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The paper presents the development of a computeraided 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. 2168-2194 © 2014 IEEE. 19 3 1129 1136 Cited By :2

There is a huge need for open source software solutions in the healthcare domain, given the flexibility, interoperability and resource savings characteristics they offer. In this context, this paper presents the development of three open source libraries - Specific Enablers (SEs) for eHealth applications that were developed under the European project titled 'Future Internet Social and Technological Alignment Research' (FI-STAR) funded under the 'Future Internet Public Private Partnership' (FI-PPP) program. The three SEs developed under the Electronic Health Record Application Support Service Enablers (EHR-EN) correspond to: a) an Electronic Health Record enabler (EHR SE), b) a patient summary enabler based on the EU project 'European patient Summary Open Source services' (epSOS SE) supporting patient mobility and the offering of interoperable services, and c) a Picture Archiving and Communications System (PACS) enabler (PACS SE) based on the dcm4che open source system for the support of medical imaging functionality. The EHR SE follows the HL7 Clinical Document Architecture (CDA) V2.0 and supports the Integrating the Healthcare Enterprise (IHE) profiles (recently awarded in Connectathon 2015). These three FI-STAR platform enablers are designed to facilitate the deployment of innovative applications and value added services in the health care sector. They can be downloaded from the FI-STAR cataloque website. Work in progress focuses in the validation and evaluation scenarios for the proving and demonstration of the usability, applicability and adaptability of the proposed enablers. © 2015 IEEE. 2015-November 1401 1404 Sponsors: Conference code: 116805 Cited By :1

Quantitative color tissue analysis in endoscopy examinations requires color standardization procedures to be applied, so as to enable compatibility among computer aided diagnosis application from different endoscopy labs. The objective of this study was to examine the usefulness of different color correction algorithms (thus facilitating color standardization), evaluated on four different endoscopy cameras. The following five color correction algorithms were investigated: two gamma correction based algorithms (the classical and a modified one), and three (2nd, 3rd, and 4th order) polynomial based correction algorithms. The above algorithms were applied to four different endoscopy cameras: (a) Circon, (b) Karl-Stortz, (c) Olympus, and (d) Snowden-Pencer. The color correction algorithms and the endoscopic cameras evaluation, was carried out using the testing color palette (24 colors of known digital values) provided by the Edmund Industrial Optics Company. In summary, we have that: (a) the modified gamma correction algorithm gave significantly smaller mean square error compared to the other four algorithms, and (b) the smallest mean square error was obtained for the Circon camera. Future work will focus on evaluating the proposed color correction algorithm in different endoscopy clinics and compare their tissue characterization results. © 2013 IEEE. Sponsors: Institute of Electrical and Electronic Engineers (IEEE) Artificial Intelligence Foundation (BAIF) Conference code: 102484

This piece of research describes an innovative e-health service that supports the cognitive and physical training of senior citizens and promotes their active ageing. The approach is adopted by the Long Lasting Memories (LLM) project, elements of which are discussed herein in the light of the functionalities provided to the users and the therapists. The aim of this work is to describe those technical elements that demonstrate the unique and integrative character of the LLM service, which is based on a modular Web service architecture, rendering the system available in different settings like the homes of seniors. The underlying database as well as the remote user interface empower therapists to set personalized training schemes, to view the progress of training sessions, as well as, adding new games and exercises into the system, thereby increasing the services sustainability and marketability. © 2011 IEEE. 2505 2509 Conference code: 87843 Cited By :20

In this paper, a simplified 2D navigation system for hysteroscopy imaging was introduced. This system was evaluated in virtual endometrium models, and the position sensor measurement errors were very small and acceptable by the physician. The 2D navigation system, combined with quantitative image analysis will help the physician in gaining a better understanding of the pathophysiology of the tissue under investigation. Key words: Endometrial Cancer, Navigation, Hysteroscopy Imaging. © 2011 ACM. 578 347 352 Sponsors: Technical University of Sofia Inst. Inf. Commun. Technol. - BAS (IOIACTBB) Technical University of Varna (TECHUVB) Fed. Sci. Eng. Unions - Bulgaria (FOSEUB) Lifelong Learning Programme - ETN TRICE Conference code: 86480 Cited By :1

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 lxl 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 2×2 to 9×9, 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. © 2008 IEEE. 1226 1229 Conference code: 75336 Cited By :1

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: (1) 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. © 2007 IEEE. 864 867 Sponsors: ACIES, Research Promotion and Management Consulting EOARD, European Office of Aerospace R and D Grand Lyon NSF, National Science Foundation Philips Research Europe Philips Research North America Conference code: 70818 Cited By :6

Background: In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i) the distance from the tissue (panoramic vs close up), (ii) difference in viewing angles and (iii) color correction. Methods: We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 × 576 pixels and 24 bits color for: (i) a variety of testing targets from a color palette with a known color distribution, (ii) different viewing angles, (iv) two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i) Statistical Features (SF), (ii) Spatial Gray Level Dependence Matrices (SGLDM), and (iii) Gray Level Difference Statistics (GLDS). All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. Results: For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better approximations to the original images. Within the proposed protocol, for human ROIs, we have found that there is a large number of texture features that showed significant differences between normal and abnormal endometrium. Conclusion: This study provides a standardized protocol for avoiding any significant texture feature differences that may arise due to variability in the acquisition procedure or the lack of color correction. After applying the protocol, we have found that significant differences in texture features will only be due to the fact that the features were extracted from different types of tissue (normal vs abnormal). © 2007 Neofytou et al licensee BioMed Central Ltd. 6 Tradenames: IP4.1 RGB video camera, Circon Manufacturers: Circon Cited By :10

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. © 2006 IEEE. 3005 3008 Conference code: 69200 Cited By :6