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Texture-based classification of hysteroscopy images of the endometrium

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dc.contributor.author Neofytou, MS en
dc.contributor.author Pattichis, MS en
dc.contributor.author Pattichis, CS en
dc.contributor.author Tanos, V en
dc.contributor.author Kyriacou, EC en
dc.contributor.author Koutsouris, DD en
dc.date.accessioned 2014-03-01T02:44:12Z
dc.date.available 2014-03-01T02:44:12Z
dc.date.issued 2006 en
dc.identifier.issn 05891019 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/31753
dc.subject Early Detection en
dc.subject Neural Network Classifier en
dc.subject Texture Analysis en
dc.subject Texture Features en
dc.subject Gray Scale Median en
dc.subject Region of Interest en
dc.subject.other Endometrium en
dc.subject.other Hysteroscopy images en
dc.subject.other Texture analysis en
dc.subject.other Texture based classification en
dc.subject.other Feature extraction en
dc.subject.other Gynecology en
dc.subject.other Neural networks en
dc.subject.other Oncology en
dc.subject.other Statistical methods en
dc.subject.other Support vector machines en
dc.subject.other Image analysis en
dc.title Texture-based classification of hysteroscopy images of the endometrium en
heal.type conferenceItem en
heal.identifier.primary 10.1109/IEMBS.2006.259811 en
heal.identifier.secondary http://dx.doi.org/10.1109/IEMBS.2006.259811 en
heal.identifier.secondary 4029511 en
heal.publicationDate 2006 en
heal.abstract 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. en
heal.journalName Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings en
dc.identifier.doi 10.1109/IEMBS.2006.259811 en
dc.identifier.spage 3005 en
dc.identifier.epage 3008 en


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