Examples of interacting cell systems are life cycles of bacteria or social amoebae, embryonic tissue formation, wound healing or tumour growth and metastasis. What are the principles underlying the dynamics of interacting cell systems? Mathematical models of spatio-temporal pattern formation can offer insight into the principles of cooperative phenomena which can not be explained at the single cell level but are emergent properties of interacting cell systems. Typical modelling attempts focus on a macroscopic perspective (formulated for example as partial differential eqution system), i.e. such models describe the spatio-temporal dynamics of cell concentrations (see for example [8]. A modelling alternative are cell-based models, e.g. cellular automata, in which the fate of each individual cell can be tracked which is not possible in continuous models. Here, we briefly describe cellular automaton model applications for bacterial pattern formation and avascular tumour growth.

Up to the end of the 70’s, Medical Imaging was mainly related to the study of planar data sets resulting from direct physical acquisitions (e.g. X-Ray radiographs). Then, the development of inverse methods associated with the increasing power of computers enabled the visualization and the analysis of human being cross-section images (e.g. CT scans, MRI): these images are the result of mathematical processes and do not present direct physical acquisitions. The visualization of these data in three-dimensional space was made possible by the use of a set of parallel cross-sections: the result was spectacular but not sufficient for further development, especially in the case of clinical applications. Such applications need the characterization of a geometrical model, e.g. for the capture of sophisticated geometrical parameters or to provide a mathematical support to mechanical simulations.

The expectation that computer networks combined with the Internet and associated technologies will be common in all businesses of the future has resulted in a flurry of investment in ICT around the globe, and in wide interest in the Internet, e-commerce and e-business. For example the Malaysian government has created a high-speed data highway, a federal capital that is a ‘wired city’, and a ‘paperless hospital’ [1]. The UK Government too has recently approved plans for major ICT projects [5, 10]. These projects are in healthcare, and involve large financial sums. It is important that the use of these public funds leads to successful outcomes. This paper analyses a large successful ICT project that uses e-business technologies. The aim of the paper is to identify the success factors in this completed project to see if these pointers indicate favourable prospects for the outcome of these new healthcare projects.

Proteasomes are enzymes which perform an essential step in degrading proteins in eukaryotic cells. In mammals they play an important role in MHC I ligand generation and thus in the regulation of specific immune responses. The cleavages or cuts made by proteasomes on typical protein substrates are not uniquely determined by adjacent amino acids in the substrate nor do they follow simple statistical rules. There are several approaches to understanding the cleaving patterns either by statistical analysis or by designing proteasome models in the form of stochastic networks. Here the latter approach will be presented. A network simulating a proteasome molecule or rather a family of such molecules has been trained on experimental data in order to extract cleaving rules. The training uses experimentally meaningful goal functions and a stochastic hill-climbing process. The network can reproduce experimentally observed cleaving patterns and also to some extent predict such patterns. The eventually obtained affinity parameters of the optimized model correspond well with experimentally determined cleavage motifs. The model can be adapted to existing types of experimentally observed proteasomes such as defect mutants or interferon-inducible proteasomes.

Renal replacement therapy involves the control of body pools of water and electrolytes, and removal of small metabolites (urea, creatinine). The correct estimation of "the dose of therapy" and optimisation of the procedure needs quantification of fluid and solute transport during dialysis as well as evaluation of the distribution and exchange of water and solutes within the body. Mathematical models can combine the general physiological knowledge with information about individual patients yielded by clinical measurements. Many of these models (urea model, sodium model, models of peritoneal transport) have been presented to the community of clinical nephrologists in the form of computer programs often supplemented with on-line measuring devices. However, the debate about their meaning and the search for better methods of their application are still vivid.

In this article we discuss mathematical modelling of vaccination programs for rubella in the UK. We briefly discuss rubella before outlining the underlying mathematical model. Age-structured serological data is used to estimate the force of infection in the absence of vaccination and hence the mixing matrix. Homogeneous, proportional and symmetric mixing are considered. The estimated mixing matrix is used to evaluate the basic reproduction number R0 and minimum elimination vaccination programs using one stage and two stage vaccination strategies.

