GT7 Medical Imaging I

Christos P Loizou's picture
Christos P Loizou
Cyprus University of Technology (CY)
Daniela Giordano's picture
Daniela Giordano
Universita Degli Studi di Catania (IT)
Conference room
Session time
Saturday, June 24, 2017 - 11:30 to 12:30
Illumination correction by dehazing for retinal vessel segmentation
Abstract: 
"Assessment of retinal vessels is fundamental for the diagnosis of many disorders such as heart diseases, diabetes and hypertension. The imaging of retina using advanced fundus camera has become a standard in computer-assisted diagnosis of opthalmic disorders. Modern cameras produce high quality color digital images, but during the acquisition process the light reflected by the retinal surface generates a luminosity and contrast variation.
Benedetta Savelli's picture
Benedetta Savelli
Alessandro Bria's picture
Alessandro Bria
Claudio Marrocco's picture
Claudio Marrocco
Mario Molinara's picture
Mario Molinara
Francesco Tortorella's picture
Francesco Tortorella
University of Cassino and South Lazio (IT)
Adrian Galdran's picture
Adrian Galdran
Aurelio Campilho's picture
Aurelio Campilho
A differential geometry approach for change detection in medical images.
Abstract: 
Change detection is of paramount importance in medical imaging, serving as a non-invasive quantifiable powerful tool in diagnosis and in assessment of the outcome of treatment of tumors. We present a new quantitative method for detecting changes in volumetric medical data and in clustering of anatomical structures, based on assessment of volumetric distortions that are required in order to deform a test three-dimensional medical dataset segment onto its previously-acquired reference, or a given prototype in the case clustering.
Alexander Naitsat's picture
Alexander Naitsat
Technion – Israel Institute of Technology (IL)
Emil Saucan's picture
Emil Saucan
Yehoshua Zeevi's picture
Yehoshua Zeevi
Brain Image and Lesions Registration and 3D Reconstruction in Dicom MRI Images
Abstract: 
During a human brain MRI acquisition the resulting image is formed out of 2D slices. The slices must then be aligned and reconstructed to provide a 3-dimensional (3D) visualization of the brain volume. We propose in this work, an integrated system for the register ion and 3D reconstruction of DICOM MRI images and lesions of the brain acquired from multiple sclerosis (MS) subjects at two different time intervals (time 0 (T0) and time 1 (T1)). The system facilitates the follow up of the MS disease development and will aid the doctor to accurately manage the follow up of the disease.
Christos P Loizou's picture
Christos P Loizou
Cyprus University of Technology (CY)
Christos Papacharalambous's picture
Christos Papacharalambous
Giorgos Samaras's picture
Giorgos Samaras
Efthivoulos Kyriakou's picture
Efthivoulos Kyriakou
Frederick University (CY)
Takis Kasparis's picture
Takis Kasparis
Cyprus University of Technology (CY)
Marios Pantziaris's picture
Marios Pantziaris
Eleni Eracleous's picture
Eleni Eracleous
Constantinos Pattichis's picture
Constantinos Pattichis
University of Cyprus (CY)
An Eye Tracker–based Computer System to Support Oculomotor and Attention Deficit Investigations
Abstract: 
Eye tracking is a non-invasive procedure to acquire eye-gaze data. The accuracy offered by the new eye tracking technologies gives to physicians and scientists a great opportunity to employ eye trackers to perform quantitative assessment of eye movements for diagnostic and rehabilitation purposes. However, eye trackers do not support physicians in their analysis, as they typically lack specific software solutions tailored to the diseases under investigation.
