Automated assessment and tracking of human body thermal variations using unsupervised clustering

The presented approach addresses a review of the overheating that occurs during radiological examinations, such as magnetic resonance imaging, and a series of thermal experiments to determine a thermally suitable fabric material that should be used for radiological gowns. Moreover, an automatic system for detecting and tracking of the thermal fluctuation is presented. It applies hue-saturated-value-based kernelled k-means clustering, which initializes and controls the points that lie on the region-of-interest (ROI) boundary. Afterward, a particle filter tracks the targeted ROI during the video sequence independently of previous locations of overheating spots. The proposed approach was tested during experiments and under conditions very similar to those used during real radiology exams. Six subjects have voluntarily participated in these experiments. To simulate the hot spots occurring during radiology, a controllable heat source was utilized near the subject’s body. The results indicate promising accuracy for the proposed approach to track hot spots. Some approximations were used regarding the transmittance of the atmosphere, and emissivity of the fabric could be neglected because of the independence of the proposed approach for these parameters. The approach can track the heating spots continuously and correctly, even for moving subjects, and provides considerable robustness against motion artifact, which occurs during most medical radiology procedures

spinfortec2022 : Tagungsband zum 14. Symposium der Sektion Sportinformatik und Sporttechnologie der Deutschen Vereinigung für Sportwissenschaft (dvs), Chemnitz 29. - 30. September 2022

Dieser Tagungsband enthält die Beiträge aller Vorträge und Posterpräsentationen des 14. Symposiums der Sektion Sportinformatik und Sporttechnologie der Deutschen Vereinigung für Sportwissenschaft (dvs) an der Technischen Universität Chemnitz (29.-30. September 2022). Mit dem Ziel, das Forschungsfeld der Sportinformatik und Sporttechnologie voranzubringen, wurden knapp 20 vierseitige Beiträge eingereicht und in den Sessions Informations- und Feedbacksysteme im Sport, Digitale Bewegung: Datenerfassung, Analyse und Algorithmen sowie Sportgeräteentwicklung: Materialien, Konstruktion, Tests vorgestellt.This conference volume contains the contributions of all oral and poster presentations of the 14th Symposium of the Section Sport Informatics and Engineering of the German Association for Sport Science (dvs) at Chemnitz University of Technology (September 29-30, 2022). With the goal of advancing the research field of sports informatics and sports technology, nearly 20 four-page papers were submitted and presented in the sessions Information and Feedback Systems in Sport, Digital Movement: Data Acquisition, Analysis and Algorithms, and Sports Equipment Development: Materials, Construction, Testing

Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion

Neurosurgery is a demanding medical discipline that requires a complex interplay of several neuroimaging techniques. This allows structural as well as functional information to be recovered and then visualized to the surgeon. In the case of tumor resections this approach allows more fine-grained differentiation of healthy and pathological tissue which positively influences the postoperative outcome as well as the patient's quality of life. In this work, we will discuss several approaches to establish thermal imaging as a novel neuroimaging technique to primarily visualize neural activity and perfusion state in case of ischaemic stroke. Both applications require novel methods for data-preprocessing, visualization, pattern recognition as well as regression analysis of intraoperative thermal imaging. Online multimodal integration of preoperative and intraoperative data is accomplished by a 2D-3D image registration and image fusion framework with an average accuracy of 2.46 mm. In navigated surgeries, the proposed framework generally provides all necessary tools to project intraoperative 2D imaging data onto preoperative 3D volumetric datasets like 3D MR or CT imaging. Additionally, a fast machine learning framework for the recognition of cortical NaCl rinsings will be discussed throughout this thesis. Hereby, the standardized quantification of tissue perfusion by means of an approximated heating model can be achieved. Classifying the parameters of these models yields a map of connected areas, for which we have shown that these areas correlate with the demarcation caused by an ischaemic stroke segmented in postoperative CT datasets. Finally, a semiparametric regression model has been developed for intraoperative neural activity monitoring of the somatosensory cortex by somatosensory evoked potentials. These results were correlated with neural activity of optical imaging. We found that thermal imaging yields comparable results, yet doesn't share the limitations of optical imaging. In this thesis we would like to emphasize that thermal imaging depicts a novel and valid tool for both intraoperative functional and structural neuroimaging

High-speed imaging in fluids

High-speed imaging is in popular demand for a broad range of experiments in fluids. It allows for a detailed visualization of the event under study by acquiring a series of image frames captured at high temporal and spatial resolution. This review covers high-speed imaging basics, by defining criteria for high-speed imaging experiments in fluids and to give rule-of-thumbs for a series of cases. It also considers stroboscopic imaging, triggering and illumination, and scaling issues. It provides guidelines for testing and calibration. Ultra high-speed imaging at frame rates exceeding 1 million frames per second is reviewed, and the combination of conventional experiments in fluids techniques with high-speed imaging techniques are discussed. The review is concluded with a high-speed imaging chart, which summarizes criteria for temporal scale and spatial scale and which facilitates the selection of a high-speed imaging system for the applicatio

