’08), and a MAP of 0.96 and 97.6% RR on the University of Notre Dame (UND) test set. Face matching with these curves is time-efficient and performs better than other sets of facial curves and depth map comparison.     

The Gaussian scale-space paradigm and the multiscale local jet

A representation of local image structure is proposed which takes into account both the image's spatial structure at a given location, as well as its deep structure, that is, its local behaviour as a function of scale or resolution (scale-space). This is of interest for several low-level image tasks. The proposed basis of scale-space, for example, enables a precise local study of interactions of neighbouring image intensities in the course of the blurring process. It also provides an extrapolation scheme for local image data, obtained at a given spatial location and resolution, to a finite scale-space neighbourhood. This is especially useful for the determination of sampling rates and for interpolation algorithms in a multilocal context. Another, particularly straightforward application is image enhancement or deblurring, which is an instance of data extrapolation in the high-resolution direction.A potentially interesting feature of the proposed local image parametrisation is that it captures a trade-off between spatial and scale extrapolations from a given interior point that do not exceed a given tolerance. This (rade-off suggests the possibility of a fairly coarse scale sampling at the expense of a dense spatial sampling large relative spatial overlap of scale-space kernels).The central concept developed in this paper is an equivalence class called the multiscale local jet, which is a hierarchical, local characterisation of the image in a full scale-space neighbourhood. For this local jet, a basis of fundamental polynomials is constructed that captures the scale-space paradigm at the local level up to any given order.     

Interactive Fibre Structure Visualization of the Heart

The heart consists of densely packed muscle fibres. The orientation of these fibres can be acquired by using Diffusion Tensor Imaging (DTI) ex vivo. A good way to visualize the fibre structure in a cross section of the heart is by showing short line segments originating from the cross section and aligned with the local direction of the fibres. If the line segments are placed dense enough, one can see how the fibre orientations change. However, generation of the line segments takes time and thus the user has to wait for new geometry to be generated when the plane defining the cross section is changed. We present a new direct rendering method for the visualization of the 3D vector field in a 2D user‐definable cross section of a heart. On the intersection of the plane with the vector field, the full 3D vectors are rendered as 3D line segments with a local ray casting approach. No preprocessing of the data is needed and no geometry is generated. This technique allows a fast inspection of the data to identify interesting areas where further analysis is necessary (e.g. quantification or generation of streamlines). We also show how the technique is generalized to other glyph shapes than line segments by implementing ellipsoids.     

A Linear Image Reconstruction Framework Based on Sobolev Type Inner Products

Exploration of information content of features that are present in images has led to the development of several reconstruction algorithms. These algorithms aim for a reconstruction from the features that is visually close to the image from which the features are extracted. Degrees of freedom that are not fixed by the constraints are disambiguated with the help of a so-called prior (i.e. a user defined model). We propose a linear reconstruction framework that generalizes a previously proposed scheme. The algorithm greatly reduces the complexity of the reconstruction process compared to non-linear methods. As an example we propose a specific prior and apply it to the reconstruction from singular points. The reconstruction is visually more attractive and has a smaller 핃₂-error than the reconstructions obtained by previously proposed linear methods. Bart Jansen, Frans Kanters and Remco Duits are joint main authors of this article.     

Induction of human breast cancer cell apoptosis from G2/M preceded by stimulation into the cell cycle by Z-1,1-dichloro-2,3-diphenylcyclopropane

We have shown previously that Z-1,1-dichloro-2,3-diphenylcyclopropane (a.k.a. Analog II, A(II)) inhibits human breast cancer cell proliferation regardless of estrogen receptor status or estrogen sensitivity, and that its cellular targets include microtubules. In the present study, we investigated the apoptosis-inducing effects of A(II). MCF-7, MCF-7/LY2, and MDA-MB-231 cells all showed nuclear fragmentation in response to 100 microM A(II) when stained with Hoechst 33342 and examined by fluorescence microscopy. Pulsed field gel electrophoretic analysis showed that each of the cell lines also developed specific high molecular weight DNA fragments: a low level of 1-2 Mb fragments appeared after 6 hr, while 30-50 kb fragments accumulated subsequently. At 24 hr of drug exposure, the majority of cells became nonadherent, and the 30-50 kb fragments were restricted to detached MCF-7 and MDA-MB-231 cells. Both adherent and detached MCF-7/LY2 cells exhibited these fragments. A previous study by single-color (propidium) flow cytometry demonstrated that A(II) blocks MDA-MB-231 cells in G2/M of the cell cycle. More refined analyses in the present study showed this same result for MDA-MB-231 cells, but MCF-7 and MCF-7/LY2 cells did not reveal apparent drug-induced cell cycle block. A(II) demonstrated growth inhibitory, cell cycle-perturbing, and hypodiploidy-inducing activity against other human breast carcinoma lines, i.e. BT-20, CAMA-1, and SKBR-3, but no such actions in the non-tumorigenic, "normal" human breast epithelial line MCF-10A. Bromodeoxyuridine labeling and two-color flow cytometric analysis, however, suggested that A(II) caused stimulation into S phase, and that G2/M was the phase of the cell cycle from which cells apoptosed. A(II) caused cell rounding, detachment from the growth matrix, and nuclear shrinkage and fragmentation in parallel with biochemical changes. Cycloheximide inhibited A(II)-induced cell death, indicating that its toxicity requires de novo protein synthesis.     

