dc.contributor.author | Neofytou, Marios S. | en |
dc.contributor.author | Tanos, Vasilios | en |
dc.contributor.author | Constantinou, Ioannis P. | en |
dc.contributor.author | Kyriacou, Efthyvoulos C. | en |
dc.contributor.author | Pattichis, Marios S. | en |
dc.contributor.author | Pattichis, Constantinos S. | en |
dc.creator | Neofytou, Marios S. | en |
dc.creator | Tanos, Vasilios | en |
dc.creator | Constantinou, Ioannis P. | en |
dc.creator | Kyriacou, Efthyvoulos C. | en |
dc.creator | Pattichis, Marios S. | en |
dc.creator | Pattichis, Constantinos S. | en |
dc.date.accessioned | 2019-11-13T10:41:27Z | |
dc.date.available | 2019-11-13T10:41:27Z | |
dc.date.issued | 2015 | |
dc.identifier.issn | 2168-2194 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54604 | |
dc.description.abstract | 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. | en |
dc.source | IEEE Journal of Biomedical and Health Informatics | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929305358&doi=10.1109%2fJBHI.2014.2332760&partnerID=40&md5=9be0146f2d5979440000268a41dff8a3 | |
dc.subject | Neural networks | en |
dc.subject | human | en |
dc.subject | Humans | en |
dc.subject | female | en |
dc.subject | Endoscopy | en |
dc.subject | procedures | en |
dc.subject | pathology | en |
dc.subject | middle aged | en |
dc.subject | Endometrial cancer | en |
dc.subject | Endometrial Neoplasms | en |
dc.subject | Classification | en |
dc.subject | Classification (of information) | en |
dc.subject | Diseases | en |
dc.subject | Support vector machines | en |
dc.subject | uterus | en |
dc.subject | receiver operating characteristic | en |
dc.subject | Textures | en |
dc.subject | Gray level differences | en |
dc.subject | Texture features | en |
dc.subject | Computer aided diagnosis | en |
dc.subject | computer assisted diagnosis | en |
dc.subject | Image Interpretation, Computer-Assisted | en |
dc.subject | Hysteroscopy | en |
dc.subject | ROC Curve | en |
dc.subject | Classification rates | en |
dc.subject | Probabilistic neural networks | en |
dc.subject | Computer aided diagnostics | en |
dc.subject | computer interface | en |
dc.subject | Computer-aided diagnostic (CAD) | en |
dc.subject | Computer-aided hysteroscopy | en |
dc.subject | Support vector machine (SVMs) | en |
dc.subject | User-Computer Interface | en |
dc.title | Computer-aided diagnosis in hysteroscopic imaging | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1109/JBHI.2014.2332760 | |
dc.description.volume | 19 | |
dc.description.issue | 3 | |
dc.description.startingpage | 1129 | |
dc.description.endingpage | 1136 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Cited By :2</p> | en |
dc.source.abbreviation | IEEE J.Biomedical Health Informat. | en |
dc.contributor.orcid | Pattichis, Constantinos S. [0000-0003-1271-8151] | |
dc.contributor.orcid | Pattichis, Marios S. [0000-0002-1574-1827] | |
dc.contributor.orcid | Kyriacou, Efthyvoulos C. [0000-0002-4589-519X] | |
dc.gnosis.orcid | 0000-0003-1271-8151 | |
dc.gnosis.orcid | 0000-0002-1574-1827 | |
dc.gnosis.orcid | 0000-0002-4589-519X | |