Application of infrared thermography in computer aided diagnosis

The invention of thermography, in the 1950s, posed a formidable problem to the research community: What is the relationship between disease and heat radiation captured with Infrared (IR) cameras? The research community responded with a continuous effort to find this crucial relationship. This effort was aided by advances in processing techniques, improved sensitivity and spatial resolution of thermal sensors. However, despite this progress fundamental issues with this imaging modality still remain. The main problem is that the link between disease and heat radiation is complex and in many cases even non-linear. Furthermore, the change in heat radiation as well as the change in radiation pattern, which indicate disease, is minute. On a technical level, this poses high requirements on image capturing and processing. On a more abstract level, these problems lead to inter-observer variability and on an even more abstract level they lead to a lack of trust in this imaging modality. In this review, we adopt the position that these problems can only be solved through a strict application of scientific principles and objective performance assessment. Computing machinery is inherently objective; this helps us to apply scientific principles in a transparent way and to assess the performance results. As a consequence, we aim to promote thermography based Computer-Aided Diagnosis (CAD) systems. Another benefit of CAD systems comes from the fact that the diagnostic accuracy is linked to the capability of the computing machinery and, in general, computers become ever more potent. We predict that a pervasive application of computers and networking technology in medicine will help us to overcome the shortcomings of any single imaging modality and this will pave the way for integrated health care systems which maximize the quality of patient care

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Dielectric-technique-assisted breast cancer surgery

Introduction. In conservative breast cancer surgery, removing all carcinogenic lesions is a sound basis for achieving therapeutic success. The cleanness of the cutting margin is usually assessed after surgery and any residual cancerous tissue that are found, require further surgery. A simple and cheap assessment method is presented which can be used during surgery, the dielectric cancer probe (DCP), which allows the surgeon to take fast decisions on whether to extend incisions. Method. The DCP method consists of measuring and then differentiating between the electrical properties (permeability and conductivity) of healthy and cancerous breast tissue. A polyethylene strip is used to separate the examined tissue from the instrument probe and the procedure is carried out by the operating surgeon. Results. This method was found to allow correct identification of cancerous breast tissue based on taking 189 samples from 70 patients using the intraoperative hand probe. The sensitivity and specificity were respectively 87% and 99%. Conclusions. The DCP is a high sensitivity method and is totally safe for the patient and can be used intraoperatively. The device is an aid to the surgeon for differentiating between healthy and cancerous breast tissue. Its widespread use should reduce the number of reoperations in the conservative surgical management of breast cancer patients.

Application-Specific Broadband Antennas for Microwave Medical Imaging

The goal of this work is the introduction of efficient antenna structures on the basis of the requirement of different microwave imaging methods; i.e. quantitative and qualitative microwave imaging techniques. Several criteria are proposed for the evaluation of single element antenna structures for application in microwave imaging systems. The performance of the proposed antennas are evaluated in simulation and measurement scenarios

Revisión de algoritmos, métodos y técnicas para la detección de UAVs y UAS en aplicaciones de audio, radiofrecuencia y video

Unmanned Aerial Vehicles (UAVs), also known as drones, have had an exponential evolution in recent times due in large part to the development of technologies that enhance the development of these devices. This has resulted in increasingly affordable and better-equipped artifacts, which implies their application in new fields such as agriculture, transport, monitoring, and aerial photography. However, drones have also been used in terrorist acts, privacy violations, and espionage, in addition to involuntary accidents in high-risk zones such as airports. In response to these events, multiple technologies have been introduced to control and monitor the airspace in order to ensure protection in risk areas. This paper is a review of the state of the art of the techniques, methods, and algorithms used in video, radiofrequency, and audio-based applications to detect UAVs and Unmanned Aircraft Systems (UAS). This study can serve as a starting point to develop future drone detection systems with the most convenient technologies that meet certain requirements of optimal scalability, portability, reliability, and availability.Los vehículos aéreos no tripulados, conocidos también como drones, han tenido una evolución exponencial en los últimos tiempos, debido en gran parte al desarrollo de las tecnologías que potencian su desarrollo, lo cual ha desencadenado en artefactos cada vez más asequibles y con mejores prestaciones, lo que implica el desarrollo de nuevas aplicaciones como agricultura, transporte, monitoreo, fotografía aérea, entre otras. No obstante, los drones se han utilizado también en actos terroristas, violaciones a la privacidad y espionaje, además de haber producido accidentes involuntarios en zonas de alto riesgo de operación como aeropuertos. En respuesta a dichos eventos, aparecen tecnologías que permiten controlar y monitorear el espacio aéreo, con el fin de garantizar la protección en zonas de riesgo. En este artículo se realiza un estudio del estado del arte de la técnicas, métodos y algoritmos basados en video, en análisis de sonido y en radio frecuencia, para tener un punto de partida que permita el desarrollo en el futuro de un sistema de detección de drones, con las tecnologías más propicias, según los requerimientos que puedan ser planteados con las características de escalabilidad, portabilidad, confiabilidad y disponibilidad óptimas

