Information Technologies in Biomedicine: Volume 2

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Duro, F. Aalborg : River Publishers, Vaidotas Marozas. Bristol : IOP Publishing. ISSN London : Hindawi. Oxford : Pergamon-Elsevier. Oxford : Elsevier. Accuracy of digital implant impressions with intraoral scanners. Berlin : Quintessence Publishing Co. ISSN X. Berlin : Springer.

Proceedings 6th International Conference, ITIB’2018, Kamień Śląski, Poland, June 18–20, 2018

New York, NY : Elsevier. Warszawa : Polish Academy of Sciences. Heidelberg : Springer. London : BioMed Central. Oxford : Elsevier Science. Amsterdam : Elsevier Science. New York : Hindawi. Schenectady, NY : Adenine Press. Medical Publishers. Oxford : Butterworth-Heinemann Ltd. Douglas; Persson, Hans W. J; Svensson, O; Sornmo, L. Peralta, Elena. School of electrical and computer engineering. Berlin, Heidelberg, New York.

Services provided by the Institute include applied scientific research, experimental development and studies of technical feasibility in the following areas:.

Ewa Pietka, Jacek Kawa - Information Technologies in Biomedicine: Volume 2 - Free eBooks Download

Biomedical Engineering Institute KTU Biomedical Engineering Institute conducts research and experimental development in the areas of health monitoring, diagnostics, images, signals and medical information processing technologies. Research areas. Methods for bio signals processing Adaptive and model-based parallel registered biomedical signals processing and applications for monitoring and diagnostics. Physiological and biomechanical information sources and sensor investigation New medical and biomechanical information sources, intelligent sensors and transducers and their wireless networks for physiology, movement analysis and ultrasonic as well as nonlinear interactions tissue for diagnosis, development and research.

Research of medical image processing methods and algorithms Medical images processing based on models and parameterisation for increasing clinical value. The research was carried out using electromagnetic modelling based on the Finite Integration method and analysis was performed in terms of computing induced electric field distributions and induced electric voltage near to the pacemaker lead stimulation poles.

The main objective was to compare levels of induced voltage on the pacemaker lead stimulation poles for both technologies, depending on the antenna distance from human torso model. The numerical results have shown that closer proximity of antenna than the distance recommended by pacemaker manufacturers could not pose higher pacemaker interference risk.

Additionally, the results of estimated induced voltage have revealed that the pacemaker interference risk of recent mobile phone transmission technology is lower compared to older ones. Seismocardiography SCG is a non-invasive method of analyzing and recording cardiovascular vibrations on the chest wall. Mobile devices offer the possibility to monitor cardiac activity.

Accelerometers used in such analysis register gravitational offset because the effects of gravity on an object are indistinguishable from acceleration. Our aim is to investigate the influence of gravitational offset removal on heart beat detection from smartphone seismocardiograms. We registered SCG signals from two subjects male and female in supine position before and after enabling gravitational offset removal to analyze its influence on beat detection algorithm performance.

Our algorithm consists of signal preprocessing, calculating analytical envelope and RMS envelope and peak finding. The influence of gravitational offset on heart beat detection is insignificant due to band-pass filtration. Offset removal slightly increased PPV for male subject and sensitivity for female subject.

We observed beat detection quality improvement when using RMS envelope. The best performance was achieved using RMS envelope on signal from male subject. This study proves insignificant influence of gravitational offset on our heart beat detection algorithm. Evidence of the relationships between physiological data has been found in previous studies. However there is still limited knowledge about underlying mechanisms and patterns of the preterm birth and direction of propagating uterine contraction.

In this paper we study transfer entropy TE that is widely used to quantify interactions between biomedical time series. We are searching for indices that could detect preterm labor using contractions extracted from differentiated electrohysterographical EHG signals.

Transfer entropy was considered as a bivariate approach to quantify the bidirectional information flow from channel 1 to channel 4. The parameters used in this study help to estimate the potential of premature labor as it progresses. Therefore, they may be useful as early risk markers of preterm birth. Manual reconfiguration of a distributed real-time communication system by humans is error-prone and time consuming. To avoid errors, which may result in latter failures of the system, the reconfiguration of the communication resources should be carried out automatically by the system itself.

