Ilias Maglogiannis

Mobile healthcare information management utilizing Cloud Computing and Android OS

Cloud Computing provides functionality for managing information data in a distributed, ubiquitous and pervasive manner supporting several platforms, systems and applications. This work presents the implementation of a mobile system that enables electronic healthcare data storage, update and retrieval using Cloud Computing. The mobile application is developed using Googles Android operating system and provides management of patient health records and medical images (supporting DICOM format and JPEG2000 coding). The developed system has been evaluated using the Amazons S3 cloud service. This article summarizes the implementation details and presents initial results of the system in practice.

Overview of Advanced Computer Vision Systems for Skin Lesions Characterization

During the last years, computer-vision-based diagnosis systems have been used in several hospitals and dermatology clinics, aiming mostly at the early detection of skin cancer, and more specifically, the recognition of malignant melanoma tumour. In this paper, we review the state of the art in such systems by first presenting the installation, the visual features used for skin lesion classification, and the methods for defining them. Then, we describe how to extract these features through digital image processing methods, i.e., segmentation, border detection, and color and texture processing, and we present the most prominent techniques for skin lesion classification. The paper reports the statistics and the results of the most important implementations that exist in the literature, while it compares the performance of several classifiers on the specific skin lesion diagnostic problem and discusses the corresponding findings.

The IEEE 802.11g standard for high data rate WLANs

Continuous WLAN public acceptance comes with increasing demand for provision of higher data rates. Building on this context, the IEEE published the IEEE 802.11g standard for providing data rates of up to 54 Mb/s at the 2.4 GHz band. This article presents the new features of IEEE 802.11g and, using an open source C++-based simulation tool, evaluates both the performance and effectiveness of these features compared to the older IEEE 802.11 standard versions.

Bringing IoT and Cloud Computing towards Pervasive Healthcare

Pervasive healthcare applications utilizing body sensor networks generate a vast amount of data that need to be managed and stored for processing and future usage. Cloud computing among with the Internet of Things (IoT) concept is a new trend for efficient managing and processing of sensor data online. This paper presents a platform based on Cloud Computing for management of mobile and wearable healthcare sensors, demonstrating this way the IoT paradigm applied on pervasive healthcare.

Emergency Fall Incidents Detection in Assisted Living Environments Utilizing Motion, Sound, and Visual Perceptual Components

This paper presents the implementation details of a patient status awareness enabling human activity interpretation and emergency detection in cases, where the personal health is threatened like elder falls or patient collapses. The proposed system utilizes video, audio, and motion data captured from the patients body using appropriate body sensors and the surrounding environment, using overhead cameras and microphone arrays. Appropriate tracking techniques are applied to the visual perceptual component enabling the trajectory tracking of persons, while proper audio data processing and sound directionality analysis in conjunction to motion information and subjects visual location can verify fall and indicate an emergency event. The postfall visual and motion behavior of the subject, which indicates the severity of the fall (e.g., if the person remains unconscious or patient recovers) is performed through a semantic representation of the patients status, context and rules-based evaluation, and advanced classification. A number of advanced classification techniques have been examined in the framework of this study and their corresponding performance in terms of accuracy and efficiency in detecting an emergency situation has been thoroughly assessed.

An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers

Abstract In recent years, computational diagnostic tools and artificial intelligence techniques provide automated procedures for objective judgments by making use of quantitative measures and machine learning techniques. In this paper we propose a Support Vector Machines (SVMs) based classifier in comparison with Bayesian classifiers and Artificial Neural Networks for the prognosis and diagnosis of breast cancer disease. The paper provides the implementation details along with the corresponding results for all the assessed classifiers. Several comparative studies have been carried out concerning both the prognosis and diagnosis problem demonstrating the superiority of the proposed SVM algorithm in terms of sensitivity, specificity and accuracy.

Patient Fall Detection using Support Vector Machines

This paper presents a novel implementation of a patient fall detection system that may be used for patient activity recognition and emergency treatment. Sensors equipped with accelerometers are attached on the body of the patients and transmit patient movement data wirelessly to the monitoring unit. The methodology of support Vector Machines is used for precise classification of the acquired data and determination of a fall emergency event. Then a context-aware server transmits video from patient site properly coded according to both patient and network status. Evaluation results indicate the high accuracy of the classification method and the effectiveness of the proposed implementation.

Face detection and recognition of natural human emotion using Markov random fields

This paper presents an integrated system for emotion detection. In this research effort, we have taken into account the fact that emotions are most widely represented with eye and mouth expressions. The proposed system uses color images and it is consisted of three modules. The first module implements skin detection, using Markov random fields models for image segmentation and skin detection. A set of several colored images with human faces have been considered as the training set. A second module is responsible for eye and mouth detection and extraction. The specific module uses the HLV color space of the specified eye and mouth region. The third module detects the emotions pictured in the eyes and mouth, using edge detection and measuring the gradient of eyes’ and mouth’s region figure. The paper provides results from the system application, along with proposals for further research.

Digital cities of the future: Extending @home assistive technologies for the elderly and the disabled

In the digital city of the future there is the vision of seamless virtual and physical access for every home and between each home and the workplace, as well as critical city infrastructure such as the post office, the bank, hospitals, transportation systems, and other entities. This paper provides an overview of technical and other issues in extending at home (@home) assistive technologies for the elderly and the disabled. The paper starts by giving a vision of what this city is supposed to look like and how a human is to act, navigate and function in it. A framework for extending assistive technologies is proposed that considers individuals belonging to special groups of interest and locations other than their home. Technology has already reached the state of ubiquitous and pervasive sensor devices measuring everything, from temperature to human behavior. Implanting intelligence into and connecting such devices will be of immense use in preventive healthcare, security in industrial installations, greater energy efficiency, and numerous other applications. The paper reviews enabling technologies that exist and focuses on healthcare applications that support a longer and higher quality of life at home for the elderly and the disabled. It discusses intelligent platforms involving agents, context-aware and location-based services, and classification systems that enable advanced monitoring and interpretation of patient status and optimization of the environment to improve medical assessments. The paper concludes with a discussion of some of the challenges that exist in extending @home assistive technologies to @city assistive technologies.

Region of Interest Coding Techniques for Medical Image Compression

We have provided an overview of state-of-the-art ROI coding techniques applied to medical images. These techniques are classified according to the image type they apply to; thus the first class includes ROI coding schemes developed for two-dimensional (2-D) still medical images whereas the second class consists of ROI coding in the case of volumetric images. In the third class, a prototype ROI encoder for compression of angiogram video sequences is presented. ROI coding preserves image quality in diagnostically critical regions by performing advanced image compression, enabling better image examination and addressing issues regarding image handling and transmission in telemedicine systems. The mapping of the ROI from the spatial domain to the wavelet domain is dependent on the used wavelet filters and it is simplified for rectangular and circular regions. Therefore, ROI coding is considered quite important in distributed and networked electronic healthcare.