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Breast tissue deformation has recently gained interest in various medical applications. The recovery of large deformations caused by gravity or compression loads and image registration is a non-trivial task. The most effective tool for breast cancer visualisation is Magnetic Resonance Imaging (MRI). However, for MRI scans the patient is in a prone position with the breast placed in signal enhancement coils, while other procedures, i.e. surgery, PET-CT (Positron Emission Tomography fused with Computer Tomography) are performed with the patient in a supine position. The need therefore arises to estimate the large breast deformations caused by natural body movement during examinations or surgery. There is no doubt that a patient's breast in both positions has a different shape and that this influences relationships between intra-breast structures. In this work, we present the fundamentals of a method for transformation of breast images based on Finite Element Methods (FEMs). This 2D model uses the simplest constitutive tissue description, which makes it easily applicable and fast. According to the Jaccard Index, the average accuracy obtained is 95%, the lowest is 87%, and the highest is 99%. The model parameter set is proposed for six different breast size classes, covering the whole population. The algorithm provides reliable breast images in a supine position in a few simple steps.

Purpose: In clinical practice, motor development in infants is assessed subjectively. Many researchers propose objective methods, which have numerous limitations, by attaching markers or sensors to the child’s limbs. The purpose of this study is to attempt to develop objectified numerical indices to describe the limb movements of infants without interfering with spontaneous activity. Methods: 20-minute video recordings of three infants’ movements who were purposively selected from 51 subjects were included in the study. The procedure of automatic calculation of head position time in 3 positions was applied. Movement features were determined to allow for the delineation of coefficients describing the movement in numerical values. Results: Presented parameters describe three infant’s movement aspects: quality (strength), distribution of postural tonus and asymmetry in relation to head position, described as four independent values. Estimated parameters variability over time was weighted up according to expert observations. The presented method is a direct reflection of infants' observation, currently performed by highly educated and experienced therapists. Conclusions: The interpretability and usefulness of the presented parameters were proved. All parameters estimation is fully automated. The conducted research is a prelude to future work related to creating an objective and repeatable tool, initially monitoring and ultimately supporting early diagnosis for differentiating normal and abnormal motor development.

Invasive or uncomfortable procedures especially during healthcare trigger emotions. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of changes to a situation responding to a situation. The presented study aimed to focus on the analysis of the individual’s affective state. The results reflect the excitation expressed by the subjects’ statements collected with psychological questionnaires. The research group consisted of 49 participants (22 women and 25 men). The measurement protocol included acquiring the electrodermal activity signal, cardiac signals, and accelerometric signals in three axes. Subjective measurements were acquired for affective state using the JAWS questionnaires, for cognitive skills the DST, and for verbal fluency the VFT. The physiological and psychological data were subjected to statistical analysis and then to a machine learning process using different features selection methods (JMI or PCA). The highest accuracy of the kNN classifier was achieved in combination with the JMI method (81.63%) concerning the division complying with the JAWS test results. The classification sensitivity and specificity were 85.71% and 71.43%.

As a result of the constantly growing number of people in the World, disabilities became one of the most important aspects related to human health. According to the World Health Organization, a disabled person is “anyone who affects a reduction in performance or fitness in the construction or functioning of the body in terms of psychological, physical or anatomical.” On a global scale, the main cause of acquired motor disability is ischemic stroke. Therefore, it is essential to look for solutions that will help people with disabilities to rejoice as much independence and health as possible. For this purpose, the library databases of universities in Poland, units and scientific institutes from Poland, as well as the most popular databases—PubMed and Google Scholar were searched. As a result of the queries, 1057 records were obtained, which, according to the adopted criteria, only 24 were selected for further analysis. The main focus of the presented study was on issues related to supporting both diagnostics and therapy. Methods such as the use of basic devices for drainage and uprightization, walking trainers and multimodal platforms, biomechanical devices—exoskeletons and the use of virtual reality in the rehabilitation process were addressed.

The analysis of the patient’s psychophysiological condition is one of the key elements of properly conducted therapy. In the therapeutic tasks special attention is paid to monitoring the patient’s condition. Our method proposed a robust for segmentation errors (based on median value) and an easily applied method for assessing the thermal profile of the face and its usage in psychophysiological state observation. The obtained results suggest that the central part of the face provides sufficient information to discriminate between an active and a non active individual, even during minor physical effort.

Ultra-widefield fluorescein angiography (UWFA) is an emerging imaging modality used to characterise pathologies in the retinal vasculature, such as microaneurysms (MAs) and vascular leakages. Despite its potential value for diagnosis and disease screening, objective quantitative assessment of retinal pathologies by UWFA is currently limited because laborious manual processing is required. In this report, we describe a geometrical method for uneven brightness compensation inherent to UWFA imaging technique. The correction function is based on the geometrical eyeball shape, therefore it is fully automated and depends only on pixel distance from the center of the imaged retina. The method’s performance was assessed on a database containing 256 UWFA images with the use of several image quality measures that show the correction method improves image quality. The method is also compared to the commonly used CLAHE approach and was also employed in a pilot study for vascular segmentation, giving a noticeable improvement in segmentation results. Therefore, the method can be used as an image preprocessing step in retinal UWFA image analysis.

One of the most common methods in breast cancer radiotherapy planning is Magnetic Resonance Imaging (MRI). It is also used for patient evaluation during treatment because of its sensitivity and lack of ionizing radiation. During each imaging session a patient position can be different and inaccuracies can occur. In this case it is very difficult to compare two image sets originating from different patient examination. The main goals of this work were to implement an algorithm, based on affine transformation with Mutual Information as the quality factor of images match and the method based on the Navier-Lame equation for elastic image co-registration. The rigid transformation is used for the preliminary processing, and the non-rigid transformation allows for successful co-registration of both image sets. Our results were evaluated visually, and the MI indices were calculated. These algorithms allowed for image co-registration in different imaging sessions during the course of treatment.

The exact delineation of tumour boundaries is of utmost importance in the planning of cancer therapy, either surgery or pre- or post-operative radiation treatment. In the case of breast cancer one of the most advanced modalities is magnetic resonance imaging (MRI). Although MRI scans provide wealth of information about the structure of a tumour and the surrounding tissues, the data obtained represent the patient in a prone position, with breast, in a coil while surgery is performed in a supine position, on lying breast. There is no doubt that a patient's breast in both positions has a different shape and that this influences the intra-breast relations. Our present preliminary study introduces a simple breast model developed from prone images. The model should be built rapidly and by a simple procedure, based only on essential structures, and the goal is to prove its usefulness in treatment planning.

According to recent studies, breast cancer is the most frequent women's cancer around the world. Fast and precise diagnosis and personalized treatment procedures are crucial in the face of the growing number of patients. One of the most informative and at the same time non-evasive diagnostic techniques is based on Magnetic Resonance Imaging (MRI). This examination is performed basically in prone position due to better visualisation of breast tissues as well as the proper magnetic coil force this position. On the other hand supplementary imaging techniques (e.g. Computer Tomography (CT) or Positron Emission Tomography (PET)) and, what is the most important, surgery are performed in supine position. Since changing position from prone to supine leads to breast deformation, we propose an algorithm based on finite element method (FEM) to model it. We compare our results with model created in ANSYS.

Image segmentation is often used in medical image processing. This crucial task can affect all results obtained from the further steps of image analysis. In nuclear medicine emission tomography imaging, where acquired and reconstructed images contain large noise and high blurring level, segmentation and tumour boundaries delineation can be very challenging task. Many from already existing image segmentation methods are based on clustering. In this work, we have tested and implemented a few clustering based methods. We have mainly focused on k-means related algorithms to evaluate and compare their accuracy. In this group we have chosen k-means algorithm, k-medoid clustering, and fuzzy C-means (FCM) method. Results for all methods were verified using the gold standard obtained from anatomical image co-registered and emission tomography dataset. Numerical values of both datasets matching were calculated using the Jaccard index. Results were compared with standard segmentation algorithm based on fixed threshold (standardized uptake value - SUV with threshold 2.5), which is a commonly used standard in clinical practice and also with previously implemented and verified methods (including game theoretical algorithm). For all tested methods we have obtained very similar results, comparable to SUV 2.5 threshold method but worse than the game theoretic method.