This paper introduces a new broker model which enables the system to respond dynamically to changes in the system composition, like newly added network nodes or services. Further, we introduce a new client model that implements protocols for the agreement and coordination of the reconfiguration phases of the network nodes. Our introduced models are finally implemented and evaluated within an experimental setup for TTEthernet. This paper presents the idea of brute force feature extraction for Electrocardiography ECG signals applied to discomfort detection.

ECG and subjective discomfort was recorded. With this experiment, we are able to show that a brute force ECG feature sets achieved better discomfort detection than traditional HRV based ECG feature set; b cepstral and spectral flux based features appear to be the most promising to capture HRV phenomena. However, their performance relies on the network architecture, quality and depth of training. Here we introduce a set of image tiles of colon biopsies from 2 subjects with inflammatory bowel disease IBD annotated for glandular epithelium EP , gland lumen together with goblet cells LG , and stroma ST.

For comparison, we used the U-Net trained de-novo. Ultimately, each model was validated in an independent digital biopsy slide. We also determined how the number of images used for training affects the performance of the model and observed a plateau in trainability at images. In the independent biopsy slide, U-Net demonstrated a segmentation accuracy of The performance of the FT-FCN-8s was slightly worse, but the model required fewer images to reach a high classification performance.

Our data demonstrate that all 3 FCNs are appropriate for segmentation of glands in biopsies from patients with IBD and open the door for quantification of IBD associated pathologies. It also negatively impacts the performance of digital image analysis algorithms, including nuclei segmentation that is deemed to be affected the most. The color-transferred images were then processed by two proposed approaches that subtract and subsequently threshold red and blue color channels.

Implementation of these steps improved the amount of false positive pixels and splitting of clustered nuclei in the nuclear mask generated by the baseline method. The performance was assessed in heterogeneous images of colon with manually delineated nuclei. Computer-assisted image analysis cytology play an important function in modern cancer diagnostics.

A crucial task of such systems is segmentation of cell nuclei. Automatic procedure have to locate their exact position in cytological preparation and determine precise edges in order to extract morphometric features. Unfortunately, segmentation of individual nuclei is a huge challenge because they often creates complex clusters without clear edges.

To deal with this problem we are proposing to combine Bayesian object recognition approach to approximate nuclei by circles with marker-controlled watershed employed to determine their exact shape. Watershed segmentation can reconstruct a precise shape of nuclei but only if their approximate location is known. On the other hand, Bayesian object recognition approach allows to isolate single nuclei even in complex nuclei structures but without determining their exact shape.

Thus, we used Bayesian object recognition to generate markers required to form a topographic map for a watershed method. The effectiveness of the proposed approach was examined using artificially generated images and real cytological images of breast cancer. Tests carried out have shown that the proposed version of the marked-controlled watershed can be used with success to segment elliptic-shaped objects. Nowadays we use multiple molecular typing methods capable of distinguishing bacteria strains.

However, a majority of these methods are time-consuming, and associated costs are not negligible. Also, reproducibility of typing results is questionable. Therefore, we propose new typing methodology based on bioinformatics. In this paper, we present an algorithm that is at the core of the new method. It consists of multiple steps, such as measuring the quality of input data, identification of genes having high diversity and analysis of results employing clustering and phylogenetic trees.

Obtained results are then compared with results from the mini-MLST method. In the article, the authors describe the use of reverse engineering technology 3D scanners and image analysis methods that allow a very accurate reflection of the curvature of the foot, making it possible to choose an orthopedic insert that provides correct support points and proper distribution of loads in the foot, thereby improving the axis of the entire lower limb. The experiment was carried out on 4 patients, which underwent the following tests: computer suboscopy, X-ray performed on 3 patients and a 3D footprint scan process for each patient.

For each case, a spatial model was generated, reflecting the foot geometry, which became a key element in determining the therapy and conservative treatment of lower limb defects flatfoot, calcaneal spur, hallux valgus, calluses. This paper presents a new 3D model of the skeletal gastrocnemius muscle. As part of the experiment, an application was developed to generate a 3D muscle.

Comparison of real volumes with volumes of the virtual muscles during deformations showed the same results. In this paper detailed analysis of the Hovorka model has been provided. The model describes the dynamics of glucose concentration in case of patients with type 1 diabetes mellitus. The Hovorka model is widely used as a virtual environment and also as a part of controller so-called an internal model. Due to the popularity of the Hovorka model, its detailed analysis can be helpful in choosing the control algorithm or in simplifying the implementation. The aim was to assess how changes from their base value will affect the glucose output.

