A tangible user interface or TUI connects physical objects and digital interfaces. It is more interactive and interesting for users than a classic graphic user interface. This article presents a descriptive overview of TUI’s real-world applications sorted into ten main application areas—teaching of traditional subjects, medicine and psychology, programming, database development, music and arts, modeling of 3D objects, modeling in architecture, literature and storytelling, adjustable TUI solutions, and commercial TUI smart toys. The paper focuses on TUI’s technical solutions and a description of technical constructions that influences the applicability of TUIs in the real world. Based on the review, the technical concept was divided into two main approaches: the sensory technical concept and technology based on a computer vision algorithm. The sensory technical concept is processed to use wireless technology, sensors, and feedback possibilities in TUI applications. The image processing approach is processed to a marker and markerless approach for object recognition, the use of cameras, and the use of computer vision platforms for TUI applications.

Alcohol intoxication is a significant phenomenon, affecting many social areas, including work procedures or car driving. Alcohol causes certain side effects including changing the facial thermal distribution, which may enable the contactless identification and classification of alcohol-intoxicated people. We adopted a multiregional segmentation procedure to identify and classify symmetrical facial features, which reliably reflects the facial-temperature variations while subjects are drinking alcohol. Such a model can objectively track alcohol intoxication in the form of a facial temperature map. In our paper, we propose the segmentation model based on the clustering algorithm, which is driven by the modified version of the Artificial Bee Colony (ABC) evolutionary optimization with the goal of facial temperature features extraction from the IR (infrared radiation) images. This model allows for a definition of symmetric clusters, identifying facial temperature structures corresponding with intoxication. The ABC algorithm serves as an optimization process for an optimal cluster’s distribution to the clustering method the best approximate individual areas linked with gradual alcohol intoxication. In our analysis, we analyzed a set of twenty volunteers, who had IR images taken to reflect the process of alcohol intoxication. The proposed method was represented by multiregional segmentation, allowing for classification of the individual spatial temperature areas into segmentation classes. The proposed method, besides single IR image modelling, allows for dynamical tracking of the alcohol-temperature features within a process of intoxication, from the sober state up to the maximum observed intoxication level.

The segmentation of hepatic vessels is crucial for liver surgical planning. It is also a challenging task because of its small diameter. Hepatic vessels are often captured in images of low contrast and resolution. Our research uses filter enhancement to improve their contrast, which helps with their detection and final segmentation. We have designed a specific fusion of the Ranking Orientation Responses of Path Operators (RORPO) enhancement filter with a raw image, and we have compared it with the fusion of different enhancement filters based on Hessian eigenvectors. Additionally, we have evaluated the 3D U-Net and 3D V-Net neural networks as segmentation architectures, and have selected 3D V-Net as a better segmentation architecture in combination with the vessel enhancement technique. Furthermore, to tackle the pixel imbalance between the liver (background) and vessels (foreground), we have examined several variants of the Dice Loss functions, and have selected the Weighted Dice Loss for its performance. We have used public 3D Image Reconstruction for Comparison of Algorithm Database (3D-IRCADb) dataset, in which we have manually improved upon the annotations of vessels, since the dataset has poor-quality annotations for certain patients. The experiments demonstrate that our method achieves a mean dice score of 76.2%, which outperforms other state-of-the-art techniques.

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.

Medical image segmentation plays an indispensable role in the identification of articular cartilage, tibial and femoral bones from magnetic resonance imaging (MRI). There are various image segmentation strategies that can be used to identify the knee structures of interest. Among the most popular are the methods based on non-hierarchical clustering, including the algorithms K-means and fuzzy C-means (FCM). Although these algorithms have been used in many studies for regional image segmentation, they have two essential drawbacks that limit their performance and accuracy of segmentation. Firstly, they rely on a precise selection of initial centroids, which is usually conducted randomly, and secondly, these algorithms are sensitive enough to image noise and artifacts, which may deteriorate the segmentation performance. Based on such limitations, we propose, in this study, two novel alternative metaheuristic hybrid schemes: non-hierarchical clustering, driven by a genetic algorithm, and Particle Swarm Optimization (PSO) with fitness function, which utilizes Kapur’s entropy and statistical variance. The goal of these optimization elements is to find the optimal distribution of centroids for the knee MR image segmentation model. As a part of this study, we provide comprehensive testing of the robustness of these novel segmentation algorithms upon the image noise generators. This includes Gaussian, Speckle, and impulsive Salt and Pepper noise with dynamic noise to objectively report the robustness of the proposed segmentation strategies in contrast with conventional K-means and FCM. This study reveals practical applications of the proposed algorithms for articular cartilage extraction and the consequent classification performance of early osteoarthritis based on segmentation models and convolutional neural networks (CNN). Here, we provide a comparative analysis of GoogLeNet and ResNet 18 with various hyperparameter settings, where we achieved 99.92% accuracy for the best classification configuration for early cartilage loss recognition.

Retinopathy of prematurity (ROP) remains a significant cause of childhood blindness despite advancements in neonatal care. Identifying the plus form of ROP, characterized by dilated and tortuous blood vessels, is crucial for timely intervention. This study introduces an intelligent segmentation–classification system for the autonomous detection of retinal blood vessels and the classification of ROP plus form. Utilizing Clarity RetCam 3 images, our system employs morphological image processing and convolutional neural networks (CNNs) for segmentation and classification, respectively. Testing on a dataset of premature infants’ retinal images demonstrates high segmentation accuracy (median = 0.974) and superior classification performance (accuracy = 0.975, sensitivity = 0.950, and specificity = 1). In addition, the system exhibits versatility, with successful segmentation in adult retinal images from public databases. These findings highlight the system’s potential for clinical use in retinal vessel identification, feature extraction, and ROP plus form classification. The proposed system is capable of effectively identifying retinal blood vessels from both alternatives including adult and premature born retinal images with a high accuracy in contrast to related studies. Thus, this system has the potential to be used in clinical practice for retinal blood vessels’ identification, retinal blood vessels’ feature extraction, and ROP plus form classification.