NMR structure of the (+)-CPI-indole/d(GACTAATTGAC)-d(GTCAATTAGTC) covalent complex

We report the NMR solution structure of (+)-CPI-indole (CPI, 1,2,8,8a-tetrahydrocyclopropa[c]pyrrolo[3,2-e]indol-4(5H)-one), an agent belonging to the CC-1065/duocarmycin family of antitumor compounds. This (+)-CPI-indole structure is covalently bound to d(G(1)ACTAATTGTC(11))-d(G(12)TCAATTAGTC(22)), a synthetic DNA duplex containing a high-affinity binding site. The three-dimensional structure has been determined by several cycles of restrained molecular dynamics calculations with a total of 563 NMR-derived constraints, both in vacuo and by using the generalized Born solvent continuum model. In-depth analysis of the structure of this ligand-DNA complex led to a detailed knowledge of the bound state conformation of the CPI-indole, the most simplified agent related to CC-1065 and duocarmycins, the parent members of a family of extremely potent antitumor compounds. Comparison of the CPI-indole bound conformation with those previously found for (+)-duocarmycin SA (DSA), its unnatural enantiomer (-)-DSA, and the demethoxylated analogue (+)-DSI in their DNA complexes provided additional evidence of the tight correlation between the catalytic effect exerted by DNA on the alkylation reaction and the extent of angular twist between the two planar heteroaromatic subunits of these agents. Additionally, comparison of the structural features of the DNA-bound state of a "naked" ligand, such as CPI-indole, with those of various other duocarmycin agents provided useful information for the interpretation of the observed effects on chemical reactivity of the different substitution patterns at the hemispheres of these types of complex.     

ScaleSpaceViz: α-Scale spaces in practice

Kernels of the so-called α-scale space have the undesirable property of having no closed-form representation in the spatial domain, despite their simple closed-form expression in the Fourier domain. This obstructs spatial convolution or recursive implementation. For this reason an approximation of the 2D α-kernel in the spatial domain is presented using the well-known Gaussian kernel and the Poisson kernel. Experiments show good results, with maximum relative errors of less than 2.4%. The approximation has been successfully implemented in a program for visualizing α-scale spaces. Some examples of practical applications with scale space feature points using the proposed approximation are given. The text was submitted by the authors in English. Frans Kanters received his MSc degree in Electrical Engineering in 2002 from the Eindhoven University of Technology in the Netherlands. Currently he is working on his PhD at the Biomedical Imaging and Informatics group at the Eindhoven University of Technology. His PhD work is part of the “Deep Structure, Singularities, and Computer Vision (DSSCV)” project sponsored by the European Union. His research interests include scale space theory, image reconstruction, image processing algorithms, and hardware implementations thereof. Luc Florack received his MSc degree in theoretical physics in 1989 and his PhD degree cum laude in 1993 with a thesis on image structure, both from Utrecht University, the Netherlands. During the period from 1994 to 1995, he was an ERCIM/HCM research fellow at INRIA Sophia-Antipolis, France, and IN-ESC Aveiro, Portugal. In 1996 he was an assistant research professor at DIKU, Copenhagen, Denmark, on a grant from the Danish Research Council. From 1997 to June 2001, he was an assistant research professor at Utrecht University in the Department of Mathematics and Computer Science. Since June 1, 2001, he has been working as an assistant professor and, then, as an associate professor at Eindhoven University of Technology, Department of Biomedical Engineering. His interest includes all multiscale structural aspects of signals, images, and movies and their applications to imaging and vision. Remco Duits received his MSc degree (cum laude) in Mathematics in 2001 from the Eindhoven University of Technology, the Netherlands. Today he is a PhD student at the Department of Biomedical Engineering at the Eindhoven University of Technology on the subject of multiscale perceptual organization. His interest subtends functional analysis, group theory, partial differential equations, multiscale representations and their applications to biomedical imaging and vision, perceptual grouping. Currently, he is finishing his thesis “Perceptual Organization in Image Analysis (A Mathematical Approach Based on Scale, Orientation and Curvature).” During his PhD work, several of his submissions at conferences were chosen as selected or best papers—in particular, at the PRIA 2004 conference on pattern recognition and image analysis in St. Petersburg, where he received a best paper award (second place) for his work on invertible orientation scores. Bram Platel received his Masters Degree cum laude in biomedical engineering from the Eindhoven University of Technology in 2002. His research interests include image matching, scale space theory, catastrophe theory, and image-describing graph constructions. Currently he is working on his PhD in the Biomedical Imaging and Informatics group at the Eindhoven University of Technology. Bart M. ter Haar Romany is full professor in Biomedical Image Analysis at the Department of Biomedical Engineering at Eindhoven University of Technology. He has been in this position since 2001. He received a MSc in Applied Physics from Delft University of Technology in 1978, and a PhD on neuromuscular nonlinearities from Utrecht University in 1983. After being the principal physicist of the Utrecht University Hospital Radiology Department, in 1989 he joined the department of Medical Imaging at Utrecht University as an associate professor. His interests are mathematical aspects of visual perception, in particular linear and non-linear scale-space theory, computer vision applications, and all aspects of medical imaging. He is author of numerous papers and book chapters on these issues; he edited a book on non-linear diffusion theory and is author of an interactive tutorial book on scale-space theory in computer vision. He has initiated a number of international collaborations on these subjects. He is an active teacher in international courses, a senior member of IEEE, and IEEE Chapter Tutorial Speaker. He is chairman of the Dutch Biophysical Society.