Adaptive emergency scenery video communications using HEVC for responsive decision support in disaster incidents.

This study proposes a unifying framework for m-Health video communication systems that provides for the joint optimization of video quality, bitrate demands, and encoding time. The framework is video modality and infrastructure independent and facilitates adaptation to the best available encoding mode that satisfies underlying technology and application imposed constraints. The scalability of the proposed algorithm is demonstrated using different HEVC encoding configurations and realistic modelling of 802.11× wireless infrastructure for emergency scenery and response videos. Extensive experimentation shows that a jointly optimal solution in the encoding time, bitrate, and video quality space is feasible.

Prediction of the time period of stroke based on ultrasound image analysis of initially asymptomatic carotid plaques.

Non-invasive ultrasound imaging of carotid plaques can provide information on the characteristics of the arterial wall including the size, morphology and texture of the atherosclerotic plaques. Several studies were carried out that demonstrated the usefulness of these feature sets for differentiating between asymptomatic and symptomatic plaques and their corresponding cerebrovascular risk stratification. The aim of this study was to develop predictive modelling for estimating the time period of a stroke event by determining the risk for short term (less or equal to three years) or long term (more than three years) events. Data from 108 patients that had a stroke event have been used. The information collected included clinical and ultrasound imaging data. The prediction was performed at base line where patients were still asymptomatic. Several image texture analysis and clinical features were used in order to create a classification model. The different features were statistically analyzed and we conclude that image texture analysis features extracted using Spatial Gray Level Dependencies method had the best statistical significance. Several predictive models were derived based on Binary Logistic Regression (BLR) and Support Vector Machines (SVM) modelling. The best results were obtained with the SVM modelling models with an average correct classifications score of 77±7% for differentiating between stroke event occurrences within 3 years versus more than 3 years. Further work is needed in investigating additional multiscale texture analysis features as well as more modelling techniques on more subjects.

Computer-aided diagnosis in hysteroscopic imaging.

The paper presents the development of a computer-aided diagnostic (CAD) system for the early detection of endometrial cancer. The proposed CAD system supports reproducibility through texture feature standardization, standardized multifeature selection, and provides physicians with comparative distributions of the extracted texture features. The CAD system was validated using 516 regions of interest (ROIs) extracted from 52 subjects. The ROIs were equally distributed among normal and abnormal cases. To support reproducibility, the RGB images were first gamma corrected and then converted into HSV and YCrCb. From each channel of the gamma-corrected YCrCb, HSV, and RGB color systems, we extracted the following texture features: 1) statistical features (SFs), 2) spatial gray-level dependence matrices (SGLDM), and 3) gray-level difference statistics (GLDS). The texture features were then used as inputs with support vector machines (SVMs) and the probabilistic neural network (PNN) classifiers. After accounting for multiple comparisons, texture features extracted from abnormal ROIs were found to be significantly different than texture features extracted from normal ROIs. Compared to texture features extracted from normal ROIs, abnormal ROIs were characterized by lower image intensity, while variance, entropy, and contrast gave higher values. In terms of ROI classification, the best results were achieved by using SF and GLDS features with an SVM classifier. For this combination, the proposed CAD system achieved an 81% correct classification rate.

Association of ultrasonic texture and echodensity features between sides in patients with bilateral carotid atherosclerosis.

OBJECTIVES: Our objective was to estimate the correlation of echodensity and textural features, using ultrasound and digital image analysis, between plaques in patients with bilateral carotid stenosis. DESIGN: Cross-sectional observational study. METHODS: Patients undergoing carotid endarterectomy were recruited from Vascular Surgery at the Royal Victoria and Jewish General hospitals in Montreal, Canada. Bilateral pre-operative carotid ultrasound and digital image analysis was performed to extract echodensity and textural features using a commercially available Plaque Texture Analysis software (LifeQMedical Ltd). Principal component analysis (PCA) was performed. Partial correlation coefficients for PCA and individual imaging variables between surgical and contralateral plaques were calculated with adjustment for age, sex, contralateral stenosis, and statin use. RESULTS: In the whole group (n = 104), the six identified PCA variables and 42/50 individual imaging variables were moderately correlated (r = .211-.641). Correlations between sides were increased in patients with ≥50% contralateral stenosis and symptomatic patients. CONCLUSION: Textural and echodensity features of carotid plaques were similar between two sides in patients with bilateral stenosis, supporting the notion that plaque instability is determined by systemic factors. Patients with unstable features of one plaque should perhaps be monitored more closely or treated more aggressively for their contralateral stenosis, particularly if this is hemodynamically significant.

eEmergency health care information systems.

In this paper we provide an overview of the way that information and communication technologies have been used for emergency healthcare support. The paper provides a literature review of case studies exploring information systems for monitoring signals, images, medical videos, as well as information protocols used during emergency health care support, and describes future trends. We anticipate that eEmergency systems can significantly improve the delivery of healthcare during emergency cases. However, the monitoring and evaluation of these systems and especially their use in daily practice still remains a goal to be achieved.

An overview of recent end-to-end wireless medical video telemedicine systems using 3G.

Advances in video compression, network technologies, and computer technologies have contributed to the rapid growth of mobile health (m-health) systems and services. Wide deployment of such systems and services is expected in the near future, and it's foreseen that they will soon be incorporated in daily clinical practice. This study focuses in describing the basic components of an end-to-end wireless medical video telemedicine system, providing a brief overview of the recent advances in the field, while it also highlights future trends in the design of telemedicine systems that are diagnostically driven.

An overview of recent health care support systems for eEmergency and mHealth applications.

Advances in mobile communications and medical technologies facilitate the development of emerging mobile systems and applications for healthcare. The objective of this paper is to provide an overview and the current status of mobile health care systems (mHealth) and their applications for Emergency healthcare support (eEmergency). Our paper reports on journal papers that use wireless, emergency telemedicine systems that appeared since 2000. The majority of the applications are focused on the transmission of crucial biosignals (mainly ECG) for the support of heart-related healthcare. A limited number of new studies were focused on supporting emergency healthcare for trauma by facilitating both 2D image or video transmission (eg: ultrasound). Alternatively, new studies have focused on integrated systems for specialized emergency scenaria such as stroke. This paper is an extension of work previously published by our group [1].

Ultrasound image texture analysis of the intima and media layers of the common carotid artery and its correlation with age and gender.

The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). It was proposed but not thoroughly investigated that the composition and texture of the media layer (ML) can be used as an indicator for the risk of stroke. In this study, we investigate the application of texture analysis of the ML of the CCA and how texture is affected by age and gender. The study was performed on 100 longitudinal-section ultrasound images acquired from asymptomatic subjects at risk of atherosclerosis. The images were separated into three different age groups, namely below 50, 50-60, and above 60 years old. Furthermore, the images were separated according to gender. A total of 61 different texture features were extracted from the intima layer (IL), the ML, and the intima-media complex (IMC). The ML and the IMC were segmented manually by a neurovascular expert and also automatically by a snakes segmentation system. We have found that male patients tended to have larger media layer thickness (MLT) values as compared to the MLT of female patients of the same age. We have found significant differences among texture features extracted from the IL, ML and IMC from different age groups. Furthermore, for some texture features, we found that they follow trends that correlate with a patient's age. For example, the gray-scale median GSM of the ML falls linearly with increasing MLT and with increasing age. Our findings suggest that ultrasound image texture analysis of the media layer has potential as an assessment biomarker for the risk of stroke.

Color multiscale texture classification of hysteroscopy images of the endometrium.

The objective of this study was to investigate the diagnostic performance of a Computer Aided Diagnostic (CAD) system based on color multiscale texture analysis for the classification of hysteroscopy images of the endometrium, in support of the early detection of gynaecological cancer. A total of 416 Regions of Interest (ROIs) of the endometrium were extracted (208 normal and 208 abnormal) from 45 subjects. RGB images were gamma corrected and were converted to the YCrCb color system. The following texture features were extracted from the Y, Cr and Cb channels: (i) Statistical Features (SF), (ii) Spatial Gray Level Dependence Matrices (SGLDM), and (iii) Gray Level Difference Statistics (GLDS). The Probabilistic Neural Network (PNN), statistical learning and the Support Vector Machine (SVM) neural network classifiers were also applied for the investigation of classifying normal and abnormal ROIs in different scales. Results showed that the highest percentage of correct classification (%CC) score was 79% and was achieved for the SVM models trained with the SF and GLDS features for the 1x1 scale. This %CC was higher by only 2% when compared with the CAD system developed, based on the SF and GLDS feature sets computed from the Y channel only. Further increase in scale from 2x2 to 9x9, dropped the %CC in the region of 60% for the SF, SGLDM, and GLDS, feature sets, and their combinations. Concluding, a CAD system based on texture analysis and SVM models can be used to classify normal and abnormal endometrium tissue in difficult cases of gynaecological cancer. The proposed system has to be investigated with more cases before it is applied in clinical practise.

Normalisation of ultrasonic images of atherosclerotic plaques and reproducibility of grey scale median using dedicated software.

AIM: Image normalization using ''Adobe Photoshop'' a commercially available software package designed for photography has provided the means of making reproducible measurements of grey scale in ultrasonic images of carotid bifurcation plaques. A dedicated software package ''Plaque Texture Analysis'' has been developed by the authors that is simple to use, provides facilities for measurement of other texture features in addition to grey scale median (GSM) and allows the operator to save the normalised images, plaques and GSM measurements in a database for subsequent analysis. The aim of the study was to determine (a) the intraobserver variability of GSM when the same plaque images were analysed using both Adobe Photoshop and the dedicated software and (b) the interobserver variability when the same plaque images were analysed using the dedicated software by different observers. METHODS: A sample of 33 images of carotid bifurcation plaques (16 symptomatic and 17 asymptomatic) producing greater than 50% stenosis of the internal carotid artery were analysed by two observers. RESULTS: The intraclass correlation coefficient and correlation coefficient (r) for GSM were 0.992 (95% CI 0.984 to 0.996) and 0.992 respectively for the first observer (MG); they were 0.986 (95% CI 0.972 to 0.993) and 0.987 respectively for the second observer (AN). The interobserver intraclass correlation coefficient and correlation coefficient (r) were 0.932 (95% CI 0.863 to 0.967) and 0.933 respectively. CONCLUSION: These findings and the automatic saving of the normalised images and measurements in a database for subsequent statistical analysis make the ''Plaque Texture Analysis'' software a powerful research tool.

Color based texture--classification of hysteroscopy images of the endometrium.

The objective of this study was to develop a CAD system for the classification of hysteroscopy images of the endometrium based on color texture analysis for the early detection of gynaecological cancer. A total of 416 Regions of Interest (ROIs) of the endometrium were extracted (208 normal and 208 abnormal) from 40 subjects. RGB images were gamma corrected and were converted to the HSV and YCrCb color systems. The following texture features were extracted for each channel of the RGB, HSV, and YCrCb systems: (i) Statistical Features, (ii) Spatial Gray Level Dependence Matrices and (iii) Gray Level Difference Statistics. The PNN statistical learning and SVM neural network classifiers were also investigated for classifying normal and abnormal ROIs. Results show that there is significant difference (using the Wilcoxon Rank Sum Test at a=0.05) between the texture features of normal and abnormal ROIs of the endometrium. Abnormal ROIs had higher gray scale median, variance, entropy and contrast and lower gray scale median and homogeneity values when compared to the normal ROIs. The highest percentage of correct classifications score was 79% and was achieved for the SVM models trained with the SF and GLDS features for differentiating between normal and abnormal ROIs. Concluding, a CAD system based on texture analysis and SVM models can be used to classify normal and abnormal endometrium tissue. Further work is needed to validate the system in more cases and organs.

An m-Health monitoring system for children with suspected arrhythmias.

Advances in wireless communications and networking technologies as well as computer and medical technologies, enable the development of small size, power efficient and more reliable medical multi-parameter recording systems, which can be used for continuous monitoring of patients. Through this paper we present the basic architecture and initial development steps of an m-Health monitoring system that will be used in order to monitor children with suspected cardiac arrhythmias. The proposed system will be based on sensor networks, in order to monitor a subject while being in a predefined area like his/her house; while a module based on PDAs and wearable ECG recorders will be used in order to extent the coverage outside the patient's house. The system will be based on a variable sampling rate to conserve power for the possible arrhythmia episode. The system design has been completed, the hardware specifications have been decided and currently the system is going through the development phase.

Selected eHealth applications in Cyprus from the training perspective.

OBJECTIVES: In this paper a review of selected eHealth applications in Cyprus is presented linked with their success or failure based on their training activities. METHODS: The eHealth systems presented and their training activities include an update of the health information system (HIS) in the public hospitals, a medical system for emergency telemedicine (AMBULANCE and EMERGENCY-112 projects), a home monitoring system for cancer patients (DITIS), a satellite-based network in healthcare applications (EMISPHER and HEALTHWARE projects), and the training activities of the Cyprus Society of Medical Informatics. Different methodologies for training were used ranging from classical approaches like train the trainers, using demo cases followed by personal training, group training, and workshops, to more recent methodologies based on eLearning sessions including teleconsultations. RESULTS: The training was carried out successfully in all cases. However, not all eHealth systems were put into practice successfully, mainly for reasons not related to training. CONCLUSIONS: It is anticipated that this paper will promote the importance of these applications and their training activities as well as help in the spin off of others thus enabling the offering of a better service to the citizen.

Texture analysis of ultrasonic images of symptomatic carotid plaques can identify those plaques associated with ipsilateral embolic brain infarction.

OBJECTIVES: The aim of our study was to determine the association between objective, computerised texture analysis of carotid plaque ultrasonic images and embolic CT-brain infarction in patients presenting with hemispheric neurological symptoms. DESIGN: Cross-sectional study in patients with 50%-99% (ECST) carotid stenosis. PATIENTS AND METHODS: Carotid plaque ultrasonic images (n=54, 26 with TIAs and 28 with stroke) obtained during carotid ultrasound were normalised and standardised for resolution and subsequently assessed visually for the presence of discrete echogenic or juxtaluminal echolucent components and overall echogenicity (plaque type). Using computer software, 51 histogram/textural features of the plaque outlines were calculated. Factor analysis was subsequently applied to eliminate redundant variables. Small cortical, large cortical and discrete subcortical infarcts on CT-brain scan were considered as being embolic. RESULTS: Twenty-five cases (46%) had embolic infarcts. On logistic regression, grey-scale median (GSM), a measure of echolucency, spatial grey level dependence matrices (SGLDM) correlation and SGLDM information measure of correlation-1, measures of homogeneity were significant (p<0.05), but not grey level runlength statistics (RUNL) Run Percentage (RP), stenosis severity, type of symptoms or echolucent juxtaluminal components. Using ROC curves methodology, SGLDM information measure of correlation-1 improved the value of GSM in distinguishing embolic from non-embolic CT-brain infarction. CONCLUSION: Computerised texture analysis of ultrasonic images of symptomatic carotid plaques can identify those that are associated with brain infarction, improving the results achieved by GSM alone. This methodology could be applied to prospective natural history studies of symptomatic patients not operated on or randomised trials of patients undergoing carotid angioplasty and stenting in order to identify high-risk subgroups for cerebral infarction.

Texture-based classification of hysteroscopy images of the endometrium.

The objective of this study was to classify hysteroscopy images of the endometrium based on texture analysis for the early detection of gynaecological cancer. A total of 418 regions of interest (ROIs) were extracted (209 normal and 209 abnormal) from 40 subjects. Images were gamma corrected and were converted to gray scale. The following texture features were extracted: (i) statistical features, (ii) spatial gray level dependence matrices (SGLDM), and (iii) gray level difference statistics (GLDS). The PNN and SVM neural network classifiers were also investigated for classifying normal and abnormal ROIs. Results show that there is significant difference (using Wilcoxon rank sum test at a=0.05) between the texture features of normal and abnormal ROIs for both the gamma corrected and uncorrected images. Abnormal ROIs had lower gray scale median and homogeneity values, and higher entropy and contrast values when compared to the normal ROIs. The highest percentage of correct classifications score was 77% and was achieved for the SVM models trained with the SF and GLDS features. Concluding, texture features provide useful information differentiating between normal and abnormal ROIs of the endometrium.

A completely decentralized workflow management system for the support of emergency telemedicine and patient monitoring.

In this work we propose a completely decentralized workflow system, based on peer to peer (P2P) computing, in order to support the procedures followed: a) when handling a patient in a hospital emergency health care department: b) telemedicine services needed during emergency cases or during remote patient examination and monitoring. The proposed architecture is an addition to an already existing telemedicine system that enables telediagnosis and long distance support during emergency and monitoring cases. The existing system is based on a client server architecture, which has dependability limitations as it depends on a single centralized machine. Dependability is a critical requirement for such cases; so the new proposed system addresses these limitations by facilitating operation and even when some peers are unavailable for any reason. Concluding, the system design has been completed successfully.

An integrated system for the support of an emergency health care department.

In this paper, we present the design and development of an integrated database system for the support of an emergency health care department. The system allows patient administration, patient record management, information exchange within the department and Ambulance vehicles management. It was designed as an addition to a previous developed system which supports emergency telemedicine. The system has passed the initial testing and verification phase and is now entering the final evaluation phase before moving into the department's daily routine.

Multiscale morphological analysis of the atherosclerotic carotid plaque.

The aim of this paper was to investigate the usefulness of multiscale morphological analysis in the assessment of atherosclerotic carotid plagues. Ultrasound images were recorded from 137 asymptomatic and 137 symptomatic plaques and were converted to binary images at low, middle and high intensity intervals based on structural morphology. Low images represent low intensity regions corresponding to blood, thrombus, lipid or hemorrhage, whereas high images describe the collagen and calcified components of the plaque. Middle image describe image regions that fall between low and high components. The morphological pattern spectra were computed and several classifiers like the K-Nearest Neighbor (KNN), the Probabilistic Neural Network (PNN), and the Support Vector Machine (SVM) were evaluated for classifying these spectra into two classes: asymptomatic or symptomatic. The highest diagnostic yield achieved was 67% that is slightly lower than texture analysis carried out on the same data set.

The effect of color correction of endoscopy images for quantitative analysis in endometrium.

The objective of this study was to develop a standardized protocol for the capturing and analysis of endoscopy digital images for subsequent use in a Computer Aided Diagnosis (CAD) system in gynaecological cancer. Images were captured at optimum illumination and focus at 720x576 pixels using 24 bits color in the following cases: (i) for a variety of testing targets from a color palette with known color distribution, (ii) different viewing angles and distances from calf endometrium, and (iii) images from the human endometrium. Images were then gamma corrected and their classification performance was compared against that of nonqamma corrected images. No significant difference in texture features was found between the close up and panoramic views, and between angles, either before or after gamma correction. There was significant difference in certain texture features between normal and abnormal endometrium, both before and after gamma correction. Our findings suggest that proper color correction can significantly impact CAD system performance, and we recommend its application prior to quantitative texture analysis in gynaecological endoscopy.

Texture analysis of the endometrium during hysteroscopy: preliminary results.

The objective of this study is to investigate the usefulness of texture analysis in the endometrium during hysteroscopy in endoscopic imaging of the uterine cavity. Endoscopy images from the endometrium from three subjects, at optimum illumination and focus, were frozen and digitized at 720x576 pixels using 24 bits color. Regions of interest (ROI) of normal (N=61) and abnormal (N=69) regions were manually selected by the physician. ROI images were converted into gray scale and statistical features (SF) and spatial gray level dependence matrix features (SGLDM) were computed. The nonparametric Wilcoxon rank sum test at a=0.05 was carried out for comparing the differences between normal and abnormal tissue. There was significant difference between normal and abnormal endometrium for the SF features variance, energy and entropy and for the SGLDM feature of angular second moment. There was no significant difference for the SF features mean, median, and SGLDM features of contrast, correlation and homogeneity.