Computer Vision Tools for Rodent Monitoring

RÉSUMÉ Les rongeurs sont régulièrement utilisés dans les expériences et la recherche biomédicale. Ceci est dû entre autres aux caractéristiques qu’ils partagent avec les humains, au faible coût et la facilité de leur entretien, et à la brièveté de leur cycle de vie. La recherche sur les rongeurs implique généralement de longues périodes de surveillance et de suivi. Quand cela est fait manuellement, ces tâches sont très fastidieuses et possiblement erronées. Ces tâches impliquent un technicien pour noter la position ou le comportement du rongeur en chaque instant. Des solutions de surveillance et de suivi automatique ont été mises au point pour diminuer la quantité de travail manuel et permettre de plus longues périodes de surveillance. Plusieurs des solutions proposées pour la surveillance automatique des animaux utilisent des capteurs mécaniques. Même si ces solutions ont été couronnées de succès dans leurs tâches prévues, les caméras vidéo sont toujours indispensables pour la validation ultérieure. Pour cette raison, il est logique d'utiliser la vision artificielle comme un moyen de surveiller et de suivre les rongeurs. Dans cette thèse, nous présentons des solutions de vision artificielle à trois problèmes connexes concernant le suivi et l’observation de rongeurs. La première solution consiste en un procédé pour suivre les rongeurs dans un environnement biomédical typique avec des contraintes minimales. La méthode est faite de deux phases. Dans la première phase, une technique de fenêtre glissante fondée sur trois caractéristiques est utilisée pour suivre le rongeur et déterminer sa position approximative dans le cadre. La seconde phase utilise la carte d’arrêts et un système d'impulsions pour ajuster les limites de la fenêtre de suivi aux contours du rongeur. Cette solution présente deux contributions. La première contribution consiste en une nouvelle caractéristique, les histogrammes d’intensité qui se chevauchent. La seconde contribution consiste en un nouveau procédé de segmentation qui utilise une soustraction d’arrière-plan en ligne basée sur les arrêts pour segmenter les bords du rongeur. La précision de suivi de la solution proposée est stable lorsqu’elle est appliquée à des rongeurs de tailles différentes. Il est également montré que la solution permet d'obtenir de meilleurs résultats qu’une méthode de l'état d’art. La deuxième solution consiste en un procédé pour détecter et identifier trois comportements chez les rongeurs dans des conditions biomédicales typiques. La solution utilise une méthode basée sur des règles combinée avec un système de classificateur multiple pour détecter et classifier le redressement, l’exploration et l’état statique chez un rongeur. La solution offre deux contributions. La première contribution consiste en une nouvelle méthode pour détecter le comportement des rongeurs en utilisant l'image historique du mouvement. La seconde contribution est une nouvelle règle de fusion pour combiner les estimations de plusieurs classificateurs de machine à vecteur du support. La solution permet d'obtenir un taux de précision de reconnaissance de 87%. Ceci est conforme aux exigences typiques dans la recherche biomédicale. La solution se compare favorablement à d'autres solutions de l’état de l’art. La troisième solution comprend un algorithme de suivi qui a le même comportement apparent et qui maintient la robustesse de l’algorithme de CONDENSATION. L'algorithme de suivi simplifie les opérations et réduit la charge de calcul de l'algorithme de CONDENSATION tandis qu’il maintient une précision de localisation semblable. La solution contribue à un nouveau dispositif pour attribuer les particules, à un certain intervalle de temps, aux particules du pas de temps précédent. Ce système réduit le nombre d'opérations complexes requis par l'algorithme de CONDENSATION classique. La solution contribue également à un procédé pour réduire le nombre moyen de particules générées au niveau de chaque pas de temps, tout en maintenant le même nombre maximal des particules comme dans l'algorithme de CONDENSATION classique. Finalement, la solution atteint une accélération 4,4 × à 12 × par rapport à l'algorithme de CONDENSATION classique, tout en conservant à peu près la même précision de suivi.----------ABSTRACT Rodents are widely used in biomedical experiments and research. This is due to the similar characteristics that they share with humans, to the low cost and ease of their maintenance and to the shortness of their life cycle, among other reasons. Research on rodents usually involves long periods of monitoring and tracking. When done manually, these tasks are very tedious and prone to error. They involve a technician annotating the location or the behavior of the rodent at each time step. Automatic tracking and monitoring solutions decrease the amount of manual labor and allow for longer monitoring periods. Several solutions have been provided for automatic animal monitoring that use mechanical sensors. Even though these solutions have been successful in their intended tasks, video cameras are still indispensable for later validation. For this reason, it is logical to use computer vision as a means to monitor and track rodents. In this thesis, we present computer vision solutions to three related problems concerned with rodent tracking and observation. The first solution consists of a method to track rodents in a typical biomedical environment with minimal constraints. The method consists of two phases. In the first phase, a sliding window technique based on three features is used to track the rodent and determine its coarse position in the frame. The second phase uses the edge map and a system of pulses to fit the boundaries of the tracking window to the contour of the rodent. This solution presents two contributions. The first contribution consists of a new feature, the Overlapped Histograms of Intensity (OHI). The second contribution consists of a new segmentation method that uses an online edge-based background subtraction to segment the edges of the rodent. The proposed solution tracking accuracy is stable when applied to rodents with different sizes. It is also shown that the solution achieves better results than a state of the art tracking algorithm. The second solution consists of a method to detect and identify three behaviors in rodents under typical biomedical conditions. The solution uses a rule-based method combined with a Multiple Classifier System (MCS) to detect and classify rearing, exploring and being static. The solution offers two contributions. The first contribution is a new method to detect rodent behavior using the Motion History Image (MHI). The second contribution is a new fusion rule to combine the estimations of several Support Vector Machine (SVM) Classifiers. The solution achieves an 87% recognition accuracy rate. This is compliant with typical requirements in biomedical research. The solution also compares favorably to other state of the art solutions. The third solution comprises a tracking algorithm that has the same apparent behavior and that maintains the robustness of the CONDENSATION algorithm. The tracking algorithm simplifies the operations and reduces the computational load of the CONDENSATION algorithm while conserving similar tracking accuracy. The solution contributes to a new scheme to assign the particles at a certain time step to the particles of the previous time step. This scheme reduces the number of complex operations required by the classic CONDENSATION algorithm. The solution also contributes a method to reduce the average number of particles generated at each time step, while maintaining the same maximum number of particles as in the classic CONDENSATION algorithm. Finally, the solution achieves 4.4× to 12× acceleration when compared to the classical CONDENSATION algorithm, while maintaining roughly the same tracking accuracy

