I am a researcher specializing in medical and veterinary data analysis, with a strong focus on biomedical signal and image processing. My work centers on extracting meaningful parameters from complex datasets to enable accurate classification, pattern discovery, and decision support.
I apply a wide range of machine learning techniques — from traditional methods to modern deep learning approaches — to identify relevant features, improve diagnostic accuracy, and support data‑driven insights in biomedical applications.
Biomedical signal processing (ECG EMG EEG and other physiological signals) Medical and veterinary imaging analysis especially texture analysis Feature extraction and parameter identification for classification Machine learning and pattern recognition Data-driven modeling in health and life sciences
Abstract Biomedical signals are frequently noisy and incomplete. They produce complex and high-dimensional data sets. In these mentioned cases, the results of traditional methods of signal processing can be skewed by noise or interference present in the signal. Information entropy, as a measure of disorder or uncertainty in the data, was introduced by Shannon. To date, many different types of entropy methods have appeared with many different application areas. The purpose of this paper is to present a short overview of some methods of entropy analysis and to discuss their suitability for use in the analysis of biomedical signals.
This paper analyses the complexity of electroencephalogram (EEG) signals in different temporal scales for the analysis and classification of focal and non-focal EEG signals. Futures from an original multiscale permutation Lempel–Ziv complexity measure (MPLZC) were obtained. MPLZC measure combines a multiscale structure, ordinal analysis, and permutation Lempel–Ziv complexity for quantifying the dynamic changes of an electroencephalogram (EEG). We also show the dependency of MPLZC on several straight-forward signal processing concepts, which appear in biomedical EEG activity via a set of synthetic signals. The main material of the study consists of EEG signals, which were obtained from the Bern-Barcelona EEG database. The signals were divided into two groups: focal EEG signals (n = 100) and non-focal EEG signals (n = 100); statistical analysis was performed by means of non-parametric Mann–Whitney test. The mean value of MPLZC results in the non-focal group are significantly higher than those in the focal group for scales above 1 (p < 0.05). The result indicates that the non-focal EEG signals are more complex. MPLZC feature sets are used for the least squares support vector machine (LS-SVM) classifier to classify into the focal and non-focal EEG signals. Our experimental results confirmed the usefulness of the MPLZC method for distinguishing focal and non-focal EEG signals with a classification accuracy of 86%.
Recent decades clearly demonstrate the growing use of nanomaterials in medical practice, and their effectiveness is systematically confirmed by the consequent scientific research. An example of the use of nanomaterials in dentistry is endodontic treatment, which, due to its specificity, is one of the most demanding procedures, fraught with numerous challenges, such as difficulties in reaching tooth roots and ineffective cleaning or insufficient sealing of root canals, which may lead to re-infection or damage to adjacent structures. The use of nanomaterials has a positive impact on solving these problems, and the combination of biomaterials with nanometric technology makes endodontic treatment more effective, precise and comfortable for patients, which contributes to improving the quality of dental care. Currently, nanomaterials with a high biocompatibility can be used in endodontics as components of irrigation solutions, for rinsing root canals and as drug carriers for intracanal use. Nanomaterials are also components of sealants filling root canals. However, the latest research shows that reducing the size of materials to the “nano” scale significantly affects their basic physicochemical properties, which leads to increased reactivity and the ability to interact at the molecular level. These unique physicochemical properties, which have contributed to the use of nanomaterials in numerous medical-related solutions, raise concerns and provoke discussions about the safety of their use in direct contact with tissues.
Abstract This paper reports on a multiresolution analysis of EEG signals. The dominant frequency components of signals with and without observed epileptic discharges were compared. The study showed that there were significant differences in dominant frequency between the signals with epileptic discharges and the signals without discharges. This gives the ability to identify epilepsy during EEG examination. The frequency of the signals coming from the frontal, central, parietal and occipital channels are similar. Multiresolution analysis can be used to describe the activity of brain waves and to try to predict epileptic seizures, thereby contributing to precise medical diagnoses.
Objectives: Non-small cell lung cancer (NSCLC), the most prevalent type of lung cancer, includes subtypes such as adenocarcinoma (ADC) and squamous cell carcinoma (SCC), which require distinct management approaches. Accurately differentiating NSCLC subtypes based on diagnostic imaging remains challenging. However, the extraction of radiomic features—such as first-order statistics (FOS), second-order statistics (SOS), and fractal dimension texture analysis (FDTA) features—from magnetic resonance (MR) images supports the development of quantitative NSCLC assessments. Methods: This study aims to evaluate whether the integration of FDTA features with FOS and SOS texture features in MR image analysis improves machine learning classification of NSCLC into ADC and SCC subtypes. The study was conducted on 274 MR images, comprising ADC (n = 122) and SCC (n = 152) cases. From the segmented MR images, 93 texture features were extracted. The random forest algorithm was used to identify informative features from both FOS/SOS and combined FOS/SOS/FDTA datasets. Subsequently, the k-nearest neighbors (kNN) algorithm was applied to classify MR images as ADC or SCC. Results: The highest performance (accuracy = 0.78, precision = 0.81, AUC = 0.89) was achieved using 37 texture features selected from the combined FOS/SOS/FDTA dataset. Conclusions: Incorporating fractal descriptors into the texture-based classification of lung MR images enhances the differentiation of NSCLC subtypes.
Infrared thermography (IRT) was applied as a potentially useful tool in the detection of pregnancy in equids, especially native or wildlife. IRT measures heat emission from the body surface, which increases with the progression of pregnancy as blood flow and metabolic activity in the uterine and fetal tissues increase. Conventional IRT imaging is promising; however, with specific limitations considered, this study aimed to develop novel digital processing methods for thermal images of pregnant mares to detect pregnancy earlier with higher accuracy. In the current study, 40 mares were divided into non-pregnant and pregnant groups and imaged using IRT. Thermal images were transformed into four color models (RGB, YUV, YIQ, HSB) and 10 color components were separated. From each color component, features of image texture were obtained using Histogram Statistics and Grey-Level Run-Length Matrix algorithms. The most informative color/feature combinations were selected for further investigation, and the accuracy of pregnancy detection was calculated. The image texture features in the RGB and YIQ color models reflecting increased heterogeneity of image texture seem to be applicable as potential indicators of pregnancy. Their application in IRT-based pregnancy detection in mares allows for earlier recognition of pregnant mares with higher accuracy than the conventional IRT imaging technique.
