Texture-based classification of hysteroscopy images of the endometrium

Neofytou, MS; Pattichis, MS; Pattichis, CS; Tanos, V; Kyriacou, EC; Koutsouris, DD

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