Breast tissue deformation has recently gained interest in various medical application. The recovery of large deformation caused by gravity or compression loads and image registration is non-trivial task. The need arise to estimate large breast deformation, which can mimic natural body movement caused by examinations or surgery. Finite element methods (FEM) have been widely applied in this field. In this work we present the current breast deformation modelling trend. The meaningful applications and essentials examinations are described. The modelling software and basic techniques are presented.

Increasing number of people are suffering from cancer these days. The highest incidence among women is breast cancer. One of the main sources of diagnostic information is Magnetic Resonance Imaging (MRI). Another one is Positron Emission Tomography with Computer Thomography (PET-CT). Both examinations take place in different patient positions (prone and supine respectively). The only way to obtain complete diagnostic information is to bring MRI images into PET-CT space and compare both. Our preliminary studies focus on creating a simple, deformable finite element model of the breast. Proposed algorithm allows to obtain breast model in supine position, created from MRI images obtained in prone position.

Segmentation and delineation of tumour boundaries are important and difficult step in emission tomography imaging, where acquired and reconstructed images presents large noise and a blurring level. Several methods have been previously proposed and can be used in single photon emission tomography (SPECT) or positron emission tomography (PET) imaging. Some of them relies on the standard uptake value (SUV) used in PET imaging. Presented approach can be used in both (SPECT and PET) modalities and it is based on support vector machines (SVM) classification system. System has been tested on standard phantom, widely used for testing the emission tomography devices. Results are presented for two classifiers SVM and DLDA.

Recent studies indicates that cellular cancerogenesis is connected with microRNA (miRNA) expression levels. In particular, different miRNAs can serve as classification features for distinguishing different cancer types. This paper provides classification attempt using miRNA isoforms with 3’-end modification as classification features. microRNA samples was obtained using next generation sequencing method. Data was preprocessed using authors algorithm developed in R. Support Vector Mashines and Partial Least Square methods were used to classify two types of miRNA samples: Follicular Adenoma and Follicular Thyroid Cancer. It was observed that only several miRNA modified isoforms were identified as the most differentiating for analyzed samples. Obtained results indicate that miRNA 3’-end modifications can be used as cancer tissue classification features.

In recent years a water pipe gains popularity among Polish young people. Unfortunately, young people use it to burn not only tobacco, but also other addictive and psychoactive substances.The aim of the study was to assess the phenomenon of using a water pipe to smoke psychoactive substances by young people.The study was conducted based on the author's guestionnaire. The anonymous test was conducted throughout Poland, selecting randomly ten high schools and two universities from each province.In the survey participated 19 037 people from the region of Poland. 83% of them were high school pupils aged 15-19 years and 17% of them were students aged 20-25 years. 38% of volunteers at least once in their life have smoked a water pipe, and 22% have smoked it during last 30 days. Apart from tobacco 38% of the smoking people used also, apart from the tobacco designet for smoking in a water pipe, psychoactive substances. The most common is marihuana. 16% of school pupils and 17% of students used marihuana, 14% and 15% hashish, 8% and 11% crack, and 11% and 15% boosters.Presented data from all-Poland research conducted among school pupils and students illustrate only occasional and sporadic experience in drug use and the outright conclusions about the scale of the permanent use of psychoactive substances can not be drawn. A social aspect of a water pipe smoking is favourable to an alcohol consumption and lots of with different psychoactive substances. Due to the scale of the problem it is advisable to do in-depth research about the described phenomenon. It is also advisable to conduct more intensive programs to promote healthy behaviors and increase more attention to the reasons of such behaviour among young people.