Evaluation of the effectiveness of the healing process in postresectal and postcystal bone loss cases using techniques guided bone regeneration, observed within 1-year-long period. Radiographic images of 20 patients (17 females and 8 males) who had undergone xenotransplantation to fill jawbone losses were analyzed. The combination therapy of intraosseous deficits following xenotransplantation consisted of bone augmentation with xenogenic material together with covering regenerative membranes and tight wound closure. The bone regeneration process was estimated comparing the images taken on the day of the surgery and 12 months later, by means of digital radiography set Kodak RVG 6100. The interpretation of the RVG image depends on the assessment ability of the eye looking at it, which gives a large margin of uncertainty. Areas of interest were separated from radiographic images and binarized. On the basis of those fragments, box-counting dimension ( $$D_{B}$$ ) and information dimension ( $$D_{I}$$ ) were calculated. Box-counting dimension and information dimension values increase with time—image structures become more complex. Knowing that in case of normal bone regeneration, the value of the fractal dimension equals 1.0 right after the surgery and 1.6 after a year after the bone treatment, and we could use image segmentation to efficiently find fragments where this value differs from those acquired in the course of tests and quicken diagnostics of irregularities in bone tissue regeneration.
Human gait recognition is one of the most interesting issues within the subject of behavioral biometrics. The most significant problems connected with the practical application of biometric systems include their accuracy as well as the speed at which they operate, understood both as the time needed to recognize a particular person as well as the time necessary to create and train a biometric system. The present study made use of an ensemble of heterogeneous base classifiers to address these issues. A Heterogeneous ensemble is a group of classification models trained using various algorithms and combined to output an effective recognition A group of parameters identified on the basis of ground reaction forces was accepted as input signals. The proposed solution was tested on a sample of 322 people (5980 gait cycles). Results concerning the accuracy of recognition (meaning the Correct Classification Rate quality at 99.65%), as well as operation time (meaning the time of model construction at <12.5 min and the time needed to recognize a person at <0.1 s), should be considered as very good and exceed in quality other methods so far described in the literature.
Abstract: The analysis of the uterine contraction signals in nonpregnant states gives information about physiological changes during the menstrual cycle. Spontaneous uterine activity was recorded directly by a dual microtip catheter. The device consisted of two ultra‐miniature pressure sensors. One sensor was placed in the fundus, the other in the cervix. It was important to identify time delays between contractions in two topographic locations, which may be of potential diagnostic significance in various pathologies: dysmenorrhea, endometriosis, and fecundity disorders. In this study the following synchronization measures—the cross‐correlation, the semblance, the mutual information—were used to visualize the time delay changes over time. These measures were computed in a moving window with a width corresponding to approximately two or three contractions. As a result, the running synchronization functions were obtained. The running synchronization functions visualize changes in the propagation of the two simultaneously recorded signals. The propagation% parameter assessed from these functions allows for quantitative description of synchronization. Finally, we illustrate the use of running synchronization functions to investigate the effect of treatment with tamoxifen on primary dysmenorrhea.
As the detection of horse state after exercise is constantly developing, a link between blood biomarkers and infrared thermography (IRT) was investigated using advanced image texture analysis. The aim of the study was to determine which combinations of RGB (red-green-blue), YUI (brightness-UV-components), YIQ (brightness-IQ-components), and HSB (hue-saturation-brightness) color models, components, and texture features are related to the blood biomarkers of exercise effect. Twelve Polish warmblood horses underwent standardized exercise tests for six consecutive days. Both thermal images and blood samples were collected before and after each test. All 144 obtained IRT images were analyzed independently for 12 color components in four color models using eight texture-feature approaches, including 88 features. The similarity between blood biomarker levels and texture features was determined using linear regression models. In the horses’ thoracolumbar region, 12 texture features (nine in RGB, one in YIQ, and two in HSB) were related to blood biomarkers. Variance, sum of squares, and sum of variance in the RGB were highly repeatable between image processing protocols. The combination of two approaches of image texture (histogram statistics and gray-level co-occurrence matrix) and two color models (RGB, YIQ), should be considered in the application of digital image processing of equine IRT.
Abstract Background The horses’ backs are particularly exposed to overload and injuries due to direct contact with the saddle and the influence of e.g. the rider’s body weight. The maximal load for a horse’s back during riding has been suggested not to exceed 20% of the horses’ body weight. The common prevalence of back problems in riding horses prompted the popularization of thermography of the thoracolumbar region. However, the analysis methods of thermographic images used so far do not distinguish loaded horses with body weight varying between 10 and 20%. Results The superficial body temperature (SBT) of the thoracolumbar region of the horse’s back was imaged using a non-contact thermographic camera before and after riding under riders with LBW (low body weight, 10%) and HBW (high body weight, 15%). Images were analyzed using six methods: five recent SBT analyses and the novel approach based on Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM). Temperatures of the horse’s thoracolumbar region were higher ( p < 0.0001) after then before the training, and did not differ depending on the rider’s body weight ( p > 0.05), regardless of used SBT analysis method. Effort-dependent differences ( p < 0.05) were noted for six features of GLCM and GLRLM analysis. The values of selected GLCM and GLRLM features also differed ( p < 0.05) between the LBW and HBW groups. Conclusion The GLCM and GLRLM analyses allowed the differentiation of horses subjected to a load of 10 and 15% of their body weights while horseback riding in contrast to the previously used SBT analysis methods. Both types of analyzing methods allow to differentiation thermal images obtained before and after riding. The textural analysis, including selected features of GLCM or GLRLM, seems to be promising tools in considering the quantitative assessment of thermographic images of horses’ thoracolumbar region.
Infrared thermography is a valuable tool adapted for veterinary diagnostics with an increasing number of uses. However, proper image acquisition is hard, not only due to various factors affecting the image but also because informative image processing is a struggle. Thus, this study aims to quantify the area of maximum temperature (Area of Tmax) on the lateral surface of horses and foals to compare the Areas of Tmax between horses and foals and to compare two new approaches to the Area of Tmax quantification in horses. Infrared images were acquired with a thermographic camera from 12 horses and 12 foals in the same ambient condition. The backgrounds of the images were removed, and the images were then processed in Rainbow HC and a grayscale palette. Then, 10 images were created, showing the Areas of Tmax in gradually decreasing ranges. The evaluation of the Area of Tmax with two image processing methods showed higher maximum temperatures in foals, although the high-temperature values covered less of their total body area than in adult horses. The results indicate the struggles of foals with thermal homeostasis. The proposed methods—multi-colored annotated pixels on Rainbow HC and red-annotated pixels on grayscale—provide a common quality in the thermogram evaluation of foals and adult horses. Further research is essential to determine their diagnostic application.
Analysis of the uterine contractility in non-pregnant women provides information about physiological changes during menstrual cycle. Spontaneous uterine activity was recorded directly by a micro-tip catheter (Millar Instruments, Inc. USA). The sensor produced an electrical signal, which varied in direct proportion to the magnitude of measured pressure. The study was approved by the regional ethics committee. We used the techniques of surrogate data analysis to testing for nonlinearity in the uterine contraction signals. Approximate entropy was the test statistic. For this analysis a healthy patient with normal contractions, a patient with dysmenorrhea and a patient with fibromyomas in the follicular phase were selected. The results showed that the spontaneous uterine contractions are considered to contain nonlinear features.
For medical images diagnostically important information often lies in the texture. Fractal dimension may be used as an index of irregularity. In this paper we describe the adaptation of the intensity difference scaling method for assessment of the fractal dimension D in the irregular regions of interest (irregular ROIs). This is of great importance because the investigated regions are often small. It is difficult to fit entire regular region of interest within the examined organ with simultaneous inclusion of the relevant fragment, and at the same time to avoid the influence of boundaries. Fractal analysis of various kinds of medical images: panoramic radiography and nuclear medicine scan showed the validity of assessment of D in irregular ROIs.
Appropriate matching of rider–horse sizes is becoming an increasingly important issue of riding horses’ care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body’s surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10–12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered.
Equine odontoclastic tooth resorption and hypercementosis (EOTRH) is one of the horses’ dental diseases, mainly affecting the incisor teeth. An increase in the incidence of aged horses and a painful progressive course of the disease create the need for improved early diagnosis. Besides clinical findings, EOTRH recognition is based on the typical radiographic findings, including levels of dental resorption and hypercementosis. This study aimed to introduce digital processing methods to equine dental radiographic images and identify texture features changing with disease progression. The radiographs of maxillary incisor teeth from 80 horses were obtained. Each incisor was annotated by separate masks and clinically classified as 0, 1, 2, or 3 EOTRH degrees. Images were filtered by Mean, Median, Normalize, Bilateral, Binomial, CurvatureFlow, LaplacianSharpening, DiscreteGaussian, and SmoothingRecursiveGaussian filters independently, and 93 features of image texture were extracted using First Order Statistics (FOS), Gray Level Co-occurrence Matrix (GLCM), Neighbouring Gray Tone Difference Matrix (NGTDM), Gray Level Dependence Matrix (GLDM), Gray Level Run Length Matrix (GLRLM), and Gray Level Size Zone Matrix (GLSZM) approaches. The most informative processing was selected. GLCM and GLRLM return the most favorable features for the quantitative evaluation of radiographic signs of the EOTRH syndrome, which may be supported by filtering by filters improving the edge delimitation.
Abstract Background Equine Odontoclastic Tooth Resorption and Hypercementosis (EOTRH) syndrome is a dental disease where the radiographic signs may be quantified using radiographic texture features. This study aimed to implement the scaled–pixel–counting protocol to quantify and compare the image structure of teeth and the density standard in order to improve the identification of the radiographic signs of tooth resorption and hypercementosis using the EOTRH syndrome model. Methods and results A detailed examination of the oral cavity was performed in 80 horses and maxillary incisor teeth were evaluated radiographically, including an assessment of the density standard. On each of the radiographs, pixel brightness (PB) was extracted for each of the ten steps of the density standard (S1–S10). Then, each evaluated incisor tooth was assigned to one of 0–3 EOTRH grade–related groups and annotated using region of interest (ROI). For each ROI, the number of pixels (NP) from each range was calculated. The linear relation between an original X–ray beam attenuation and PB was confirmed for the density standard. The NP values increased with the number of steps of the density standard as well as with EOTRH degrees. Similar accuracy of the EOTRH grade differentiation was noted for data pairs EOTRH 0–3 and EOTRH 0–1, allowing for the differentiation of both late and early radiographic signs of EOTRH. Conclusion The scaled–pixel–counting protocol based on the use of density standard has been successfully implemented for the differentiation of radiographic signs of EOTRH degrees.
The paranasal sinuses, a bilaterally symmetrical system of eight air-filled cavities, represent one of the most complex parts of the equine body. This study aimed to extract morphometric measures from computed tomography (CT) images of the equine head and to implement a clustering analysis for the computer-aided identification of age-related variations. Heads of 18 cadaver horses, aged 2–25 years, were CT-imaged and segmented to extract their volume, surface area, and relative density from the frontal sinus (FS), dorsal conchal sinus (DCS), ventral conchal sinus (VCS), rostral maxillary sinus (RMS), caudal maxillary sinus (CMS), sphenoid sinus (SS), palatine sinus (PS), and middle conchal sinus (MCS). Data were grouped into young, middle-aged, and old horse groups and clustered using the K-means clustering algorithm. Morphometric measurements varied according to the sinus position and age of the horses but not the body side. The volume and surface area of the VCS, RMS, and CMS increased with the age of the horses. With accuracy values of 0.72 for RMS, 0.67 for CMS, and 0.31 for VCS, the possibility of the age-related clustering of CT-based 3D images of equine paranasal sinuses was confirmed for RMS and CMS but disproved for VCS.
Dental disorders are a serious health problem in equine medicine, their early recognition benefits the long-term general health of the horse. Most of the initial signs of Equine Odontoclastic Tooth Resorption and Hypercementosis (EOTRH) syndrome concern the alveolar aspect of the teeth, thus, the need for early recognition radiographic imaging. This study is aimed to evaluate the applicability of entropy measures to quantify the radiological signs of tooth resorption and hypercementosis as well as to enhance radiographic image quality in order to facilitate the identification of the signs of EOTRH syndrome. A detailed examination of the oral cavity was performed in eighty horses. Each evaluated incisor tooth was assigned to one of four grade–related EOTRH groups (0–3). Radiographs of the incisor teeth were taken and digitally processed. For each radiograph, two–dimensional sample (SampEn2D), fuzzy (FuzzEn2D), permutation (PermEn2D), dispersion (DispEn2D), and distribution (DistEn2D) entropies were measured after image filtering was performed using Normalize, Median, and LaplacianSharpening filters. Moreover, the similarities between entropy measures and selected Gray–Level Co–occurrence Matrix (GLCM) texture features were investigated. Among the 15 returned measures, DistEn2D was EOTRH grade–related. Moreover, DistEn2D extracted after Normalize filtering was the most informative. The EOTRH grade–related similarity between DistEn2D and Difference Entropy (GLCM) confirms the higher irregularity and complexity of incisor teeth radiographs in advanced EOTRH syndrome, demonstrating the greatest sensitivity (0.50) and specificity (0.95) of EOTRH 3 group detection. An application of DistEn2D to Normalize filtered incisor teeth radiographs enables the identification of the radiological signs of advanced EOTRH with higher accuracy than the previously used entropy–related GLCM texture features.