Daniela Giordano's picture
Daniela Giordano
Universita Degli Studi di Catania (IT)
Carmelo Pino's picture
Carmelo Pino
Isaak Kavasidis's picture
Isaak Kavasidis
Concetto Spampinato's picture
Concetto Spampinato
Massimo Di Pietro's picture
Massimo Di Pietro
Renata Rizzo's picture
Renata Rizzo
Anna Scuderi's picture
Anna Scuderi
Rita Barone's picture
Rita Barone

GT9: Games Robotics and smart technologies

Efthyvoulos Kyriacou's picture
Efthyvoulos Kyriacou
Evdokimos Konstantinidis's picture
Evdokimos Konstantinidis
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Saturday, June 24, 2017 - 14:45 to 15:45
On supporting Parkinson's Disease patients: The i-PROGNOSIS Personalized Game Suite design approach
Abstract: 
The use of serious games in health care interventions sector has grown rapidly in the last years, however, there is still a gap in the understanding on how these types of interventions are used for the management of the Parkinson Disease (PD), in particular. Targeting intelligent early detection and intervention in PD area, the Personalized Game Suite (PGS) design process approach is presented as part of the H2020 i-PROGNOSIS project that introduces the integration of different serious games in a unified platform (i.e., ExerGames, DietaryGames, EmoGames, and Handwriting/Voice Games).
S. B. Dias's picture
S. B. Dias
Evdokimos Konstantinidis's picture
Evdokimos Konstantinidis
Aristotle University of Thessaloniki (GR)
J. A. Diniz's picture
J. A. Diniz
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
V Charisis's picture
V Charisis
S Hadjidimitriou's picture
S Hadjidimitriou
M. Stadtschnitzer's picture
M. Stadtschnitzer
P. Fagerberg's picture
P. Fagerberg
Ioannis Ioakimidis's picture
Ioannis Ioakimidis
K Dimitropoulos's picture
K Dimitropoulos
N Grammalidis's picture
N Grammalidis
Leontios Hadjileontiadis's picture
Leontios Hadjileontiadis
Aristotle University of Thessaloniki (GR)
Carotid Bifurcation Plaque Stability Estimation based on Motion Analysis
Abstract: 
"Through this study we are presenting the initial steps towards a real time motion analysis system to predict the stability of carotid bifurcation plaques. The analysis is performed on B-mode video loops. Loops are analyzed in order to follow systoly and diastoly sections of the cardiac cycle and trace the motion of plaques during these periods. We had created a system that applies Farneback’s optical flow estimation method in order to estimate the flow between consecutive frames or frames at a predefined interval.
Efthyvoulos Kyriacou's picture
Efthyvoulos Kyriacou
Andrew Nicolaides's picture
Andrew Nicolaides
Alexandra Constantinou's picture
Alexandra Constantinou
Maura Griffin's picture
Maura Griffin
Christos P Loizou's picture
Christos P Loizou
Cyprus University of Technology (CY)
Marios S. Pattichis's picture
Marios S. Pattichis
Hamed Nasrabadi's picture
Hamed Nasrabadi
Constantinos Pattichis's picture
Constantinos Pattichis
University of Cyprus (CY)
A Versatile Architecture for Building IoT Quantified-Self Applications
Abstract: 
The abundance of activity trackers and biosignal sensors as well as the evolution of IoT and communication technologies have considerably advanced the concept of Quantified-Self. Nowadays there are several frameworks and applications that realize the concept, focusing though strictly on specific areas, from daily use to professional activities such as sport and healthcare. This work proposes a versatile, cross-domain solution for building quantified-self applications exploiting the capacities for open-design, modularity and extensibility of the AGILE IoT gateway.
Charalampos Doukas's picture
Charalampos Doukas
Panayiotis Tsanakas's picture
Panayiotis Tsanakas
Ilias Maglogiannis's picture
Ilias Maglogiannis
University of Piraeus (GR)
Commercial BCI Control And Functional Brain Networks in Spinal Cord Injury: A Proof-of-Concept.
Abstract: 
Spinal Cord Injury (SCI), along with disability, results in changes of brain organization and structure. While sensorimotor networks of patients and healthy individuals share similar patterns, unique functional interactions have been identified in SCI networks. Brain-Computer Interfaces (BCIs) have emerged as a promising technology for movement restoration and rehabilitation of SCI patients. We describe an experimental methodology to combine high-resolution electroencephalography (EEG) for investigation of functional connectivity following SCI and non-invasive BCI control of robotic arms.
Alkinoos Athanasiou's picture
Alkinoos Athanasiou
Aristotle University of Thessaloniki (GR)
George Arfaras's picture
George Arfaras
Ioannis Xygonakis's picture
Ioannis Xygonakis
Panagiotis Kartsidis's picture
Panagiotis Kartsidis
Aristotle University of Thessaloniki (GR)
Niki Pandria's picture
Niki Pandria
Aristotle University of Thessaloniki (GR)
Kyriaki Rafailia Kavazidi's picture
Kyriaki Rafailia Kavazidi
Lab of Medical Physics The Medical School Aristotle University of Thessaloniki
Alexander Astaras's picture
Alexander Astaras
American College of Thessaloniki (GR)
Nicolas Foroglou's picture
Nicolas Foroglou
Konstantinos Polyzoidis's picture
Konstantinos Polyzoidis
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

ST3 - Immersive Personalized Patient Engagement; Computer Based Medical Intervention Systems

Panagiotis Antoniou's picture
Panagiotis Antoniou
Aristotle University of Thessaloniki (GR)
Daphne Economou's picture
Daphne Economou
University of Westminster (UK)
Conference room
Session time
Saturday, June 24, 2017 - 09:00 to 11:00
Detecting Gamification in Breast Cancer Apps: an automatic methodology for screening purposes
Abstract: 
Breast cancer is the most common cancer in women both in developed and developing countries. More than half of all cancer mobile application concern breast cancer. Gamification is widely used in mobile software applications created for health-related services. Current prevalence of gamification in breast cancer apps is unknown and detection must be manually performed. The purpose of this study is to describe and produce a tool allowing automatic detection of apps which contain gamification elements and thus empowering researchers to study gamification using large data samples.
Guido Giunti's picture
Guido Giunti
Salumedia Tecnologias (SP)
Diego Giunta's picture
Diego Giunta
Santiago Hors-Fraile's picture
Santiago Hors-Fraile
Minna Isomursu's picture
Minna Isomursu
Diana Karoseviciute's picture
Diana Karoseviciute
Connected Health in Multiple Sclerosis: a mobile applications review
Abstract: 
Multiple Sclerosis (MS) is an unpredictable, often disabling disease that can adversely affect any body function; this often requires persons with MS to be active patients who are able to self-manage. There are currently thousands of health applications available but it is unknown how many concern MS. We conducted a systematic review of all MS apps present in the most popular app stores (iTunes and Google Play store) on June 2016 to identify all relevant MS apps. After discarding non-MS related apps and duplicates, only a total of 25 MS apps were identified.
Guido Giunti's picture
Guido Giunti
Salumedia Tecnologias (SP)
Estefania Guisado-Fernandez's picture
Estefania Guisado-Fernandez
Brian Caulfield's picture
Brian Caulfield
A Framework for Morphological Feature Extraction of Organs from MR Images for Detection and Classification of Abnormalities
Abstract: 
In clinical practice, a misdiagnosis can lead to incorrect treatment, delayed treatment, or in some cases, no treatment at all; consequently, the condition of a patient may worsen to varying degrees, in some cases proving fatal. The accurate 3D reconstruction of organs, which is a pioneering tool of medical image computing (MIC) technology, plays a key role in computer aided diagnosis (CADx), whereby enabling medical professionals to perform enhanced analysis on a region of interest.
Barbara Villarini's picture
Barbara Villarini
University of Westminster (UK)
Hykoush Asaturyan's picture
Hykoush Asaturyan
E. Louise Thomas's picture
E. Louise Thomas
Rhys Mould's picture
Rhys Mould
Jimmy D Bell's picture
Jimmy D Bell
Visualization of Wearable Data and Biometrics for Analysis and Recommendations in Childhood Obesity
Abstract: 
Obesity is one of the major health risk factors behind the rise of non-communicable conditions. Understanding the factors influencing obesity is very complex since there are many variables that can affect the health behaviors leading to it. Nowadays, multiple data sources can be used to study health behaviors, such as wearable sensors for physical activity and sleep, social media, mobile and health data. In this paper we describe our experiences with the design of a dashboard for the visualization of actigraphy and biometric data from a childhood obesity camp in Qatar.
Michael Aupetit's picture
Michael Aupetit
Luis Fernandez-Luque's picture
Luis Fernandez-Luque
Qatar Computing Research Institute (QA)
Meghna Singh's picture
Meghna Singh
Qatar Computing Research Institute (QA)
Jaideep Srivastava's picture
Jaideep Srivastava
The 360QS Toolkit for Sleep and Physical Activity Analysis based on Wearables
Abstract: 
Sleep and physical activity are human behaviors that play a major role in our health. Poor sleep or lack of physical activity have been found to increase health risks and reduce quality of life. The rapid adoption and evolution of pervasive computing systems, both in the health and wellness domain, are creating a new data-intensive context in which we can learn about the sleep and physical activity patterns of individuals. In this paper we provide an overview of the toolkit we have developed to conduct research on personal health data about sleep and physical activity.
Meghna Singh's picture
Meghna Singh
Qatar Computing Research Institute (QA)
Luis Fernandez-Luque's picture
Luis Fernandez-Luque
Qatar Computing Research Institute (QA)
Jaideep Srivastava's picture
Jaideep Srivastava
PhytoCloud: A Gamified Mobile Web Application to Modulate Diet and Physical Activity of Women with Breast Cancer
Abstract: 
Breast cancer incidence and mortality rates vary geographically reflecting factors including regional and cultural differences in diet and lifestyle. There are numerous successful commercial mobile apps to help dieters control their diet and manage weight. However, such products are not suitable for people with special medical conditions that may require targeted dietary as well as motivational support.
Daphne Economou's picture
Daphne Economou
University of Westminster (UK)
Miriam Dwek's picture
Miriam Dwek
Claire Roberston's picture
Claire Roberston
Elliott Bradley's picture
Elliott Bradley
Thanos Kounenis's picture
Thanos Kounenis
Mohammad Ramezanian's picture
Mohammad Ramezanian
Nathan Bell's picture
Nathan Bell
Tayebeh Azimi's picture
Tayebeh Azimi
Towards evidence based m-health application design in cancer patient healthy lifestyle interventions.
Abstract: 
Cancer is one of the most prevalent diseases in Europe and the world. Significant correlations between dietary habits and cancer incidence and mortality have been confirmed by the literature. Physical activity habits are also directly implicated in the incidence of cancer. Lifestyle behaviour change may be benefited by using mobile technology to deliver health behaviour interventions. M-Health offers a promising cost-efficient approach to deliver en-masse interventions.
Panagiotis Antoniou's picture
Panagiotis Antoniou
Aristotle University of Thessaloniki (GR)
Octavio Rivera-Romero's picture
Octavio Rivera-Romero
Maria Karagianni's picture
Maria Karagianni
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Virtual scenarios for stealth assessment of the elderly; Perceptions and acceptance of technology-based health and wellness interventions.
Abstract: 
As adults get older, the risks are increasing on their health, such as chronic diseases, functional decline and geriatric syndromes which threaten their well-being. Technology has many features to support aging wellness enabling older people maintaining healthy and sociable as they grow. However, the needs of the elderly of the population are not always the same. Stealth assessment, coming from the educational domain, can assess such needs.
Panagiotis Antoniou's picture
Panagiotis Antoniou
Aristotle University of Thessaloniki (GR)
Vasiliki Zilidou's picture
Vasiliki Zilidou
Aristotle University of Thessaloniki (GR)
Anastasios Siountas's picture
Anastasios Siountas
Aristotle University of Thessaloniki (GR)
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

ST4 - Computer-aided and robotic endoscopy systems

Dimitris Iakovidis's picture
Dimitris Iakovidis
University of Thessaly (GR)
Anastasios Koulaouzidis's picture
Anastasios Koulaouzidis
Conference room
Session time
Friday, June 23, 2017 - 16:45 to 18:00
Improved Barrett's Cancer Detection in Volumetric Laser Endomicroscopy scans using Multiple-Frame Voting
Abstract: 
This paper explores the feasibility of using multi-frame analysis to increase the classification performance of machine learning methods for cancer detection in Volumetric Laser Endomicroscopy (VLE). VLE is a novel and promising modality for the detection of neoplasia in patients with Barett's Esophagus (BE). It produces hundreds of high-resolution, cross-sectional images of the esophagus and offers considerable advantages compared to current methods.
Alexandros Rikos's picture
Alexandros Rikos
Fons van der Sommen's picture
Fons van der Sommen
Eindhoven Univeristy of Technology (NL)
Anne-Fre Swager's picture
Anne-Fre Swager
Svitlana Zinger's picture
Svitlana Zinger
Wouter Curvers's picture
Wouter Curvers
Erik Schoon's picture
Erik Schoon
Jacques Bergman's picture
Jacques Bergman
Peter de With's picture
Peter de With
Real-Time Instrument Scene Detection in Screening GI Endoscopic Procedures
Abstract: 
Here we describe a new and effective real-time solution for detecting video segments showing an instrument used during diagnostic or therapeutic operations in endoscopic procedures. In addition, we present a new method to create training data: similarity-based data augmentation. This method automates most of the creation of a large training dataset and prevents extensive manual effort to collect and annotate training data by domain experts.
Chuanhai Zhang's picture
Chuanhai Zhang
Iowa State University (USA)
Wallapak Tavanapong's picture
Wallapak Tavanapong
Johnny Wong's picture
Johnny Wong
Piet C. de Groen's picture
Piet C. de Groen
Junghwan Oh's picture
Junghwan Oh
Sparse coded handcrafted and deep features for colon capsule video summarization
Abstract: 
Abstract—Capsule endoscopy, which uses a wireless camera to take images of the digestive track, is emerging as an alternative to traditional wired colonoscopy. A single examination produces a sequence of approximately 50,000 frames. These sequences are manually reviewed, which is time consuming and typically takes about 45–90 minutes and requires the undivided concentration of the reviewer. In this paper, we propose a novel capsule video summarization framework using sparse coding and dictionary learning in feature space.
Ahmed Mohammed's picture
Ahmed Mohammed
Norwegian University of Science and Technology (NO)
Sule Yildirim's picture
Sule Yildirim
Marius Pedersen's picture
Marius Pedersen
Oistein Hovde's picture
Oistein Hovde
Faouzi Cheikh's picture
Faouzi Cheikh
Visual Localization of Wireless Capsule Endoscopes Aided by Artificial Neural Networks
Abstract: 
Various modalities are used for the examination of the gastrointestinal (GI) tract. One such modality is Wireless Capsule Endoscopy (WCE), a non- invasive technique which consists of a swallowable color camera that enables the detection of GI pathology with only minimal patient discomfort. Currently, tracking of the capsule position is estimated in the 3D abdominal space, using radio-frequency (RF) triangulation.
George Dimas's picture
George Dimas
Dimitris Iakovidis's picture
Dimitris Iakovidis
University of Thessaly (GR)
Alexandros Karargyris's picture
Alexandros Karargyris
Gastone Ciuti's picture
Gastone Ciuti
Anastasios Koulaouzidis's picture
Anastasios Koulaouzidis
Retinal OCT Image Segmentation Using Fuzzy Histogram Hyperbolization and Continuous Max-Flow
Abstract: 
The segmentation of retinal layers is vital for tracking progress of medication and diagnosis of various eye diseases. To date many methods for the analysis exists, however the speckle noise and shadows of retinal blood vessel remains a challenge, with negative influence on the performance of segmentation algorithms. Previous attempts have been focused on image pre-processing or developing sophisticated models for segmentation to address this problem, but it still remains an area of active research.
Bashir Dodo's picture
Bashir Dodo
Brunel Unevirsity London (UK)
Yongmin Li's picture
Yongmin Li
Xiaohui Liu's picture
Xiaohui Liu

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