Results for 3 parameters of the model: rate of an insulin elimination from a patient plasma, endogenous glucose production and total glucose fluctuations independent of insulin were compared. Another purpose of the research was to assess the model nonlinearity intensity. The study was performed on 6 patients who represent the virtual population of type 1 diabetic patients. The performed analysis indicated that an insulin-glucose system described by the Hovorka model was weakly nonlinear. The values of the nonlinear coefficient were inter-patients varied and depended on an insulin dose.

These values ranged: 0. The measured glucose concentration became sensitive to all studied parameters of the Hovorka model. The most sensibilized parameter were glucose fluctuations independent of insulin. These results of the analysis may be used to develop new control algorithms based on the internal patient model. They will be able to adapt their parameters to the individual patient by updating specific value in each step of the algorithms. This paper examines practical constraints and problems related to modelling of the human cornea during intraocular pressure measurement using Corvis ST.

It highlights the essential role of corneal deformation image processing and analysis in the field of numerical modelling. By combining these two disciplines: biomechanics, which deals with modelling the behaviour of biological structures, and image processing methods, it is possible to verify and compare the values obtained for the tested models in numerical experiments with those obtained through image analysis. One of the important steps of analysis of any mathematical model is the sensitivity analysis.

It is routinely used but it can be applied only for constant parameters.

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It cannot be applied for non-stationary parameters nor for varying in time external input signals. This work describes a toolbox written in MATLAB environment, which can be useful in sensitivity analysis of biomedical models described by system of ordinary differential equations. To illustrate this type of sensitivity analysis, we use the created tool to analyze a model of cell signaling pathway of p53 protein, which plays crucial role in the response of tumor and healthy cells to radiotherapy.

The paper presents the classification of text documents presenting radiology examinations, taking into consideration two groups: cases with aneurysms and those without it. A database containing descriptions of cases was classified using the maximum entropy algorithm and frequent phrase extraction. It was revealed that the best method was the classifier using the maximum entropy algorithm based on nouns.

The worse diagnostic capacity demonstrates frequent phrase extraction algorithm. The other classifiers turned out to be less effective, than the random ones. Clustering large amounts of unstructured data is an important challenge in contemporary medicine and biology. This article presents an automatic clustering method for unstructured medical data.

Information Technologies in Biomedicine: Volume 2 (Advances in Intelligent and Soft Computing, 69)

The presented method consists of the following main steps: transformation of the document corpus to a frequency matrix of terms; dimensionality reduction of the frequency matrix of terms using principal component analysis PCA ; the direct comparison of pairs of documents similarity measures using the cosine and correlation distances; and finding the optimal number of groups for expertly labelled data sets by treating the clustering problem as an optimization problem in which the objective function is an F measure to be optimized via the selection of parameter values such as PCA resolution and the similarity threshold of the pairs of documents.

The usefulness of the proposed methodology was demonstrated by performing calculations on three data sets: short sentences divided into three themes, radiological reports of aneurysms, and radiological reports of abdomen studies. A common barrier in clustering unstructured data is difficulty in results interpretation. To overcome this limitation, the utility of presentation methods, including group histograms, similarity matrices, plots of document assignment to founding clusters, F-measure interpolation and alphabetical- and term-frequency dictionaries, are presented.

Excluding the labelling step, the presented method is completely automated and can be used as a preliminary data analysis method for large bodies of text to discover potential groups of interesting topics. The first aim of the paper is to investigate whether the very promising method of data analysis the Big Data Analysis, further called BDA — used now across various domains — can serve for information technologies in medicine, in the area of planning pathways of organic syntheses. The eventual usage of BDA in the field stated is meant within a speculation — synthesis of new drugs. Therefore characteristic features of BDA are here briefly discussed; they are commonly marked by five letters V, coming from the following concepts: Volume, Velocity, Variety, Veracity, Value.

In the research performed it was found that BDA unfortunately seems to have rather restricted applicability in planning of chemical compounds; rather we have to focus in details of a single, chosen reaction. Therefore in a second part of the paper — in its core — the Achmatowicz reaction is selected to expose the required ingenuity in organic synthesis. As a pattern for discussion, the structure of Bao Gong Tung A molecule, a novel natural product showing strong antiglaucoma properties, was fixed.