NASA Tech Briefs, February 1996

Topics covered include: Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Life Sciences; Books and Reports

Ultrafast photochemistry produces superbright short-wave infrared dots for low-dose in vivo imaging

12 p.-5 fig.Optical probes operating in the second near-infrared window (NIR-II, 1,000-1,700 nm), where tissues are highly transparent, have expanded the applicability of fluorescence in the biomedical field. NIR-II fluorescence enables deep-tissue imaging with micrometric resolution in animal models, but is limited by the low brightness of NIR-II probes, which prevents imaging at low excitation intensities and fluorophore concentrations. Here, we present a new generation of probes (Ag2S superdots) derived from chemically synthesized Ag2S dots, on which a protective shell is grown by femtosecond laser irradiation. This shell reduces the structural defects, causing an 80-fold enhancement of the quantum yield. PEGylated Ag2S superdots enable deep-tissue in vivo imaging at low excitation intensities (<10 mW cm-2) and doses (<0.5 mg kg-1), emerging as unrivaled contrast agents for NIR-II preclinical bioimaging. These results establish an approach for developing superbright NIR-II contrast agents based on the synergy between chemical synthesis and ultrafast laser processing.Authors thank Dr A. Benayas (CICECO, U. Aveiro, Portugal), Prof G. Lifante and Prof J. García Sole (UAM) for helpful discussions. This work has been founded by Ministerio de Economı́a y Competitividad-MINECO (MAT2017-83111R and MAT2016-75362-C3-1-R) and the Comunidad de Madrid (B2017/BMD-3867 RENIM-CM) co-financed by European Structural and Investment Fund. D.M.-G. thanks UCM-Santander for a predoctoral contract (CT17/17-CT18/17). We thank the staff at the ICTS-National Centre for Electron Microscopy at the UCM for the help in the electron microscopy studies and C.M. at the beamline BL22-CLAESS of the Spanish synchrotron ALBA for his help in the XANES experiments. We also thank J.G.I at the Ultrafast Laser Laboratory at UCM for his help and fruitful discussion. Y.S. acknowledges the support from the China Scholarship Council (CSC File No. 201806870023). Additional funding was provided by the European Commission Horizon 2020 project NanoTBTech, the Fundación para la Investigación Biomédica del Hospital Universitario Ramón y Cajal project IMP18_38 (2018/0265). Ajoy K. Kar and Mark D. Mackenzie acknowledge support from the UK Engineering and Physical Sciences Research Council (Project CHAMP, EP/M015130/1). C. Jacinto thanks the financial support of the Brazilian agencies: CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) through the grants: Projeto Universal Nr. 431736/2018-9 and Scholarship in Research Productivity 1C under the Nr. 304967/20181; FINEP (Financiadora de Estudos e Projetos) through the grants INFRAPESQ-11 and INFRAPESQ-12; FAPEAL (Fundação de Amparo à Pesquisa do Estado de Alagoas) grant Nr. 1209/2016. H. D. A. Santos was supported by a graduate studentship from CNPq and by a sandwich doctoral program (PDSE-CAPES) developed at Universidad Autonoma de Madrid, Spain, Project Nr. 88881/2016-01.Peer reviewe

Ag2S nanodots for advanced biological applications through Luminescence Thermometry and Fluorescence Images .

Tesis doctoral inédita cotutelada por la Universidade Federal de Alagoas de Brasil y la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Física de Materiales. Fecha de lectura: 13-03-202

Small business innovation research. Abstracts of completed 1987 phase 1 projects

Non-proprietary summaries of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA in the 1987 program year are given. Work in the areas of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robotics, computer sciences, information systems, spacecraft systems, spacecraft power supplies, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered