Performance and Robustness of Regional Image Segmentation Driven by Selected Evolutionary and Genetic Algorithms: Study on MR Articular Cartilage Images.

The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of these methods is prone to the image noise and artefacts. In this context, regional segmentation strategies, driven by either genetic algorithms or selected evolutionary computing strategies, have the potential to overcome these traditional methods such as Otsu thresholding or K-means in the context of their performance. These optimization strategies consecutively generate a pyramid of a possible set of histogram thresholds, of which the quality is evaluated by using the fitness function based on Kapur's entropy maximization to find the most optimal combination of thresholds for articular cartilage segmentation. On the other hand, such optimization strategies are often computationally demanding, which is a limitation of using such methods for a stack of MR images. In this study, we publish a comprehensive analysis of the optimization methods based on fuzzy soft segmentation, driven by artificial bee colony (ABC), particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO), and a genetic algorithm for an optimal thresholding selection against the routine segmentations Otsu and K-means for analysis and the features extraction of articular cartilage from MR images. This study objectively analyzes the performance of the segmentation strategies upon variable noise with dynamic intensities to report a segmentation's robustness in various image conditions for a various number of segmentation classes (4, 7, and 10), cartilage features (area, perimeter, and skeleton) extraction preciseness against the routine segmentation strategies, and lastly the computing time, which represents an important factor of segmentation performance. We use the same settings on individual optimization strategies: 100 iterations and 50 population. This study suggests that the combination of fuzzy thresholding with an ABC algorithm gives the best performance in the comparison with other methods as from the view of the segmentation influence of additive dynamic noise influence, also for cartilage features extraction. On the other hand, using genetic algorithms for cartilage segmentation in some cases does not give a good performance. In most cases, the analyzed optimization strategies significantly overcome the routine segmentation methods except for the computing time, which is normally lower for the routine algorithms. We also publish statistical tests of significance, showing differences in the performance of individual optimization strategies against Otsu and K-means method. Lastly, as a part of this study, we publish a software environment, integrating all the methods from this study.

QTY code enables design of detergent-free chemokine receptors that retain ligand-binding activities.

Structure and function studies of membrane proteins, particularly G protein-coupled receptors and multipass transmembrane proteins, require detergents. We have devised a simple tool, the QTY code (glutamine, threonine, and tyrosine), for designing hydrophobic domains to become water soluble without detergents. Here we report using the QTY code to systematically replace the hydrophobic amino acids leucine, valine, isoleucine, and phenylalanine in the seven transmembrane α-helices of CCR5, CXCR4, CCR10, and CXCR7. We show that QTY code-designed chemokine receptor variants retain their thermostabilities, α-helical structures, and ligand-binding activities in buffer and 50% human serum. CCR5QTY, CXCR4QTY, and CXCR7QTY also bind to HIV coat protein gp41-120. Despite substantial transmembrane domain changes, the detergent-free QTY variants maintain stable structures and retain their ligand-binding activities. We believe the QTY code will be useful for designing water-soluble variants of membrane proteins and other water-insoluble aggregated proteins.

Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing.

There are various modern systems for the measurement and consequent acquisition of valuable patient's records in the form of medical signals and images, which are supposed to be processed to provide significant information about the state of biological tissues [...].

Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System.

In the area of musculoskeletal MR images analysis, the image denoising plays an important role in enhancing the spatial image area for further processing. Recent studies have shown that non-local means (NLM) methods appear to be more effective and robust when compared with conventional local statistical filters, including median or average filters, when Rician noise is presented. A significant limitation of NLM is the fact that thy have the tendency to suppress tiny objects, which may represent clinically important information. For this reason, we provide an extensive quantitative and objective analysis of a novel NLM algorithm, taking advantage of pixel and patch similarity information with the optimization procedure for optimal filter parameters selection to demonstrate a higher robustness and effectivity, when comparing with NLM and conventional local means methods, including average and median filters. We provide extensive testing on variable noise generators with dynamical noise intensity to objectively demonstrate the robustness of the method in a noisy environment, which simulates relevant, variable and real conditions. This work also objectively evaluates the potential and benefits of the application of NLM filters in contrast to conventional local-mean filters. The final part of the analysis is focused on the segmentation performance when an NLM filter is applied. This analysis demonstrates a better performance of tissue identification with the application of smoothing procedure under worsening image conditions.

Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things.

The operating cost minimization of smart homes can be achieved with the optimization of the management of the building's technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.

Analysis and Testing of a Suitable Compatible Electrode's Material for Continuous Measurement of Glucose Concentration.

The subject of the submitted work is the proposal of electrodes for the continual measurement of the glucose concentration for the purpose of specifying further hemodynamic parameters. The proposal includes the design of the electronic measuring system, the construction of the electrodes themselves and the functionality of the entire system, verified experimentally using various electrode materials. The proposed circuit works on the basis of micro-ammeter measuring the size of the flowing electric current and the electrochemical measurement method is used for specifying the glucose concentration. The electrode system is comprised of two electrodes embedded in a silicon tube. The solution consists of the measurement with three types of materials, which are verified by using three solutions with a precisely given concentration of glucose in the form of a mixed solution and enzyme glucose oxidase. For the testing of the proposed circuit and the selection of a suitable material, the testing did not take place on measurements in whole blood. For the construction of the electrodes, the three most frequently used materials for the construction of electrodes used in clinical practice for sensing biopotentials, specifically the materials Ag/AgCl, Cu and Au, were used. The performed experiments showed that the material Ag/AgCl, which had the greatest sensitivity for the measurement even without the enzyme, was the most suitable material for the electrode. This conclusion is supported by the performed statistical analysis. On the basis of the testing, we can come to the conclusion that even if the Ag/AgCl electrode appears to be the most suitable, showing high stability, gold-plated electrodes showed stability throughout the measurement similarly to Ag/AgCl electrodes, but did not achieve the same qualities in sensitivity and readability of the measured results.

Towards to Optimal Wavelet Denoising Scheme-A Novel Spatial and Volumetric Mapping of Wavelet-Based Biomedical Data Smoothing.

Wavelet transformation is one of the most frequent procedures for data denoising, smoothing, decomposition, features extraction, and further related tasks. In order to perform such tasks, we need to select appropriate wavelet settings, including particular wavelet, decomposition level and other parameters, which form the wavelet transformation outputs. Selection of such parameters is a challenging area due to absence of versatile recommendation tools for suitable wavelet settings. In this paper, we propose a versatile recommendation system for prediction of suitable wavelet selection for data smoothing. The proposed system is aimed to generate spatial response matrix for selected wavelets and the decomposition levels. Such response enables the mapping of selected evaluation parameters, determining the efficacy of wavelet settings. The proposed system also enables tracking the dynamical noise influence in the context of Wavelet efficacy by using volumetric response. We provide testing on computed tomography (CT) and magnetic resonance (MR) image data and EMG signals mostly of musculoskeletal system to objectivise system usability for clinical data processing. The experimental testing is done by using evaluation parameters such is MSE (Mean Squared Error), ED (Euclidean distance) and Corr (Correlation index). We also provide the statistical analysis of the results based on Mann-Whitney test, which points out on statistically significant differences for individual Wavelets for the data corrupted with Salt and Pepper and Gaussian noise.

A New Approach for Testing Fetal Heart Rate Monitors.

In this paper, a new approach for the periodical testing and the functionality evaluation of a fetal heart rate monitor device based on ultrasound principle is proposed. The design and realization of the device are presented, together with the description of its features and functioning tests. In the designed device, a relay element, driven by an electric signal that allows switching at two specific frequencies, is used to simulate the fetus and the mother's heartbeat. The simulator was designed to be compliant with the standard requirements for accurate assessment and measurement of medical devices. The accuracy of the simulated signals was evaluated, and it resulted to be stable and reliable. The generated frequencies show an error of about 0.5% with respect to the nominal one while the accuracy of the test equipment was within ±3% of the test signal set frequency. This value complies with the technical standard for the accuracy of fetal heart rate monitor devices. Moreover, the performed tests and measurements show the correct functionality of the developed simulator. The proposed equipment and testing respect the technical requirements for medical devices. The features of the proposed device make it simple and quick in testing a fetal heart rate monitor, thus providing an efficient way to evaluate and test the correlation capabilities of commercial apparatuses.

Expression, purification and stabilization of human serotonin transporter from E. coli.

The serotonin transporter belongs to the family of sodium-chloride coupled neurotransmitter transporter and is related to depression in humans. It is therefore an important drug target to support treatment of depression. Recently, structures of human serotonin transporter in complex with inhibitor molecules have been published. However, the production of large protein amounts for crystallization experiments remains a bottleneck. Here we present the possibility to obtain purified serotonin transporter from E. coli. Fos-choline 12 solubilized target protein was obtained with a purity of >95% and a yield of 1.2 mg L-1 culture in autoinduction medium. CD spectroscopic analysis of protein stability allowed identifying CHS and POPX as stabilizing components, which increased hSERT thermostability by 7 °C. The kinetic dissociation constant KD of 2.8 µM (±0.05) for of the inhibitor Desipramine was determined with a ka of 10,848 M - 1 s-1 (±220) and a kd of 0.03 s-1 (±4.7 × 10-5).