The paper presents the different aspects of analyzing public speeches of deputies in the Parliament Sejm of the Republic of Poland with use of SAS tools for text analytics. A document repository was created based on publicly available transcriptions of speeches for 7th from Nov to Nov and 8th from Nov to Jan term of the Parliament Sejm. A database contains pdf files with transcriptions of the full-day parliament session.

This repository was cleaned and preprocessed, every file was split into a set of personal speeches. As a result, the source data table contains records. Due to global digitalisation, teaching in virtual reality is becoming a growing market. Compared to learning in class, individual learning scenarios are possible. To find out, if a person is currently stressed or overstrained and the training course thus should be adapted, it is necessary to detect the emotional state of the person.

Therefore in this paper a sensor headband is introduced, which is able to measure certain physiological values such as galvanic skin conductance, blood volume pulse or body temperature. With the help of feature extraction it is then possible to determine, which emotional state relevant in learning scenarios is predominate.

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In this paper a convolutional neural network, CNN, is trained to perform mental task recognition on the basis of the EEG signal. We address the problem of EEG data representation and processing, comparing two different approaches to the construction of the convolutional layers of the CNN. We demonstrate that splitting the input EEG data into individual channels and frequency bands is beneficial in terms of the generalization error, although the training process is faster and more stable if complete, unsplit two-dimensional spectrograms of the EEG signal are processed.

Several approaches to automate its assessment are available, yet they require digitizers or other electronic devices. However, archived data from the past 60 years are stored only on paper. This pilot study concerns the extraction of the features from the paper version of AST using image processing algorithms.

In this study, AST data from twenty two cases with progressive supranuclear palsy are processed. Each case contains reference data a pattern sequence drawn by the examiner and diagnostic data continuation of the pattern drawn by the patient. Spatial-based features as well as novel erosion bar charts EBC are extracted from the initial characters of the examiner and patient sequence. The results are evaluated to find parameters that are statistically different between the groups. The results indicate that patients typically write larger characters than examiners, yet their pen-paper pressure is smaller.

In this paper, an automatic, offline evaluation method of the Degree Turn Test based on a video recording is presented. The method is evaluated using 30 samples registered in a group of 13 residents of a Social Assistance Center resulting in a 2. The results are promising in terms of the automatic assessment of the balance. The paper presents the use of dynamic thermography in an assessment of quadriceps activity during a static load in healthy persons and patients with spastic quadriplegia.

The differential thermograms relating to the muscle localisation were quantitatively described by the mean and standard deviation of pixels thermal values belonging to the ROI. The obtained trends in temperature distribution in healthy persons correspond to blood flow changes observed by other authors.

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These trends were opposite to the trend received for the first time in a spastic person. These results suggest that a dynamic thermography may have a potential application in the assessment of spasticity intensity. The research aimed to determine the standards of kinematic quantities and indices of regularity, namely the Gillette Gait Index and the Gait Deviation Index related to children aged between 7 and 17 as well as the identification of differences concerning the above-named quantities according to age and height.

The study group consisted of 56 healthy children aged 7 to The tests were performed using the BTS Smart system.

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Detailed analysis involved a set of sixteen variables describing gait kinematics; the above-named variables are used when creating the GGI. The children were divided into five groups in relation to age and six groups in relation to height. Sixteen parameters composing the GGI were determined for all of the children constituting the test group.

The confirmation or the exclusion of differences between age-related and height-related groups was based on statistical analysis. The identified courses of the kinematic time series can be used as normative in relation to patients aged 7— The aim of the work was to assess locomotion functions and posture stability of patients who had undergone prenatal or postnatal surgery for myelomeningocele.

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The gait tests were conducted with BTS System and the stabilographic tests with the dynamographic platform by Zebris. Eight patients operated for myelomeningocele, 4 prenatally and 4 postnatally, have undergone the analysis. A thorough analysis was made of the set of space-time, kinematic parameters, values of the Gillette Gait Index and Gait Deviation Index.

The obtained results were compared with the standard values. The conducted biomechanical examinations of indicate that regardless of the time when the closing of the myelomeningocele was done, the patients show biggest disorders in the motion of the pelvis. Posture stability of children with myelomeningocele is reduced when compared to a group of healthy children. Erweiterte Suche. Springer Professional. Inhaltsverzeichnis Frontmatter. Krzysztof J.

Marcin D. Bugdol, Maria J.