Health Risk Measurement and Assessment Technology: Current State and Future Prospect

With accelerated technologies, different kinds of health technology devices have been provided to customers that continuously record bio and vital signals. Some of these products are wearable that can be used all day long and during sleeping time. Due to the wearability feature and continuous recording, a vast amount of data can be achieved and analyzed. The recorded data are usually shared with a cloud to implement comprehensive analysis methods where deep and machine learning algorithms play the main role. Finally, they can assess some health factors of the customer and most likely predict future health risks. This chapter shall review the role of the clinical scanners and their valuable data in risk detection, more portable modalities, home-used commercial devices, and emerging techniques which are so potent for future home-used health risks analysis. In the end, we conclude the state-of-the-art and provide our vision about the future of health risk analysis

A CNN based Multifaceted Signal Processing Framework for Heart Rate Proctoring Using Millimeter Wave Radar Ballistocardiography

The recent pandemic has refocused the medical world's attention on the diagnostic techniques associated with cardiovascular disease. Heart rate provides a real-time snapshot of cardiovascular health. A more precise heart rate reading provides a better understanding of cardiac muscle activity. Although many existing diagnostic techniques are approaching the limits of perfection, there remains potential for further development. In this paper, we propose MIBINET, a convolutional neural network for real-time proctoring of heart rate via inter-beat-interval (IBI) from millimeter wave (mm-wave) radar ballistocardiography signals. This network can be used in hospitals, homes, and passenger vehicles due to its lightweight and contactless properties. It employs classical signal processing prior to fitting the data into the network. Although MIBINET is primarily designed to work on mm-wave signals, it is found equally effective on signals of various modalities such as PCG, ECG, and PPG. Extensive experimental results and a thorough comparison with the current state-of-the-art on mm-wave signals demonstrate the viability and versatility of the proposed methodology. Keywords: Cardiovascular disease, contactless measurement, heart rate, IBI, mm-wave radar, neural networkComment: 13 pages, 10 figures, Submitted to Elsevier's Array Journa

Biomedical research and aerospace technology applications

The accomplishments and activities of an Applications Team for biomedical subjects are presented. The team attempts to couple the technological problems and requirements in medicine with the relevant aerospace technology and, in particular, NASA-generated technology. The team actively engages in identifying these problems through direct contact with medical staffs or problem originators. The identification and specification of medical problems is followed by a search for technology which may be relevant to solutions to these problems

Investigation of late time response analysis for security applications

The risk of armed attack by individual’s intent on causing mass casualties against soft targets, such as transport hubs continues. This has led to an increased need for a robust, reliable and accurate detection system for concealed threat items. This new system will need to improve upon existing detection systems including portal based scanners, x-ray scanners and hand held metal detectors as these all suffer from drawbacks of limited detection range and relatively long scanning times. A literature appraisal has been completed to assess the work being undertaken in the relevant field of Concealed Threat Detection (CTD). From this Ultra-Wide Band (UWB) radar has been selected as the most promising technology available for CTD at the present. UWB radar is provided by using Frequency Modulated Continuous Waves (FMCW) from laboratory test equipment over a multi gigahertz bandwidth. This gives the UWB radar the ability to detect both metallic and dielectric objects. Current published results have shown that it is possible to use the LTR technique to detect and discriminate both single objects isolated in air and multiple objects present within the same environment. A Vector Network Analyser (VNA) has been used to provide the Ultra-Wide Band (UWB) Frequency Modulated Continuous Wave (FMCW) radar signal required for the LTR technique. This thesis presents the application of the Generalized Pencil-of-Function (GPOF), Dual Tree Wavelet Transform (DTWT) and the Continuous Wavelet Transform (CWT), both real and complex valued, in Late Time Response (LTR) security analysis to produce a viable detection algorithm. Supervised and unsupervised Artificial Neural Networks (ANN) have been applied to develop a successful classification scheme for Concealed Threat Detection (CTD) in on body security screening. Signal deconvolution and other techniques have been applied in post processing to allow for extraction of the LTR signal from the scattered return. Data vectorization has been applied to the extracted LTR signal using an unsupervised learning based ANN to prepare data for classification. Classification results for both binary threat/non-threat classifiers and a group classifier are presented. The GPOF method presented true positive classification results approaching 72% with wavelet based methods offering between 98% and 100%

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis