IEEE CBMS 2017 - Amphitheater I

GT7 Medical Imaging I

teosavv — Fri, 19 May 2017 13:10:23 +0000

Saturday, June 24, 2017 - 11:30 to 12:30

Christos P Loizou
Cyprus University of Technology (CY)

Daniela Giordano
Universita Degli Studi di Catania (IT)

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

Alessandro Bria

Claudio Marrocco

Mario Molinara

Francesco Tortorella
University of Cassino and South Lazio (IT)

Adrian Galdran

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
Technion – Israel Institute of Technology (IL)

Emil Saucan

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
Cyprus University of Technology (CY)

Christos Papacharalambous

Giorgos Samaras

Efthivoulos Kyriakou
Frederick University (CY)

Takis Kasparis
Cyprus University of Technology (CY)

Marios Pantziaris

Eleni Eracleous

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
Universita Degli Studi di Catania (IT)

Carmelo Pino

Isaak Kavasidis

Concetto Spampinato

Massimo Di Pietro

Renata Rizzo

Anna Scuderi

Rita Barone

Closing

teosavv — Tue, 16 May 2017 14:21:20 +0000

Saturday, June 24, 2017 - 15:45 to 16:15

Amphitheater I

GT9: Games Robotics and smart technologies

teosavv — Tue, 16 May 2017 14:19:45 +0000

Saturday, June 24, 2017 - 14:45 to 15:45

Amphitheater I

Efthyvoulos Kyriacou

Evdokimos Konstantinidis
Aristotle University of Thessaloniki (GR)

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

Evdokimos Konstantinidis
Aristotle University of Thessaloniki (GR)

J. A. Diniz

Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

V Charisis

S Hadjidimitriou

M. Stadtschnitzer

P. Fagerberg

Ioannis Ioakimidis

K Dimitropoulos

N Grammalidis

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

Andrew Nicolaides

Alexandra Constantinou

Maura Griffin

Christos P Loizou
Cyprus University of Technology (CY)

Marios S. Pattichis

Hamed Nasrabadi

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.

Andreas Menychtas

Charalampos Doukas

Panayiotis Tsanakas

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
Aristotle University of Thessaloniki (GR)

George Arfaras

Ioannis Xygonakis

Panagiotis Kartsidis
Aristotle University of Thessaloniki (GR)

Niki Pandria
Aristotle University of Thessaloniki (GR)

Kyriaki Rafailia Kavazidi
Lab of Medical Physics The Medical School Aristotle University of Thessaloniki

Alexander Astaras
American College of Thessaloniki (GR)

Nicolas Foroglou

Konstantinos Polyzoidis

Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

Recorded Keynote from AIME Conf

teosavv — Tue, 16 May 2017 14:15:34 +0000

Saturday, June 24, 2017 - 14:00 to 14:45

Amphitheater I

Charalampos Bratsas
Open Knowledge Greece (GR)

Kenneth J. Barker

Lunch

teosavv — Tue, 16 May 2017 14:10:30 +0000

Saturday, June 24, 2017 - 13:15 to 14:00

Amphitheater I

Amphitheater II

Keynote Speech

teosavv — Tue, 16 May 2017 14:09:47 +0000

Most medical treatments are designed for a specific disease and for the “average patient". This "one-size-fits-all-approach," may be successful for some patients but not for others. Precision medicine, sometimes known as "personalized medicine" is an innovative approach to disease prevention and treatment that takes into account differences in people’s genes, environments and lifestyles. In January 2015, President Obama launched the Precision Medicine Initiative (PMI), a bold new research effort to revolutionize how we improve health and treat disease, empowering health care providers to tailor treatment and prevention strategies to individuals’ unique characteristics. Advances in precision medicine have already led to powerful new discoveries and several new FDA-approved treatments that are tailored to specific characteristics of individuals, such as a person’s genetic makeup, or the genetic profile of an individual’s tumor. Patients with a variety of cancers routinely undergo molecular testing as part of patient care, enabling physicians to select treatments that improve chances of survival and reduce exposure to adverse effects. The PMI seeks to identify genetically-based drivers of disease in order to develop new, more effective treatments. There is a component in this equation that relates to Public Health that is very seldom discussed or incorporated into an individual’s health record: Geomedicine. This presentation will discuss how this field will change the future model of Healthcare.

Saturday, June 24, 2017 - 12:30 to 13:15

Amphitheater I

Giuseppe Conti
-

Anne Wilson
-

Luis Kun
Editor in Chief - Springer (USA)

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

teosavv — Tue, 16 May 2017 14:05:47 +0000

Saturday, June 24, 2017 - 09:00 to 11:00

Amphitheater I

Panagiotis Antoniou
Aristotle University of Thessaloniki (GR)

Daphne Economou
University of Westminster (UK)

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
Salumedia Tecnologias (SP)

Diego Giunta

Santiago Hors-Fraile

Minna Isomursu

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
Salumedia Tecnologias (SP)

Estefania Guisado-Fernandez

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
University of Westminster (UK)

Hykoush Asaturyan

E. Louise Thomas

Rhys Mould

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

Luis Fernandez-Luque
Qatar Computing Research Institute (QA)

Meghna Singh
Qatar Computing Research Institute (QA)

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
Qatar Computing Research Institute (QA)

Luis Fernandez-Luque
Qatar Computing Research Institute (QA)

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
University of Westminster (UK)

Miriam Dwek

Claire Roberston

Elliott Bradley

Thanos Kounenis

Mohammad Ramezanian

Nathan Bell

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
Aristotle University of Thessaloniki (GR)

Octavio Rivera-Romero

Maria Karagianni

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
Aristotle University of Thessaloniki (GR)

Vasiliki Zilidou
Aristotle University of Thessaloniki (GR)

Anastasios Siountas
Aristotle University of Thessaloniki (GR)

Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

Conference Dinner

teosavv — Tue, 16 May 2017 14:00:37 +0000

Friday, June 23, 2017 - 20:00

Amphitheater I

Keynote Speech

teosavv — Tue, 16 May 2017 14:00:05 +0000

Friday, June 23, 2017 - 18:00 to 18:45

Amphitheater I

Constantinos Pattichis
University of Cyprus (CY)

Dimitris Fotiadis
University of Ioannina (GR)

ST4 - Computer-aided and robotic endoscopy systems

teosavv — Tue, 16 May 2017 13:56:00 +0000

Friday, June 23, 2017 - 16:45 to 18:00

Amphitheater I

Dimitris Iakovidis
University of Thessaly (GR)

Anastasios Koulaouzidis

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

Fons van der Sommen
Eindhoven Univeristy of Technology (NL)

Anne-Fre Swager

Svitlana Zinger

Wouter Curvers

Erik Schoon

Jacques Bergman

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
Iowa State University (USA)

Wallapak Tavanapong

Johnny Wong

Piet C. de Groen

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
Norwegian University of Science and Technology (NO)

Sule Yildirim

Marius Pedersen

Oistein Hovde

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

Dimitris Iakovidis
University of Thessaly (GR)

Alexandros Karargyris

Gastone Ciuti

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
Brunel Unevirsity London (UK)

Yongmin Li

Xiaohui Liu