The paper is concerned with development of optimal treatment protocols that take into account both action of several drugs and the evolution of drug resistance. It is a result of analysis of evolution of drug resistance in cancer population but presented methodology can be applied in any case involving drug resistance stemming from gene amplification. First, a biological background is given. In subsequent sections of the paper, the developed technique is presented and some early analytical results, which form a basis for more precise modeling, are shown. Afterwards, the model description is transformed into a vector integro-differential equation, which makes it possible to define necessary conditions of optimal solution to the minimization problem arising from the search for the optimal treatment. Finally, some remarks on the model applicability are presented.

This paper focuses on three-dimensional (3-D) adaptive median filters based on the impulse detection approach designed to effectively remove the impulse noise from cardiographic image sequences. Impulse noise affects the useful information in the form of bit errors and it introduces to the image high frequency changes that prohibit to process and to evaluate the heart dynamics correctly. Therefore biomedical imaging such as vascular imaging and quantification of heart dynamics is closely related to digital filtering. In order to suppress impulse noise effectively, well-known non-linear filters based on the robust order-statistic theory provide interesting results. Although median filters have excellent impulse noise attenuation characteristics, their performance is often accompanied by undesired processing of noise-free samples resulting in edge blurring. The reason is that median filters do not satisfy the superposition property and thus the optimal filtering situation where only noisy samples are affected can never be fully obtained. The presented adaptive impulse detection based median filters, can achieve the excellent balance between the noise suppression and the signal-detail preservation. In this paper, the performance of the proposed approaches is successfully tested for the heart image sequence of 38 frames and the wide range of noise corruption intensity. The results are evaluated in terms of mean absolute error, mean square error and cross correlation.

Recently, data on multiple gene expression at sequential time points were analyzed, using Singular Value Decomposition (SVD) as a means to capture dominant trends, called characteristic modes, followed by fitting of a linear discrete-time dynamical model in which the expression values at a given time point are linear combinations of the values at a previous time point. We attempt to address several aspects of the method. To obtain the model we formulate a nonlinear optimization problem and present how to solve it numerically using standard MATLAB procedures. We use publicly available data to test the approach. Then, we investigate the sensitivity of the method to missing measurements and its possibilities to reconstruct missing data. Summarizing we point out that approximation of multiple gene expression data preceded by SVD provides some insight into the dynamics but may also lead to unexpected difficulties.

A new filtering approach designed to eliminate impulsive noise in color images, while preserving fine image details is presented. The computational complexity of the new filter is significantly lower than that of the Central Weighted Vector Median Filter (CWVMF). The comparison shows that the new filter outperforms the CWVMF, as well as other standard procedures used in color image filtering for the removal of impulsive noise.

In this paper a novel approach to the problem of edge preserving smoothing is proposed and evaluated. The new algorithm is based on the combined forward and backward anisotropic diffusion with incorporated time dependent cooling process. This method is able to efficiently remove image noise, while preserving and enhancing its edges.

The general problem of computer-based clinical decision support systems is considered. A class of rules in medical databases characteristic for therapeutic decisions has been distinguished. The necessity of application of a complete set of data for learning computerised systems of decision support has been pointed out. Rough set approach is applied to the analysis of the problem. An illustrative example of application of the presented results in the treatment of endometrial and breast cancer is given.

Firstly the idea of objective psychological tests and their characteristics related to various features of human psychophysiology are introduced. Examples of objective tests are given. Next application of data mining algorithms to analyse data obtained from different tests are outlined. The general concept of a computer system for objective psychological studies of psychomotoric processes in humans is then described. Finally the possibility of implementation of the system in medical and psychoacoustical studies is pointed out.

The algorithms for examination of the density and chosen parameters of movement of sperm cell were elaborated and implemented. The conducted research is a part of a work on a computer system for analysis of semen. The system will allow for an increase of the preciseness of examination thanks to the exact specification of numerical values of chosen parameters. Additional advantage of the system is a shorter time of the examination. Nowadays, the basic type of examination is an estimated analysis of parameters of semen done by visual observation of a sample. This type of examination is based on a subjective assessment of an image by a physician. Moreover the registration of images in visual analysis is not possible.

An objective of the work is to demonstrate some difficulties with construction of a classifier based on the k-NN rule. The standard k-NN classifier and the parallel k-NN classifier have been chosen as the two most powerful approaches. This kind of classifiers has been applied to automatic recognition of diaphragm paralysis degree. The classifier construction consists in determination of the number of nearest neighbors, selection of features and estimation of the classification quality. Three classes of muscle pathology, including the control class, and five ventilatory parameters are taken into account. The data concern a model of the diaphragm pathology in a cat. The animals were forced to breathe in three different experimental situations: air, hypercapnic and hypoxic conditions. A separate classifier is constructed for each kind of the mentioned situations. The calculation of the misclassification rate is based on the leave one out and on the testing set method. Several computational experiments are suggested for the correct feature selection, the classifier type choice and the misclassification probability estimation.

This paper shows the current state of research and development over the telemedical services system for family doctor’s practices. The introduction of this telemedical system should result in improvement in accessibility to medical services for the patient and to actual medical information, publications and up-to-date consultations for the physician, regardless of the distance from physician’s clinic to the consultation center. This system should reduce the total costs of basic medical services. The system consists of two autonomous subsystems, that work in two arrangements: patient ï‚« family doctor’s practice and family doctor’s practice ï‚« information & consultation center. The remote medical care system provides vital signs monitoring through telephone line - physician can observe electrocardiogram and oxidation of blood, while the patient is many kilometres away. In case of unstable state of health, this system is able to alert the physician. The teleconsultation system provides a videoconference service with shared multimedia document (audio-video streams, graphic, text, etc.).

It is commonly suggested that (i) Information and Communication Technology (ICT) is essential for the delivery of quality healthcare [17], and (ii) the role of doctors is crucial in the implementation of ICT-based systems in hospitals and in general practice. Based on these two premises, this paper argues for a medical informatics course for medical students. The paper outlines the content of a one-semester elective in medical informatics that was presented over eight academic years. The course pre-dates the recommendations of the International Medical Informatics Association, IMIA [7]. Nevertheless, it is compatible with the recommendations of IMIA and also with the earlier findings of the EDUCTRA study [5]. The principles formulated in this paper are presented in order to help other educationalists (i) to obtain insights and (ii) thence to assist them to engineer medical informatics changes into their medical school environments. Each time that the course was presented, participants were asked for their evaluation of the course [9]. These views were favourable. It was thought that a follow-up survey of these same respondents - now practising doctors, some of whom took the course 10 years ago - might give further insights into the relevance and value of this course. The views of these practising doctors are presented within the paper.

The multimedia applications are usually developed within various graphical interfaces. The application user expects to use rich formats for graphics, animation and various audio effects. Many efforts have already been done on this field. Anyhow new challenge one can observe that concerns the Internet applications [1], [2]. The paper shows the platform unifying the multimedia databases development, available on Wide Area Network servers.

Many works has recently been done within presentation databases attributes development. The multimedia applications use various graphical interfaces and rich formats of graphics, animation and various audio effects. Although many efforts have already been done on this field new challenge one can observe that provide the Internet application [1], [2]. The paper shows the database organisation for multimedia applications available on web servers.

This study is based on clinical observations of patient motor-disturbances, measured by PSW (Parotec System for Windows) pedobarographic recorder [1]. The gait regular asymmetry in a data spectrum has been noticed as an independent factor from disease duration and severity. In majority of analysed cases a gravity centre of the body moves towards a left limb, into a heel region. A trajectory of foot gravity centre elongation, its irregularity, a floor-contact time and impulse values increase is also visible. Predominantly on more affected limb. These diagnosis factors allow concluding that the PSW recorder could successfully be used for recognition and quantification of the motor disturbances, illustrating the Parkinson disease progress.

Various options available in PSW footprint and walking characteristics measuring equipment [6], [7], give the user many aims in putting diagnosis. A Conclusion-Making Unit (CMU) that has been described in this paper supports the diagnosis automation procedures. Due to simplifying the CMU training process some affords in a field of the input record length reduction have been undertaken. The paper describes an analytical method of the data record description that allows converting discrete data samples into continuous function. This way a re-digitalisation of the record can be done, where sampling period is matched with the walk length. This normaliza-tion allows reducing the data record length used for fast training of the CMU.