Using the IBM SPSS SW Tool with Wavelet Transformation for CO2 Prediction within IoT in Smart Home Care.

Standard solutions for handling a large amount of measured data obtained from intelligent buildings are currently available as software tools in IoT platforms. These solutions optimize the operational and technical functions managing the quality of the indoor environment and factor in the real needs of residents. The paper examines the possibilities of increasing the accuracy of CO2 predictions in Smart Home Care (SHC) using the IBM SPSS software tools in the IoT to determine the occupancy times of a monitored SHC room. The processed data were compared at daily, weekly and monthly intervals for the spring and autumn periods. The Radial Basis Function (RBF) method was applied to predict CO2 levels from the measured indoor and outdoor temperatures and relative humidity. The most accurately predicted results were obtained from data processed at a daily interval. To increase the accuracy of CO2 predictions, a wavelet transform was applied to remove additive noise from the predicted signal. The prediction accuracy achieved in the selected experiments was greater than 95%.

Recent Trends, Technical Concepts and Components of Computer-Assisted Orthopedic Surgery Systems: A Comprehensive Review.

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.

Electrogastrography measurement systems and analysis methods used in clinical practice and research: comprehensive review.

Electrogastrography (EGG) is a non-invasive method with high diagnostic potential for the prevention of gastroenterological pathologies in clinical practice. In this study, a review of the measurement systems, procedures, and methods of analysis used in electrogastrography is presented. A critical review of historical and current literature is conducted, focusing on electrode placement, measurement apparatus, measurement procedures, and time-frequency domain methods of filtration and analysis of the non-invasively measured electrical activity of the stomach. As a result, 129 relevant articles with primary aim on experimental diet were reviewed in this study. Scopus, PubMed, and Web of Science databases were used to search for articles in English language, according to the specific query and using the PRISMA method. The research topic of electrogastrography has been continuously growing in popularity since the first measurement by professor Alvarez 100 years ago, and there are many researchers and companies interested in EGG nowadays. Measurement apparatus and procedures are still being developed in both commercial and research settings. There are plenty variable electrode layouts, ranging from minimal numbers of electrodes for ambulatory measurements to very high numbers of electrodes for spatial measurements. Most authors used in their research anatomically approximated layout with two++ active electrodes in bipolar connection and commercial electrogastrograph with sampling rate of 2 or 4 Hz. Test subjects were usually healthy adults and diet was controlled. However, evaluation methods are being developed at a slower pace, and usually the signals are classified only based on dominant frequency. The main review contributions include the overview of spectrum of measurement systems and procedures for electrogastrography developed by many authors, but a firm medical standard has not yet been defined. Therefore, it is not possible to use this method in clinical practice for objective diagnosis. Systematic Review Registration: https://www.prisma-statement.org/.

Present results and methods of vectorcardiographic diagnostics of ischemic heart disease.

This article presents an overview of existing approaches to perform vectorcardiographic (VCG) diagnostics of ischemic heart disease (IHD). Individual methodologies are divided into categories to create a comprehensive and clear overview of electrical cardiac activity measurement, signal pre-processing, features extraction and classification procedures. An emphasis is placed on methods describing the electrical heart space (EHS) by several features extraction techniques based on spatiotemporal characteristics or signal modelling and signal transformations. Performance of individual methodologies are compared depending on classification of extent of ischemia, acute forms - myocardial infarction (MI) and myocardial scars localization. Based on a comparison of imaging methods, the advantages of VCG over the standard 12-leads ECG such as providing a 3D orthogonal leads imaging, better performance, and appropriate computer processing are highlighted. The issues of electrical cardiac activity measurements on body surface, the lack of VKG databases supported by a more accurate imaging method, possibility of comparison with the physiology of individual cases are outlined as potential reserves for future research.

Bioimpedance measurement: a non-invasive diagnosis of limb compartment syndrome.

Introduction: The methods for diagnosing compartment syndrome non-invasively remain under debate. Bioimpedance measurements offer a promising avenue in clinical practice, detecting subtle changes in organ impedance due to volume shifts. This study explores bioimpedance measurement as a novel, painless method for diagnosing compartment syndrome, potentially enabling continuous monitoring. Objective: This work aims to develop a prototype device for non-invasive diagnosis of compartment syndrome based on bioimpedance changes and assess initial results through in vitro experiments on inanimate biological material. We assume a change in the bioimpedance value after the application of physiological solution. Materials and Methods: Between 2018 and 2022, a prototype device for diagnosing limb compartment syndrome was collaboratively developed with the Department of Cybernetics and Biomedical Engineering at the Technical University of Ostrava, Czech Republic. This device operates by comparing bioimpedance between two compartments, one of which is pathologically affected (experiencing compartment syndrome). The Bioimpedance Analyzer for Compartment Syndrome (BACS) has been utilized to conduct measurements on inanimate biological material in laboratory settings. Two samples of duck and chicken tissue, as well as piglets, were employed for these experiments. According to the size of sample was compartment syndrome simulated by injecting 20-120 mL saline into one limb (breast) while leaving the other as a control. Invasive intramuscular pressure measurements were conducted post-saline injection using a conventional device (Stryker). Changes in bioimpedance were evaluated following saline application. Results: The non-invasive bioimpedance measurement instrument has been developed. It meets the safety requirements of European standard EN 60601-1. Measurement of accuracy showed minimal deviation for both channels (1.08% for the left channel and 1.84% for the right channel) when measuring on resistors. Ten measurements were conducted using the BACS prototype - two on chicken legs, two on duck breasts, two on duck legs, and four on piglets. Compartment syndrome simulation was achieved for all 10 measurements (IMP variance 31-45 mmHg). Following saline application, a notable decrease in bioimpedance was observed in the compartment simulating compartment syndrome (decrease by 12-78 Ω). Conclusion: Non-invasive methods could revolutionize limb compartment syndrome diagnosis, offering advantages such as non-invasiveness and continuous monitoring of compartment swelling.

Retinal Image Dataset of Infants and Retinopathy of Prematurity.

Retinopathy of prematurity (ROP) represents a vasoproliferative disease, especially in newborns and infants, which can potentially affect and damage the vision. Despite recent advances in neonatal care and medical guidelines, ROP still remains one of the leading causes of worldwide childhood blindness. The paper presents a unique dataset of 6,004 retinal images of 188 newborns, most of whom are premature infants. The dataset is accompanied by the anonymized patients' information from the ROP screening acquired at the University Hospital Ostrava, Czech Republic. Three digital retinal imaging camera systems are used in the study: Clarity RetCam 3, Natus RetCam Envision, and Phoenix ICON. The study is enriched by the software tool ReLeSeT which is aimed at automatic retinal lesion segmentation and extraction from retinal images. Consequently, this tool enables computing geometric and intensity features of retinal lesions. Also, we publish a set of pre-processing tools for feature boosting of retinal lesions and retinal blood vessels for building classification and segmentation models in ROP analysis.

Representative QRS loop of the VCG record evaluation.

Introduction: This study proposes an algorithm for preprocessing VCG records to obtain a representative QRS loop. Methods: The proposed algorithm uses the following methods: Digital filtering to remove noise from the signal, wavelet-based detection of ECG fiducial points and isoelectric PQ intervals, spatial alignment of QRS loops, QRS time synchronization using root mean square error minimization and ectopic QRS elimination. The representative QRS loop is calculated as the average of all QRS loops in the VCG record. The algorithm is evaluated on 161 VCG records from a database of 58 healthy control subjects, 69 patients with myocardial infarction, and 34 patients with bundle branch block. The morphologic intra-individual beat-to-beat variability rate is calculated for each VCG record. Results and Discussion: The maximum relative deviation is 12.2% for healthy control subjects, 19.3% for patients with myocardial infarction, and 17.2% for patients with bundle branch block. The performance of the algorithm is assessed by measuring the morphologic variability before and after QRS time synchronization and ectopic QRS elimination. The variability is reduced by a factor of 0.36 for healthy control subjects, 0.38 for patients with myocardial infarction, and 0.41 for patients with bundle branch block. The proposed algorithm can be used to generate a representative QRS loop for each VCG record. This representative QRS loop can be used to visualize, compare, and further process VCG records for automatic VCG record classification.

A innovative wavelet transformation method optimization in the noise-canceling application within intelligent building occupancy detection monitoring.