Abstract Background In veterinary medicine, conventional radiography is the first–choice method for most diagnostic imaging applications in both small animal and equine practice. One direction in its development is the integration of bone density evaluation and artificial intelligence–assisted clinical decision–making, which is expected to enhance and streamline veterinarians’ daily practices. One such decision–support method is k–means clustering, a machine learning and data mining technique that can be used clinically to classify radiographic signs into healthy or affected clusters. The study aims to investigate whether the k–means clustering algorithm can differentiate cortical and trabecular bone in both healthy and affected horse limbs. Therefore, identifying the optimal computed digital absorptiometry parameters was necessary. Methods and results Five metal–made density standards, made of pure aluminum, aluminum alloy (duralumin), cuprum alloy, iron–nickel alloy, and iron–silicon alloy, and ten X–ray tube settings were evaluated for the radiographic imaging of equine distal limbs, including six healthy limbs and six with radiographic signs of osteoarthritis. Density standards were imaged using ten combinations of X–ray tube settings, ranging from 50 to 90 kV and 1.2 to 4.0 mAs. The relative density in Hounsfield units was firstly returned for both bone types and density standards, then compared, and finally used for clustering. In both healthy and osteoarthritis–affected limbs, the relative density of the long pastern bone (the proximal phalanx) differed between bone types, allowing the k–means clustering algorithm to successful differentiate cortical and trabecular bone. Conclusion Density standard made of duralumin, along with the 60 kV, 4.0 mAs X–ray tube settings, yielded the highest clustering metric values and was therefore considered optimal for further research. We believe that the identified optimal computed digital absorptiometry parameters may be recommended for further researches on the relative quantification of conventional radiographs and for distal limb examination in equine veterinary practice.
Purpose: The aim of this study was to apply a LempelZiv complexity measure for quantifying biomedical signals and images. Material and methods: We analyzed angiogenic patterns and the signals (the heart rate, the respiration rate and the blood oxygen concentration). Biomedical signals were obtained by means of Internet. Medical images were from Department of Pathophysiology of Pregnancy Medical University of Bialystok. Results: The values of normalized complexity measures for respiratory rate signal are high, what indicates that this time series is close to unstructured randomness. The Lempel-Ziv complexity values for angiogenic patterns were growing with the FIGO stage of disease. Conclusions: Lempel-Ziv complexity may be a very helpful tool in analyzing the signals and images. It can be easily computed from the analysed data.
Purpose: We analyzed the heart rate variability (RR intervals) by means of nonlinear dynamics methods: Poincare plot (return map), approximate entropy (ApEn) and detrended fluctuation analysis (DFA). The purpose of this study was the quantitative and qualitative assessment of heart rate variability by means of these nonlinear dynamics methods. Material and methods: The Poincare plot is a scattergram, which is constructed by plotting each RR interval against the previous one. Approximate entropy describes the complexity and irregularity of the signals. Detrended fluctuation analysis quantifies fractal-like correlation properties of the data. We analyzed two groups of patients: test group A – 15 diabetic children with diabetes type 1 and microalbuminuria and control group C – 24 healthy children. For each patient 24 hour ECG (RR intervals) was recorded. Statistical analysis was performed by means of nonparametric Mann-Whitney test. Results: Return maps of healthy children are mostly very complex. In the case of diabetic children we found torpedoshaped plots. The values of ApEn were lower in diabetic children that indicated more regular heart rate in these patients. DFA method shows also differences between the investigated groups. Conclusions: We concluded that using nonlinear dynamics methods we could quantitatively and qualitatively study the heart rate variability in healthy and diabetic patients.
Abstract Biological time series have a finite number of samples with noise included in them. Because of this fact, it is not possible to reconstruct phase space in an ideal manner. One kind of biomedical signals are electrohisterographical (EHG) datasets, which represent uterine muscle contractile activity. In the process of phase space reconstruction, the most important thing is suitable choice of the method for calculating the time delay τ and embedding dimension d , which will reliably reconstruct the original signal. The parameters used in digital signal processing are key to arranging adequate parameters of the analysed attractor embedded in the phase space. The aim of this paper is to present a method employed for phase space reconstruction for EHG signals that will make it possible for their further analysis to be carried out.
As obesity is a serious problem in the human population, overloading of the horse’s thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse’s back overload will shortly integrate the IRT-based rider-horse fit into everyday equine practice. This study aimed to evaluate the applicability of entropy measures to select the most informative measures and color components, and the accuracy of rider:horse bodyweight ratio detection. Twelve horses were ridden by each of the six riders assigned to the light, moderate, and heavy groups. Thermal images were taken pre- and post-exercise. For each thermal image, two-dimensional sample (SampEn), fuzzy (FuzzEn), permutation (PermEn), dispersion (DispEn), and distribution (DistEn) entropies were measured in the withers and the thoracic spine areas. Among 40 returned measures, 30 entropy measures were exercise-dependent, whereas 8 entropy measures were bodyweight ratio-dependent. Moreover, three entropy measures demonstrated similarities to entropy-related gray level co-occurrence matrix (GLCM) texture features, confirming the higher irregularity and complexity of thermal image texture when horses worked under heavy riders. An application of DispEn to red color components enables identification of the light and heavy rider groups with higher accuracy than the previously used entropy-related GLCM texture features.
Osteoarthritis (OA) of the tarsal joint, also known as bone spavin, is a progressive joint disease that increases in severity with age. It is a significant cause of hind limb lameness, leading to a deterioration in the quality of life of horses, particularly in old age. In this study, the tarsal joints of 20 older horses aged 15 to 35 years were radiographically imaged and processed using the computed digital absorptiometry (CDA) method for bone mineral density (BMD) assessment. The radiological signs of bone spavin were scored on a scale ranging from normal (0) to severe OA (3), and the examined joints were grouped according to the severity of OA. The percentage of color pixels (%color pixels), representing successive steps on the scale of X-ray absorption by a density standard, differed between the steps in a BMD characteristic manner for each group. Furthermore, two examined ranges of relative density allowed for the distinction of joints affected by severe OA from other joints, while another two ranges allowed for the differentiation of joints affected by moderate and severe OA from normal joints. The proposed color annotation-assisted decomposition of radiological images based on the CDA protocol shows promise for advancing research on the quantification of radiological signs of OA. This approach could be valuable for monitoring the progression of the disease in older horses.