The paper describes an analytical method of data record description that allows converting samples of discrete data record into continuous function. This operation allows re-sampling the data record with a sampling rate that is adequate to step duration. The record length is limited to an efficient size for training the Conclusion-Making Unit (CMU). Various options available in the PSW equipment [6], [7] give the user many aims in putting diagnosis anyhow, due to simplification of the CMU training process several me¬thods for data records modifications are considered.

The aim of the proposed watershed based image segmentation technique is to split images into spatially homogeneous regions, which can be further processed by different image analysis tools. The advantage of such approach, in comparison to pixel oriented processing, is its lower sensitivity to superimposed noise due to averaging of regions properties over their area. The watershed segmentation technique is based on interpretation of an image as a topographic relief and on simulation of flow of water along steepest descent paths called downstreams. Thus, for each local minimum of the image, a drainage region is defined, which, if computed for a gradient image, represents an area with approximately constant properties. The segmentation technique is further extended for multi-scale image analysis by means of Gaussian smoothing. The aim of smoothing is to suppress image details that are smaller than standard deviation of the Gaussian. However, smoothing results not only in the desired increase of region size, but it also affects position of region boundaries, at least for larger standard deviations of the Gaussian filter. Therefore a new technique is proposed, based on region hierarchies, which enables to transfer region contours with precise position from the levels with low smoothing to levels with higher smoothing. Thus, segmentation of an image into large regions, but with exact contours, is obtained.

Quality assessment in external beam radiotherapy necessitates for an efficient and robust tool for comparing the planned and realised geometry of the treatment. Such a tool using the modified Hausdorff distance measure has been developed and successfully introduced to clinical practice. The majority of steps of the method are automatic. The user specifies the share of edge data extracted from the images to be used in calculations, not before, but after these calculations are actually performed. Thus, the results of the choice can be seen immediately. This mechanism makes the method extremely robust against partially erroneous or missing data.

In this paper we introduce a new method for non-rigid voxel-based registration of medical images. There exist many applications where an alignment between two image datasets has to be established. Often a registration of a time-shifted medical image sequence with appearing deformation of soft tissue (e.g. pre- and intraoperative data) has to be conducted. Soft tissue deformations are usually highly non-linear. In our approach, for the handling of this phenomenon and for obtaining an optimal non-linear alignment of respective datasets we transform one of them using 3D Bézier functions, which provides some inherent smoothness as well as elasticity. In order to find the optimal transformation, many evaluations of this Bézier function are necessary. In order to make the method more efficient, graphics hardware is extensively used. We applied our non-rigid algorithm successfully to MR brain images in several clinical cases and showed its value.

This paper presents a method for computer assisted selection of optimal donor sites for autologous osseous grafts in the craniofacial surgery. At the initial graft design stage the surgeon defines in the CT data set the shape of the bone segment to be reconstructed and in the donor region CT data set a set of constraints for the optimization task. This non-automatic step is followed by a fully automatic optimization stage, which delivers a set of sub-optimal and optimal donor sites for a given template. Such approach permits the surgeon to find the best site for harvesting the graft and enables an exact anatomical reconstruction of the osseous section.

The aim of this paper is to propose a new quality index which measures the distance between a reference (source) image and its corrupted copy in the way as Human Visual System (HVS) does. The new quality index called the Mean Weighted Quality Index (MW) is defined with the help of the well known easy calculated indexes. The experiments performed on a number of medical images confirmed usefulness of the new index.

In the paper we present some results concerning application of simple shape parameters that are successfully used to distinguish between normal and pathological blood cells. All descriptors are based on shape area and its perimeter. We use five parameters that are automatically calculated for objects pointed out in the analyzed image with the help of our software. The experiments performed on the same set of tested images as in [3] let us draw the same conclusions as those reported in [3] where fractal analysis to shape has been used.

Registration is one of the essential medical image processing techniques. The goal is to find a geometric transformation, that relates corresponding voxels in two different 3D images of the same object. The publication presents a registration technique based on maximization of mutual information.