The study deals with detection of the occupation of Intelligent Building (IB) using data obtained from indirect methods with Big Data Analysis within IoT. In the area of daily living activity monitoring, one of the most challenging tasks is occupancy prediction, giving us information about people's mobility in the building. This task can be done via monitoring of CO2 as a reliable method, which has the ambition to predict the presence of the people in specific areas. In this paper, we propose a novel hybrid system, which is based on the Support Vector Machine (SVM) prediction of the CO2 waveform with the use of sensors that measure indoor/outdoor temperature and relative humidity. For each such prediction, we also record the gold standard CO2 signal to objectively compare and evaluate the quality of the proposed system. Unfortunately, this prediction is often linked with a presence of predicted signal activities in the form of glitches, often having an oscillating character, which inaccurately approximates the real CO2 signals. Thus, the difference between the gold standard and the prediction results from SVM is increasing. Therefore, we employed as the second part of the proposed system a smoothing procedure based on Wavelet transformation, which has ambitions to reduce inaccuracies in predicted signal via smoothing and increase the accuracy of the whole prediction system. The whole system is completed with an optimization procedure based on the Artificial Bee Colony (ABC) algorithm, which finally classifies the wavelet's response to recommend the most suitable wavelet settings to be used for data smoothing.

Design of Proposed Software System for Prediction of Iliosacral Screw Placement for Iliosacral Joint Injuries Based on X-ray and CT Images.

One of the crucial tasks for the planning of surgery of the iliosacral joint is placing an iliosacral screw with the goal of fixing broken parts of the pelvis. Tracking of proper screw trajectory is usually done in the preoperative phase by the acquisition of X-ray images under different angles, which guide the surgeons to perform surgery. This approach is standardly complicated due to the investigation of 2D X-ray images not showing spatial perspective. Therefore, in this pilot study, we propose complex software tools which are aimed at making a simulation model of reconstructed CT (DDR) images with a virtual iliosacral screw to guide the surgery process. This pilot study presents the testing for two clinical cases to reveal the initial performance and usability of this software in clinical conditions. This model is consequently used for a multiregional registration with reference intraoperative X-ray images to select the slide from the 3D dataset which best fits with reference X-ray. The proposed software solution utilizes input CT slices of the pelvis area to create a segmentation model of individual bone components. Consequently, a model of an iliosacral screw is inserted into this model. In the next step, we propose the software CT2DDR which makes DDR projections with the iliosacral screw. In the last step, we propose a multimodal registration procedure, which performs registration of a selected number of slices with reference X-ray, and based on the Structural Similarity Index (SSIM) and index of correlation, the procedure finds the best match of DDR with X-ray images. In this pilot study, we also provide a comparative analysis of the computational costs of the multimodal registration upon various numbers of DDR slices to show the complex software performance. The proposed complex model has versatile usage for modeling and surgery planning of the pelvis area in fractures of iliosacral joints.

Review of present method of glucose from human blood and body fluids assessment.

the work has been aimed to create an overview of available and used methods and ways to determine the concentration of glucose in body fluids, especially from a technical point of view. It also provides an overview of the clinical features of these methods. The survey found that today's market offers a large number of options and approaches to the issue. There are accurate reference laboratory methods, self-monitoring methods for measuring glucose levels using glucometers, or continuous methods for daily monitoring of blood glucose trends and for insulin pump control. However, it must not be forgotten that the development of full closure of feedback is still not complete today. Individual methods cannot always be compared with each other, precisely because of the focus and the use of these methods. Choosing the right method of blood glucose levels in the body measuring can help patients to manage their diabetes mellitus. The methods listed in the overview are divided in terms of measurement continuity and further according to the invasiveness of the method. Finally, the issues of accuracy in the detection of glycaemia variability and the possibility of further development of these methods are discussed, as it is clear from the survey that the development is focused mainly on continuous methods improving that get to the forefront and also on developing a biosensor that is purely non-invasive and continuous.

Recent Trends, Construction, and Applications of Smart Textiles and Clothing for Monitoring of Health Activity: A Comprehensive Multidisciplinary Review.

In the area of biomedical signal monitoring, wearable electronics represents a dynamically growing field with a significant impact on the market of commercial products of biomedical signal monitoring and acquisition, as well as consumer electronic for vital functions monitoring. Since the electrodes are perceived as one of the most important part of the biomedical signal monitoring, they have been one of the most frequent subjects in the research community. Electronic textile (e-textile), also called smart textile represents a modern trend in the wearable electronics, integrating of functional materials with common clothing with the goal to realize the devices, which include sensors, antennas, energy harvesters and advanced textiles for self-cooling and heating. The area of textile electrodes and e-textile is perceived as a multidisciplinary field, integrating material engineering, chemistry, and biomedical engineering. In this review, we provide a comprehensive view on this area. This multidisciplinary review integrates the e-textile characteristics, materials and manufacturing of the textile electrodes, noise influence on the e-textiles performance, and mainly applications of the textile electrodes for biomedical signal monitoring and acquisition, including pressure sensors, electrocardiography, electromyography, electroencephalography and electrooculography monitoring.