Computed tomography (CT) is one of the fundamental imaging modalities used in medicine, allowing for the acquisition of accurate cross-sectional images of internal body tissues. However, during the acquisition and reconstruction process, various artifacts can arise, and one of them is ring artifacts. These artifacts result from the inherent limitations of CT scanner components and the properties of the scanned material, such as detector defects, non-uniform distribution of radiation from the source, or the presence of metallic elements within the scanning region. The purpose of this study was to identify and reduce ring artifacts in tomographic images using image decomposition and average filtering methods. In this study, tests were conducted on the effectiveness of identifying ring artifacts using wavelet decomposition methods for images. The test was performed on a Shepp–Logan phantom with implemented artifacts of different intensity levels. The analysis was performed using different wavelet families, and linear approximation methods were used to filter the image in the identified areas. Additional filtering was performed using moving average methods and empirical mode decomposition (EMD) techniques. Image comparison methods, i.e., RMSE, SSIM and MS-SSIM, were used to evaluate performance. The results of this study showed a significant improvement in the quality of tomographic phantom images. The authors obtained more than 50% improvement in image quality with reference to the image without any filtration. The different wavelet families had different efficiencies with relation to the identification of the induction regions of ring artifacts. The Haar wavelet and Coiflet 1 showed the best performance in identifying artifact induction regions, with comparative RMSE values for these wavelets of 0.1477 for Haar and 0.1469 for Coiflet 1. The applied additional moving average filtering and EMD permitted us to improve image quality, which is confirmed by the results of the image comparison. The obtained results allow us to assess how the used methods affect the reduction in ring artifacts in phantom images with induced artifacts.
Incorporating lunging into a horse’s daily routine aims to enhance fitness, physical condition, and specific skills or exercises when using lunging aids (LAs). To assess the effectiveness of lunging, non-contact technologies like geometric morphometrics and infrared thermography can be employed. This study seeks to evaluate lunging efficiency based on the horse’s posture and surface temperature when lunging with different head and neck positions. The study aims to determine if changes in a horse’s posture correspond to increased metabolic activity, as indicated by body surface temperature. Thirteen horses included in the study were lunged with chambon (CH), rubber band (RB), and triangle side reins (TRs) as well as with a freely moving head (FMH). Images were taken in visible light and infrared. Principal Component Analysis (PCA) was used to analyze horse posture changes and a Pixel-Counting Protocol (PCP) was used to quantify surface temperature patterns. The horses’ posture exhibited contrasting changes, reflected by a changing centroid shape (p < 0.0001) but not size (p > 0.05) when lunged with RB and TRs, but not CH. Different (p < 0.0001) surface temperature patterns were observed during lunging. FMH lunging resulted in lower temperatures over a larger surface, CH induced moderate temperatures on a smaller area, RB caused moderate to high temperatures across a broader surface, and TRs led to higher temperatures over a smaller region. The studied lunging cases returned different (p < 0.0001) surface temperature patterns. Lunging with FMH returned lower temperatures over a larger surface, CH moderate temperatures on a smaller area, RB moderate to high temperatures across a broader surface, and TRs higher temperatures over a smaller region. The proposed methods can be applied to evaluate the efficiency of lunging in horses.
Osteoarthritis (OA), including knee joint OA, is a common chronic condition in cats. In both cats and humans, knee joint OA is characterized radiographically by the presence of osteophytes, enthesiophytes, subchondral sclerosis, and joint space narrowing. However, only in humans have these radiographic signs been reported to increase bone mineral density (BMD). Therefore, this study aims to quantify the volumetric (vBMD) and relative (rBMD) BMD measures of the feline knee joint and compare BMD measures between various severities of OA to test the hypothesized OA–BMD relationship in the knee joint in cats. The 46 feline knee joints were imaged using computed tomography (CT) and conventional radiography supported by the computed digital absorptiometry (CDA) method to obtain vBMD and rBMD, respectively. Both BMD measures were assessed in three regions of interest (ROIs): the distal femur (ROI 1), patella (ROI 2), and proximal tibia (ROI 3). In all locations, vBMD and rBMD showed moderate (ROI 2: r = 0.67, p < 0.0001) to strong (ROI 1: ρ = 0.96, p < 0.0001; ROI 3: r = 0.89, p < 0.0001) positive correlations. Due to differences (p < 0.0001) in the width of the distal femur (17.9 ± 1.21 mm), patella (8.2 ± 0.82 mm), and proximal tibia (19.3 ± 1.16 mm), the rBMD was corrected (corr rBMD) using the thickness coefficient of 0.46 ± 0.04 for ROI 2 and 1.08 ± 0.03 for ROI 3. Regardless of the quantification method used, BMD measures increased linearly from a normal knee joint to severe OA, with differences in BMD between normal and mild to severe knee joint OA. The OA–BMD relationship in the feline knee joint can be preliminarily confirmed.
The use of surface electromyography (sEMG) in equine locomotion research has increased significantly due to the essential role of balanced, symmetrical, and efficient movement in riding. However, variations in sEMG signal processing for forelimb extensor muscles across studies have made cross-study comparisons challenging. This study aims to compare the sEMG signal characteristics from carpal extensor muscles under different filtering methods: raw signal, low-pass filtering (10 Hz cut-off), and bandpass filtering (40–450 Hz cut-off and 7–200 Hz cut-off). sEMG signals were collected from four muscles of three horses during walking and trotting. The raw signals were normalized and filtered separately using a 4th-order Butterworth filter: low-pass 10 Hz, bandpass 40–450 Hz, or bandpass 7–200 Hz. For each filtered signal variant, eight activity bursts were annotated, and amplitude, root mean square (RMS), median frequency (MF), and signal-to-noise ratio (SNR) were extracted. Signal loss and residual signal were calculated to assess noise reduction and data retention. For m. extensor digitorum lateralis and m. extensor carpi ulnaris, bandpass filtering at 40–450 Hz resulted in the lowest signal loss and the highest amplitude, RMS, MF, and SNR after filtering. However, variations were observed for the other two carpal extensors. These findings support the hypotheses that the characteristics of myoelectric activity in equine carpal extensors vary depending on the filtering method applied and differ among individual muscles, thereby guiding future research on sEMG signal processing and, consequently, equine biomechanics. Since both noise and its reduction alter raw sEMG signals, potentially affecting data analysis, this study provides valuable insights for improving the reliability and reproducibility of equine biomechanics research across different sEMG studies.
Abstract Purpose of the study: The purpose of the study was to examine on the basis of radiographic images of bone tissue, whether there are differences in the rate of bone remodelling using different shapes of implants in the mandible and maxilla. Moreover, the study also compares texture features obtained on the basis of these images for healthy bone tissue, bone directly after implantation and after a 12-month period of prosthetic loading. Materials and Methods: The subject of the analysis was radiovisiogram images obtained from the Medical University of Bialystok from the Department of Dental Surgery. They are radiovisiogram photographs of 146 people aged 18–74, treated implantally due to missing teeth. The whole group of patients received two types of implants (Active and Replace) of the same company, made of titanium, intraosseous, screw-in. Results: It has been shown that both in the upper jaw and the mandible, the values of texture parameters obtained for bone images made after one year of prosthetic loading are closer to healthy tissue than immediately after implantation. These values for the mandible were relatively closer to those obtained on the basis of healthy tissue than those for the upper jaw. The bone around the implant with a single threading achieved better results in the mandible than the one with a double threading. Conclusion: The type of bone tissue and the shape of the implant have an impact on the achieved osseointegration. With the passage of time and the process of bone remodelling, the damaged tissue returns to its normal structure.
Abstract Background: As high-performance human and equine athletes train and compete at the highest level of effort, the prevention of high-performance-cased diseases, such as osteoarthritis (OA), requires knowledge of the anatomy and physiology of the subjected bones. Objective: Implementation of the scaled–pixel–counting protocol to quantify the radiological features of anatomical structures of the normal equine tarsal joint as the first step in the prevention of the tarsal joints OA in high-performance sport horses. Methods: A radiographic examination was performed in six cadaverous equine pelvic limbs. The dorso–plantar projection of the tarsal joint was performed using density standard (DS) attached to the radiographic cassette, standard X-ray equipment and standard diagnostic imaging protocol. On each of the radiographs, pixel brightness (PB) was extracted for each of the 10 steps (S1–S10) of DS. On each of the radiographs, seven regions of interest (ROIs) were annotated representing four bones (II tarsal bone [II TB], III tarsal bone [III TB], IV tarsal bone [IV TB] and central tarsal bone [CTB]) and three joints (proximal intertarsal joint [PIJ], distal intertarsal joint [DIJ] and tarsometatarsal joint [TMJ]), respectively. For each ROI, the percentage (%) of number of pixels (NP) from each range was calculated. Results: The % of NP was lower in bones than in joint spaces for S1–S6 and was higher in bones than in joint spaces for S8–S10. The % of NP was higher in PIJ than TMJ for S1 and higher in PIJ than DIJ for S4. No differences were found between consecutive bones for all examined steps of DS. Conclusions: An application of the scaled–pixel–counting protocol provides the quantitative radiological features of normal bone and joint structures of the tarsal joint in horses, making possible differentiation of the lucency of joint space and opacity of bone structure.
Dental diagnostic imaging has progressed towards the use of advanced technologies such as 3D image processing. Since multidetector computed tomography (CT) is widely available in equine clinics, CT-based anatomical 3D models, segmentations, and measurements have become clinically applicable. This study aimed to use a 3D segmentation of CT images and volumetric measurements to investigate differences in the surface area and volume of equine incisors. The 3D Slicer was used to segment single incisors of 50 horses’ heads and to extract volumetric features. Axial vertical symmetry, but not horizontal, of the incisors was evidenced. The surface area and volume differed significantly between temporary and permanent incisors, allowing for easy eruption-related clustering of the CT-based 3D images with an accuracy of >0.75. The volumetric features differed partially between center, intermediate, and corner incisors, allowing for moderate location-related clustering with an accuracy of >0.69. The volumetric features of mandibular incisors’ equine odontoclastic tooth resorption and hypercementosis (EOTRH) degrees were more than those for maxillary incisors; thus, the accuracy of EOTRH degree-related clustering was >0.72 for the mandibula and >0.33 for the maxilla. The CT-based 3D images of equine incisors can be successfully segmented using the routinely achieved multidetector CT data sets and the proposed data-processing approaches.
Bone mineral density (BMD) varies with aging and both systemic and local diseases; however, such evidence is lacking in feline medicine. This may be due to the need for general anesthesia in cats for direct BMD measurements using dual-energy X-ray absorptiometry (DXA) or quantitative computed tomography (QCT). In this study, computed digital absorptiometry (CDA), an indirect relative BMD-measuring method, was optimized to select an X-ray tube setting for the quantitative assessment of the feline knee joint. The knee joints of nine cats were radiographically imaged and processed using the CDA method with an aluminum density standard and five X-ray tube settings (from 50 to 80 kV; between 1.2 and 12 mAs). The reference attenuation of the X-ray beam for ten steps (S1–S10) of the density standard was recorded in Hounsfield units (HU), compared between X-ray tube settings, and used to determine the ranges of relative density applied for radiograph decomposition. The relative density decreased (p < 0.0001) with an increase in kV and dispersed with an increase in mAs. Then, the percentage of color pixels (%color pixels), representing ranges of relative density, was compared among S1–S10 and used for the recognition of background artifacts. The %color pixels was the highest for low steps and the lowest for high steps (p < 0.0001), regardless of X-ray tube settings. The X-ray tube setting was considered the most beneficial when it effectively covered the lowest possible HU ranges without inducing background artifacts. In conclusion, for further clinical application of the CDA method for quantitative research on knee joint OA in cats, 60 kV and 1.2 mAs settings are recommended.
Canine functional magnetic resonance imaging (fMRI) neurocognitive studies represent an emerging field that is advancing more gradually compared to progress in human fMRI research. Given the potential benefits of canine fMRI for veterinary, comparative, and translational research, this systematic review highlights significant findings, focusing on specific brain areas activated during task-related and resting state conditions in dogs. The review addresses the following question: “What brain areas in dogs are activated in response to various stimuli?”. Following PRISMA 2020 guidelines, a comprehensive search of PUBMED, Scopus, and Web of Knowledge databases identified 1833 studies, of which 46 met the inclusion criteria. The studies were categorized into themes concerning resting state networks and visual, auditory, olfactory, somatosensory, and multi-stimulations studies. In dogs, resting state networks and stimulus-specific functional patterns were confirmed as vital for brain function. These findings reveal both similarities and differences in the neurological mechanisms underlying canine and human cognition, enhance the understanding of neural activation pathways in dogs, expand the knowledge of social bonding patterns, and highlight the potential use of fMRI in predicting the suitability of dogs for assistance roles. Further studies are needed to further map human–canine similarities and identify the unique features of canine brain function. Additionally, implementing innovative human methods, such as combined fMRI–magnetic resonance spectroscopy (MRS), into canine neurocognitive research could significantly advance the field.
The aim of this paper is to present the use of the Walsh-Hadamard transform in the analysis of electromygraphic signals representing the uterine contractile activity in pigs. The Fourier spectral analysis is widely used in many biomedical applications. However, for binary time series the Walsh-Hadamard transform based on square or rectangular waves with peaks of ±1 is more accurate. Dominant normalized sequency can serve as a parameter describing the biomedical signal, which may have diagnostic importance.
Abstract Prevention and early diagnosis of forthcoming preterm labor is of vital importance in preventing child mortality. To date, our understanding of the coordination of uterine contractions is incomplete. Among the many methods of recording uterine contractility, electrohysterography (EHG) – the recording of changes in electrical potential associated with contraction of the uterine muscle, seems to be the most important from a diagnostic point of view. There is some controversy regarding whether EHG may identify patients with a high risk of preterm delivery. There is a need to check various digital signal processing techniques to describe the recorded signals. The study of synchronization of multivariate signals is important from both a theoretical and a practical point of view. Application of the Hilbert transformation seems very promising.
New trends in the economic systems management in the context of modern global challenges: collective monograph / scientific edited by M. Bezpartochnyi, in 2 Vol. // VUZF University of Finance, Business and Entrepreneurship. – Sofia: VUZF Publishing House “St. Grigorii Bogoslov”, 2020. – Vol. 1. – 309 p.
The analysis of the uterine contractility in the non-pregnant states provides information about physiological changes during the menstrual cycle. Spontaneous uterine activity was recorded by a micro-tip catheter (Millar Instruments, Inc. USA). The purpose of this study was to compare the results from autoregressive modeling and fast Fourier transform. These methods are commonly used for power spectral analysis. We analyzed 31 uterine contraction signals. Dominant frequency was the test statistics. We did not find statistically significant differences (nonparametric Wilcoxon test for paired samples) between results obtained by means of these two different methods of power spectral analysis.
The aim of the present study was to determine the dependency between the state of dentition and the birth weight of the pupils of the third grade of primary schools in Lublin. The subjects of the examination were 150 aged 9 year and 8 months +/- 3.9 months (76 girls and 74 boys). Clinical and questionnaire tests and a statistical analysis were carried out. The obtained results and literature review seem to allow for the following conclusions: there is a relationship between the sum of dmfs and DMFs indexes and the advancement of the carious process and OHI--S--with increment of birth weight and decrement of OHI--S index the sum of dmfs and DMFs indexes decreased; there was no correlation between disturbances of teeth mineralization and the birth weight.
In the original article [...]
The dataset contains computed tomography (CT) images of head horses with annotations of 12 segments corresponding to areas with teeth. Imaged animals: 49 horses. Measured animal features such as surface area, and volume are included. The study was supported by the National Science Centre, Poland as a part of the projectMiniatura 6 No 2022/06/X/ST6/00431.
The dataset contains computed tomography (CT) images of head horses with annotations of 12 segments corresponding to areas with teeth. Imaged animals: 49 horses. Measured animal features such as surface area, and volume are included. The study was supported by the National Science Centre, Poland as a part of the projectMiniatura 6 No 2022/06/X/ST6/00431.
Background: Febrile seizures are a common form of convulsions in childhood, with poorly known cellular mechanisms. The objective of this pioneering study was to provide qualitative and quantitative ultrastructural research on the large neuronal perikarya in the cerebellar dentate nucleus (DN), using an experimental model of hyperthermia-induced seizures (HSs), comparable to febrile seizures in children. Methods: The study used young male Wistar rats, divided into experimental and control groups. The HSs were evoked by a hyperthermic water bath at 45 °C for 4 min for four consecutive days. Specimens (1 mm3) collected from the DN were routinely processed for transmission electron microscopy studies. Results: The ultrastructure of the large neurons in the DN affected by hyperthermic stress showed variously pronounced lesions in the perikarya, including total cell disintegration. The most pronounced neuronal lesions exhibited specific morphological signs of aponecrosis, i.e., dark cell degeneration (‘dark neurons’). In close vicinity to the ‘dark neurons’, the aponecrotic bodies were found. The findings of this qualitative ultrastructural study correspond with the results of the morphometric analysis of the neuronal perikarya. Conclusions: Our results may constitute interesting comparative material for similar submicroscopic observations on large DN neurons in HS morphogenesis and, in the future, may help to find potential treatment targets to prevent febrile seizures or reduce recurrent seizures in children.
In equine surface electromyography (sEMG), challenges related to the reliability and interpretability of data arise, among other factors, from methodological differences, including signal processing and analysis. The aim of this study is to demonstrate the filtering–induced changes in basic signal features in relation to the balance between signal loss and noise attenuation. Raw sEMG signals were collected from the quadriceps muscle of six horses during walk, trot, and canter and then filtered using eight filtering methods with varying cut–off frequencies (low–pass at 10 Hz, high–pass at 20 Hz and 40 Hz, and bandpass at 20–450 Hz, 40–450 Hz, 7–200 Hz, 15–500 Hz, and 30–500 Hz). For each signal variation, signal features—such as amplitude, root mean square (RMS), integrated electromyography (iEMG), median frequency (MF), and signal–to–noise ratio (SNR)—along with signal loss metrics and power spectral density (PSD), were calculated. High–pass filtering at 40 Hz and bandpass filtering at 40–450 Hz introduced significant filtering–induced changes in signal features while providing full attenuation of low–frequency noise contamination, with no observed differences in signal loss between these two methods. Other filtering methods led to only partial attenuation of low–frequency noise, resulting in lower signal loss and less consistent changes across gaits in signal features. Therefore, filtering–induced changes should be carefully considered when comparing signal features from studies using different filtering approaches. These findings may support cross-referencing in equine sEMG research related to training, rehabilitation programs, and the diagnosis of musculoskeletal diseases, and emphasize the importance of applying standardized filtering methods, particularly with a high–pass cut–off frequency set at 40 Hz.
In human medicine, computer-aided diagnosis (CAD) is increasingly employed for screening, identifying, and monitoring early endoscopic signs of various diseases. However, its potential—despite proven benefits in human healthcare—remains largely underexplored in equine veterinary medicine. This study aimed to quantify endoscopic signs of pharyngeal lymphoid hyperplasia (PLH) as digital data and to assess their effectiveness in CAD of PLH in comparison and in combination with clinical data reflecting respiratory tract disease. Endoscopic images of the pharynx were collected from 70 horses clinically assessed as either healthy or affected by PLH. Digital data were extracted using an object detection-based processing technique and first-order statistics (FOS). The data were transformed using linear discriminant analysis (LDA) and classified with the random forest (RF) algorithm. Classification metrics were then calculated. When considering digital and clinical data, high classification performance was achieved (0.76 accuracy, 0.83 precision, 0.78 recall, and 0.76 F1 score), with the highest importance assigned to selected FOS features: Number of Objects and Neighbors, and Tracheal Auscultation. The proposed protocol of digitizing standard respiratory tract diagnostic methods provides effective discrimination of PLH grades, supporting the clinical value of CAD in veterinary medicine and paving the way for further research in digital medical diagnostics.
Objectives: Periapical periodontitis, which is a periodontal dysfunction, is a current clinical problem. Due to the frequency of occurrence and the adverse effects they cause, they are considered a social disease. They require detailed diagnostics to implement appropriate treatment. Mathematical calculations based on data obtained from radiological images used in routine clinical practice may help differentiate the forms of periodontitis. This study aimed to evaluate the areas affected by periodontitis in comparison to the healthy tissues of the periapical area. Methods: The study analyzed texture components using the gray-level co-occurrence matrix (GLCM) and the gray-level run-length matrix (GRLM) on an orthopantomogram (OPG) from 50 patients with clinically confirmed periodontitis treated at the Department of Maxillofacial and Plastic Surgery, University of Bialystok. Texture analysis was performed on defined regions of interest (ROIs) to distinguish diseased from healthy tissues. We employed four classification algorithms to assess model performance. Results: The data set included 50 patients, with 76 cases of periodontitis and 50 healthy ROIs. The reference standard was clinical diagnosis confirmed by two specialist doctors. The best-performing algorithm achieved an AUC of 98%. Conclusions: The obtained results showed significant statistical differences in the inflamed regions compared to the control, which may aid in diagnosing and selecting the treatment method for periodontitis.
The dataset contains magnetic resonance (MR) images of non-small lung cancer, categorized into two groups: adenocarcinoma (ADC, 24 patients, with 75 images) and squamous cell carcinoma (SCC, 20 patients, with 80 images). Measured features, including sample entropy, fuzzy entropy, permutation entropy, dispersion entropy, and distribution entropy, are examined across five scales. Directory structure:* data/ - numpy arrays corresponding to MRI images (128x128 pixels) +arrays are named: o_<number_image>_<slice_of_image>.png * oryg/ - numpy arrays corresponding to original MRI images +arrays are named: o_<number_image>_<slice_of_image>.png * MRI_slice_load.py code for data loading and calculating features* database.xlsx - a .xlsx file with two columns ['FileName', 'Group'] corresponding to MRI images (128x128 pixels) and group annotations (adenocarcinoma (ADC) and squamous cell carcinoma (SCC))* database_features.xlsx - a .xlsx file with three sheets of filtration ('Normalize', 'Median') and measured features in 5 scales (range 0-4) of every image: + No. + FileName + Group + Sample Entropy (SampEn) + Fuzzy Entropy (FuzzEn) + Dispersion Entropy (DispEn) + Distribution Entropy (DistEn) + Permutation Entropy (PermEn) Licence:The dataset is licensed under the Creative Commons Attribution 4.0 International LicenseTo view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
The dataset contains magnetic resonance (MR) images of non-small lung cancer, categorized into two groups: adenocarcinoma (ADC, 24 patients, with 75 images) and squamous cell carcinoma (SCC, 20 patients, with 80 images). Measured features, including sample entropy, fuzzy entropy, permutation entropy, dispersion entropy, and distribution entropy, are examined across five scales. Directory structure:* data/ - numpy arrays corresponding to MRI images (128x128 pixels) +arrays are named: o_<number_image>_<slice_of_image>.png * oryg/ - numpy arrays corresponding to original MRI images +arrays are named: o_<number_image>_<slice_of_image>.png * MRI_slice_load.py code for data loading and calculating features* database.xlsx - a .xlsx file with two columns ['FileName', 'Group'] corresponding to MRI images (128x128 pixels) and group annotations (adenocarcinoma (ADC) and squamous cell carcinoma (SCC))* database_features.xlsx - a .xlsx file with three sheets of filtration ('Normalize', 'Median') and measured features in 5 scales (range 0-4) of every image: + No. + FileName + Group + Sample Entropy (SampEn) + Fuzzy Entropy (FuzzEn) + Dispersion Entropy (DispEn) + Distribution Entropy (DistEn) + Permutation Entropy (PermEn) Licence:The dataset is licensed under the Creative Commons Attribution 4.0 International LicenseTo view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Background: In the treatment of dentofacial deformities, miniplates and screws made of titanium and its alloys (Ti6Al4V) are currently used for osteosynthesis of bone segments, which is due to the high biocompatibility of these materials. Despite the unquestionable advantages of titanium implants, there is an ongoing discussion about their potential negative impact on the human body, both at the implantation site and systemically. This study aimed to assess the influence of titanium fixations (miniplates and screws) on the texture and to identify the texture features that vary in the surrounding bone tissue. Methods: The orthopantomograms were obtained from 20 patients who were treated at the Department of Maxillofacial and Plastic Surgery, University of Bialystok. Regions of Interest (ROIs) of bone tissue surrounding titanium fixations in the maxilla and mandible were annotated using separate masks and compared to healthy areas of the same structures in the same patients. The images were independently filtered using Mean, Median, and Laplacian Sharpening filters, followed by analysis of the texture parameters obtained through methods such as First-Order Statistics (FOS), the Gray-Level Co-occurrence Matrix (GLCM), Neighbouring Gray Tone Difference Matrix (NGTDM), Gray-Level Dependence Matrix (GLDM), Gray-Level Run Length Matrix (GLRLM), and Gray-Level Size Zone Matrix (GLSZM). Results: The results showed that FOS, GLCM, and GLDM provide the most informative features for quantitative assessment of the areas around titanium fixations, and that smoothing filters reduce measurement noise and artifacts. Conclusions: The findings confirm that texture analysis can support the diagnosis of structural alterations in the bone surrounding titanium fixations, in both the maxilla and mandible.
Texture analysis is a foundational approach in imaging studies and demonstrates excellent diagnostic performance, with radiomic analysis being the most widely used method. New approaches to texture analysis continue to be developed. However, magnetic resonance imaging (MRI)–based radiomics studies for identifying histological subtypes of lung cancer remain scarce. This study aimed to improve the efficiency of the computer–aided non–invasive diagnosis of non–small cell lung cancer (NSCLC) by supplementing the statistical approaches to MRI image texture analysis with entropy–based methods. The study included 31 patients with NSCLC, categorized into two histological groups containing 12 patients (75 images) with adenocarcinoma (ADC) and 19 patients (79 images) with squamous cell carcinoma (SCC). A total of 154 MRI images were annotated using 154 regions of interest (ROIs). ROIs were extracted, filtered using normalize and median filtrations, and analyzed using standard statistical approaches and novel entropy–based methods. Texture features were selected using Select From Model (SFM) protocol and the classified using k–Nearest Neighbors (kNN), Support Vector Machines (SVM), and Logistic Regression (LR), separately. After 5–fold stratified cross–validation, the LR algorithm achieved the highest classification performance (0.75 accuracy and 0.78 presision) on the combined statistical and entropy–based texture features extracted from MRI images after median filtration. The proposed protocol presented higher efficiency than protocols that worked only on the statistical texture features on unfiltered or normalize filtered MRI images; therefore, it may be suggested for further research on the computer–aided diagnosis of NSCLC histological subtypes.
I am interested in international research collaborations involving biomedical data analysis, algorithm development, machine learning–based …